sensational_Analysis-checkpoint-2.ipynb

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      "source": [
        "# Alternus Vera \n",
        "\n",
        "Course code : **CMPE-257** \n",
        "    \n",
        "Group name : **TEAM DRIFTER**\n",
        "    \n",
        "Name: **Paramdeep Saini**  **013770341**\n",
        "\n",
        "**Note**: During the building of this notebook, I referred following GitHub repos:\n",
        "* https://github.com/hemambujamV/AlternusVera\n",
        "* https://github.com/aarsanjani/AlternusVera\n",
        "* https://github.com/shriaithal/AlternusVera\n",
        "\n",
        "-----\n",
        "\n",
        "GitHub URL: \n",
        "\n",
        "\n",
        "### Liar Liar Pants on Fire Dataset Description \n",
        "- It has 3 files test, training and valid.\n",
        "- Each file has 14 columns\n",
        "    \n",
        "    Column 1: the ID of the statement ([ID].json).\n",
        "    \n",
        "    Column 2: the label.\n",
        "    \n",
        "    Column 3: the statement.\n",
        "    \n",
        "    Column 4: the subject(s).\n",
        "    \n",
        "    Column 5: the speaker.\n",
        "    \n",
        "    Column 6: the speaker's job title.\n",
        "    \n",
        "    Column 7: the state info.\n",
        "    \n",
        "    Column 8: the party affiliation.\n",
        "    \n",
        "    Column 9-13: the total credit history count, including the current statement.\n",
        "    \n",
        "    Column 14: the context (venue / location of the speech or statement).\n",
        "    \n",
        "###  Process of My Approach\n",
        "* Load the Data\n",
        "* Distillation Process  \n",
        "  * Data Cleaning and Text Preprocessing\n",
        "  * Visualization\n",
        "* Feature 1 : Sentiment Analysis\n",
        "* Feature 2 : LDA Topic Modelling\n",
        "* Feature 3 : Sensationalism\n",
        "* Feature 4 : Political Affiliation\n",
        "* Feature 5 : Clickbait\n",
        "* Feature 6 : Spam\n",
        "* Feature 7 : Author Credibility\n",
        "* Feature 8 : Source Reputation\n",
        "* Feature 9 : Content Length\n",
        "* Feature 10 : Word Frequency\n",
        "* Vector Classification Modeling\n",
        "* Ranking and Importance\n",
        "* Merge all features and individual contributions\n",
        "* Form Polynomial Equation\n",
        "\n",
        "#### Feature Selection\n",
        "List top Features Selected based on research articles\n",
        "* Sensationalism\n",
        "* Political Affiliation \n",
        "* Biased\n",
        "* Authenticity\n",
        "\n",
        "#### Team Contribution\n",
        "\n",
        "|Features  |  Member |\n",
        "|-----|-----|\n",
        "| Sensationalism                         |  Paramdeep Saini |  \n",
        "| PolticalAffiliation                 |  Julian | \n",
        "| Biased                   |  Viswa  |   \n",
        "| Authenticity                             |  RajaLakshmi |\n",
        "\n",
        "#### Enrichment\n",
        "* Kaggle Fake news Data 2016\n",
        "\n",
        "##### Libraries\n",
        "* NLTK\n",
        "* Gensim\n",
        "* Numpy\n",
        "* Pandas\n",
        "* CSV\n",
        "* WordCloud\n",
        "* Seaborn\n",
        "* Scipy\n",
        "* Regualr Expression\n",
        "* Matplotlib\n",
        "* Sklearn\n",
        "\n",
        "#### What did I try and What worked?\n",
        "* I preprocessed the given dataset using NLTK in-build libraries for tokenization, stopwords removal, stemming and lemmentization. \n",
        "* Visualized the cleaned data using WordCloud. I decided to extract compound features like Sentiment.\n",
        "* Sensationalism and LDA Topic score and utilized it to classify the news document as fake or not.\n",
        "* I got following results on LL dataset.\n",
        "\n",
        "|Algorithim    | Accuracy   | Confusion Matrix | Classification Report                                 |\n",
        "|---------------:|------------:|------------------:|-------------------------------------------------------:|\n",
        "| DescionTree   | 0.53038674 | [[265 288]       |               precision    recall  f1-score   support |\n",
        "|               |            |  [307 407]]      |                                                       |\n",
        "|               |            |                  |            0       0.46      0.48      0.47       553 |\n",
        "|               |            |                  |            1       0.59      0.57      0.58       714 |\n",
        "|               |            |                  |                                                       |\n",
        "|               |            |                  |     accuracy                           0.53      1267 |\n",
        "|               |            |                  |    macro avg       0.52      0.52      0.52      1267 |\n",
        "|               |            |                  | weighted avg       0.53      0.53      0.53      1267 |\n",
        "|               |            |                  |                                                       |\n",
        "| ---           | ---        | ---              | ---                                                   |\n",
        "| Randon Forest | 0.53196527 | [[226 327]       |               precision    recall  f1-score   support |\n",
        "|               |            |  [266 448]]      |                                                       |\n",
        "|               |            |                  |            0       0.46      0.41      0.43       553 |\n",
        "|               |            |                  |            1       0.58      0.63      0.60       714 |\n",
        "|               |            |                  |                                                       |\n",
        "|               |            |                  |     accuracy                           0.53      1267 |\n",
        "|               |            |                  |    macro avg       0.52      0.52      0.52      1267 |\n",
        "|               |            |                  | weighted avg       0.53      0.53      0.53      1267 |\n",
        "|               |            |                  |                                                       |\n",
        "| ---           | ---        | ---              | ---                                                   |\n",
        "| naive Bayes   | 0.56985004 | [[ 23 530]       |               precision    recall  f1-score   support |\n",
        "|               |            |  [ 15 699]]      |                                                       |\n",
        "|               |            |                  |            0       0.61      0.04      0.08       553 |\n",
        "|               |            |                  |            1       0.57      0.98      0.72       714 |\n",
        "|               |            |                  |                                                       |\n",
        "|               |            |                  |     accuracy                           0.57      1267 |\n",
        "|               |            |                  |    macro avg       0.59      0.51      0.40      1267 |\n",
        "|               |            |                  | weighted avg       0.58      0.57      0.44      1267 |\n",
        "|               |            |                  |                                                       |\n",
        "| ---           | ---        | ---              | ---                                                   |\n",
        "| SVC           | 0.56353591 | [[152 401]       |               precision    recall  f1-score   support |\n",
        "|               |            |  [152 562]]      |                                                       |\n",
        "|               |            |                  |            0       0.50      0.27      0.35       553 |\n",
        "|               |            |                  |            1       0.58      0.79      0.67       714 |\n",
        "|               |            |                  |                                                       |\n",
        "|               |            |                  |     accuracy                           0.56      1267 |\n",
        "|               |            |                  |    macro avg       0.54      0.53      0.51      1267 |\n",
        "|               |            |                  | weighted avg       0.55      0.56      0.53      1267 |\n",
        "|               |            |                  |                                                       |\n",
        "| ---           | ---        | ---              | ---                                                   |\n",
        "| xgb_clf       | 0.56906077 | [[142 411]       |               precision    recall  f1-score   support |\n",
        "|               |            |  [135 579]]      |                                                       |\n",
        "|               |            |                  |            0       0.51      0.26      0.34       553 |\n",
        "|               |            |                  |            1       0.58      0.81      0.68       714 |\n",
        "|               |            |                  |                                                       |\n",
        "|               |            |                  |     accuracy                           0.57      1267 |\n",
        "|               |            |                  |    macro avg       0.55      0.53      0.51      1267 |\n",
        "|               |            |                  | weighted avg       0.55      0.57      0.53      1267 |\n",
        "\n",
        "\n",
        " \n",
        "\n",
        "\n",
        "  We found that sensational data had following characteristics:\n",
        " * Higher punctuations like (!,?), \n",
        " * Higher profanity, \n",
        " * Lot of sensational phrases and number of capital letters.\n",
        " * Second dataset used along with doc2vec to get sensationalism classification. \n",
        " * Accuracy level better. \n",
        " * Based on this, we can say if we have sensationalism classificaition on liar dataset then results would be much better.\n",
        " \n",
        "#### What did not work?\n",
        "* Sensationalism is not enough to conclude if a news is fake or not but it does play a role.\n",
        "* Sensationalism is not linearly related to the overall equation.\n",
        "* It is better to understand that there are many factors which can play the role to determine if news is fake or not such as political affliation, creditbility source and biased.\n",
        "* Using only sensationalism doesn't provide any linear relationship between fakenews.\n",
        "\n",
        "#### What alternatives did you try?\n",
        "* I used the data enrichment downloaded from kaggle and integrated with LL dataset.\n",
        "* Applied Doc2vec algorthim \n",
        "* Applied Random forst and Naive Bayes. New accuracy level is 57%\n",
        "* We assigned 35% importance to sensationalism among the 4 factors that we are using to classify data as true or not. Hence sensationalism factor = 0.35*(prediction from NB)\n",
        "* Accuracy has improved as compared to LL after data amlagamation. After new dataset, I got following result where accuracy has increased little bit:\n",
        "\n",
        "| Algorithim    | Accuracy   | Confusion Matrix | Classification Report                                 |\n",
        "|---------------:|------------:|------------------:|-------------------------------------------------------:|\n",
        "| DescionTree   | 0.51144436 | [[263 290]       |               precision    recall  f1-score   support |\n",
        "|               |            |  [329 385]]      |                                                       |\n",
        "|               |            |                  |            0       0.44      0.48      0.46       553 |\n",
        "|               |            |                  |            1       0.57      0.54      0.55       714 |\n",
        "|               |            |                  |                                                       |\n",
        "|               |            |                  |     accuracy                           0.51      1267 |\n",
        "|               |            |                  |    macro avg       0.51      0.51      0.51      1267 |\n",
        "|               |            |                  | weighted avg       0.52      0.51      0.51      1267 |\n",
        "|               |            |                  |                                                       |\n",
        "| ---           | ---        | ---              | ---                                                   |\n",
        "| Randon Forest | 0.50986582 | [[229 324]       |               precision    recall  f1-score   support |\n",
        "|               |            |  [297 417]]      |                                                       |\n",
        "|               |            |                  |            0       0.44      0.41      0.42       553 |\n",
        "|               |            |                  |            1       0.56      0.58      0.57       714 |\n",
        "|               |            |                  |                                                       |\n",
        "|               |            |                  |     accuracy                           0.51      1267 |\n",
        "|               |            |                  |    macro avg       0.50      0.50      0.50      1267 |\n",
        "|               |            |                  | weighted avg       0.51      0.51      0.51      1267 |\n",
        "|               |            |                  |                                                       |\n",
        "| ---           | ---        | ---              | ---                                                   |\n",
        "| naive Bayes   | 0.56985004 | [[ 24 529]       |               precision    recall  f1-score   support |\n",
        "|               |            |  [ 16 698]]      |                                                       |\n",
        "|               |            |                  |            0       0.60      0.04      0.08       553 |\n",
        "|               |            |                  |            1       0.57      0.98      0.72       714 |\n",
        "|               |            |                  |                                                       |\n",
        "|               |            |                  |     accuracy                           0.57      1267 |\n",
        "|               |            |                  |    macro avg       0.58      0.51      0.40      1267 |\n",
        "|               |            |                  | weighted avg       0.58      0.57      0.44      1267 |\n",
        "|               |            |                  |                                                       |\n",
        "| ---           | ---        | ---              | ---                                                   |\n",
        "| SVC           | 0.55801105 | [[148 405]       |               precision    recall  f1-score   support |\n",
        "|               |            |  [155 559]]      |                                                       |\n",
        "|               |            |                  |            0       0.49      0.27      0.35       553 |\n",
        "|               |            |                  |            1       0.58      0.78      0.67       714 |\n",
        "|               |            |                  |                                                       |\n",
        "|               |            |                  |     accuracy                           0.56      1267 |\n",
        "|               |            |                  |    macro avg       0.53      0.53      0.51      1267 |\n",
        "|               |            |                  | weighted avg       0.54      0.56      0.53      1267 |\n",
        "|               |            |                  |                                                       |\n",
        "| ---           | ---        | ---              | ---                                                   |\n",
        "| xgb_clf       | 0.57616417 | [[136 417]       |               precision    recall  f1-score   support |\n",
        "|               |            |  [120 594]]      |                                                       |\n",
        "|               |            |                  |            0       0.53      0.25      0.34       553 |\n",
        "|               |            |                  |            1       0.59      0.83      0.69       714 |\n",
        "|               |            |                  |                                                       |\n",
        "|               |            |                  |     accuracy                           0.58      1267 |\n",
        "|               |            |                  |    macro avg       0.56      0.54      0.51      1267 |\n",
        "|               |            |                  | weighted avg       0.56      0.58      0.53      1267 |\n",
        "|               |            |                  |                                                       |\n"
      ]
    },
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      "metadata": {
        "id": "YfaEcQS8LFnP",
        "colab_type": "code",
        "outputId": "6e8aa969-661c-461e-cd8b-a211a920257c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import csv\n",
        "import gensim\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.pipeline import Pipeline\n",
        "from nltk.corpus import stopwords\n",
        "from string import punctuation\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk\n",
        "import re\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('popular')\n",
        "nltk.download('punkt')\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import sparse"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
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            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data] Downloading collection 'popular'\n",
            "[nltk_data]    | \n",
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            "[nltk_data]    |       to-date!\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bg3iNKjaSRiq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "# Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "googleDrive = GoogleDrive(gauth)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QN-uCQOaSZnb",
        "colab_type": "code",
        "outputId": "935f42a5-bbb5-44fe-d7e8-f15008f98422",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_bgkn1pTOT7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_link = \"https://drive.google.com/open?id=1_xQWkwm-ZD0NR824TH_eTyIYzW79uvyZ\"\n",
        "testfluff, test_id = test_link.split('=')\n",
        "train_link = \"https://drive.google.com/open?id=1kqCYbfVoHXE3qjjcZR6L0degFuQ0EJGc\"\n",
        "trainfluff, train_id = train_link.split('=')\n",
        "valid_link = \"https://drive.google.com/open?id=1gq7xgR9pG7G22STzQV8WYgySGcRP2hks\"\n",
        "validfluff, valid_id = valid_link.split('=')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlzDhZM3LFnV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Read the test, training and valid data from files\n",
        "# Header = 0 indicates that the first line of the file contains column names,\n",
        "# As there is no Header, create a column names for each column in the dataset\n",
        "# delimiter = \\t indicates that the fields are seperated by tabs, and \n",
        "\n",
        "\n",
        "colnames = ['jsonid', 'label', 'headline_text', 'subject', 'speaker', 'speakerjobtitle', 'stateinfo','partyaffiliation', 'barelytruecounts', 'falsecounts','halftruecounts','mostlytrueocunts','pantsonfirecounts','context']\n",
        "\n",
        "downloaded=googleDrive.CreateFile({'id':test_id})\n",
        "downloaded.GetContentFile('test.csv')\n",
        "test_news = pd.read_csv(\"test.csv\", sep='\\t', names = colnames, error_bad_lines=False)\n",
        "\n",
        "downloaded=googleDrive.CreateFile({'id':train_id})\n",
        "downloaded.GetContentFile('train.csv')  \n",
        "train_news = pd.read_csv(\"train.csv\", sep='\\t', names = colnames, error_bad_lines=False)\n",
        "                        \n",
        "downloaded = googleDrive.CreateFile({'id':test_id}) \n",
        "downloaded.GetContentFile('valid.csv')  \n",
        "valid_news = pd.read_csv(\"valid.csv\", sep='\\t', names = colnames, error_bad_lines=False)\n",
        "                        \n",
        "#train_news = pd.read_csv(train_filename, sep='\\t', names = colnames, error_bad_lines=False)\n",
        "#test_news = pd.read_csv(test_filename, sep='\\t', names = colnames, error_bad_lines=False)\n",
        "#valid_news = pd.read_csv(valid_filename, sep='\\t', names = colnames, error_bad_lines=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARlRLMWDLFnZ",
        "colab_type": "code",
        "outputId": "2431cb4e-a599-48c8-d6a3-7f3945d1d23a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "#Total number of rows\n",
        "print(\"Total Number of Rows and Columns: in Trained Data:\", train_news.shape)\n",
        "print(train_news.loc[0])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total Number of Rows and Columns: in Trained Data: (10240, 14)\n",
            "jsonid                                                       2635.json\n",
            "label                                                            false\n",
            "headline_text        Says the Annies List political group supports ...\n",
            "subject                                                       abortion\n",
            "speaker                                                   dwayne-bohac\n",
            "speakerjobtitle                                   State representative\n",
            "stateinfo                                                        Texas\n",
            "partyaffiliation                                            republican\n",
            "barelytruecounts                                                     0\n",
            "falsecounts                                                          1\n",
            "halftruecounts                                                       0\n",
            "mostlytrueocunts                                                     0\n",
            "pantsonfirecounts                                                    0\n",
            "context                                                       a mailer\n",
            "Name: 0, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUTLJxVGLFnc",
        "colab_type": "text"
      },
      "source": [
        "### Visualize the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ut15LoeRLFnc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train=train_news[\"subject\"]\n",
        "Y_train=train_news[\"label\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJ9yQilfLFnf",
        "colab_type": "text"
      },
      "source": [
        "#### The below table shows that most occurred topics include following:\n",
        "* health-care\n",
        "* taxes\n",
        "* immigration etc. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2naWa4g-LFng",
        "colab_type": "code",
        "outputId": "f8ec394a-fff3-46d2-9458-37b74cb2e52f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "#cross tabulation\n",
        "tab=pd.crosstab(X_train,Y_train,margins=True)\n",
        "tab.sort_values('All', ascending=False)\n",
        "tab"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>label</th>\n",
              "      <th>barely-true</th>\n",
              "      <th>false</th>\n",
              "      <th>half-true</th>\n",
              "      <th>mostly-true</th>\n",
              "      <th>pants-fire</th>\n",
              "      <th>true</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>subject</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alcohol</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alcohol,animals,children,crime</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alcohol,campaign-finance,ethics,public-service</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alcohol,candidates-biography</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alcohol,candidates-biography,crime</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>welfare,workers</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>women</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>women,workers</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>workers</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>1654</td>\n",
              "      <td>1993</td>\n",
              "      <td>2114</td>\n",
              "      <td>1962</td>\n",
              "      <td>839</td>\n",
              "      <td>1676</td>\n",
              "      <td>10238</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3828 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "label                                           barely-true  false  ...  true    All\n",
              "subject                                                             ...             \n",
              "Alcohol                                                   0      1  ...     2      4\n",
              "Alcohol,animals,children,crime                            0      0  ...     1      1\n",
              "Alcohol,campaign-finance,ethics,public-service            0      0  ...     0      1\n",
              "Alcohol,candidates-biography                              0      0  ...     1      1\n",
              "Alcohol,candidates-biography,crime                        1      0  ...     0      1\n",
              "...                                                     ...    ...  ...   ...    ...\n",
              "welfare,workers                                           1      0  ...     0      1\n",
              "women                                                     2      1  ...     3      8\n",
              "women,workers                                             0      0  ...     0      2\n",
              "workers                                                   1      2  ...     1     12\n",
              "All                                                    1654   1993  ...  1676  10238\n",
              "\n",
              "[3828 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zeg6Ms34LFnj",
        "colab_type": "text"
      },
      "source": [
        "#### The below bar chart shows following:\n",
        "* 25% in this field have False category\n",
        "* 20% are half-true.\n",
        "This indicates that this issue is not extremely polarized to one category."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trF8YLVQLFnk",
        "colab_type": "code",
        "outputId": "543bd28b-0de1-43f5-d491-e6d7825004ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        }
      },
      "source": [
        "tab_normalized=pd.crosstab(X_train,Y_train,normalize='index', margins=True)\n",
        "tab_normalized.loc[tab_normalized.index.get_level_values('subject') == \"health-care\"].plot(kind='bar')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4b53efa9e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A34wVEQk4Bb2ISMAp6EVEAk5BLyIScAp6EZGAU9CLiAScgl5EJOAU9CIiAaeg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\n0IuIBJyCXkQk4BT0IiIB9/8B3Bv7G3HPgJ8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdnXN-C6LFno",
        "colab_type": "text"
      },
      "source": [
        "#### Visualize the true and false allocation based on following to classify the desicion:\n",
        "* False\n",
        "* barely-true\n",
        "* pants-fire as lie and half-true\n",
        "* mostly-true and True as truth in separate column verdict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RGcRR8eRLFno",
        "colab_type": "code",
        "outputId": "b4c6924c-37da-43c6-943a-022f18a897e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        }
      },
      "source": [
        "dfTwoCols= train_news[['label','subject']]\n",
        "dfTwoCols.groupby(\"label\").count().plot(kind='bar')\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4b527b6da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IQ9/MLCP9mobBzKy/2mde39DPW37e0Q39vOHOZ/pmZhlx6JuZZcShb2aWEYe+mVlGBh36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\nLCP/CbEyckaSkdpuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZzXRY-3ZLFns",
        "colab_type": "code",
        "outputId": "427ae29c-e5e5-41bc-c1dd-62580a9ff012",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "dfTwoCols['verdict'] = dfTwoCols['label'].map({'false':0, 'true':1, 'barely-true':0, 'half-true':1,'mostly-true':1,'pants-fire':0})\n",
        "print(dfTwoCols.head(100))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "          label                             subject  verdict\n",
            "0         false                            abortion        0\n",
            "1     half-true  energy,history,job-accomplishments        1\n",
            "2   mostly-true                      foreign-policy        1\n",
            "3         false                         health-care        0\n",
            "4     half-true                        economy,jobs        1\n",
            "..          ...                                 ...      ...\n",
            "95  barely-true                     social-security        0\n",
            "96         true              immigration,population        1\n",
            "97  mostly-true                           education        1\n",
            "98         true                                guns        1\n",
            "99  mostly-true                             housing        1\n",
            "\n",
            "[100 rows x 3 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3Ie-VuTLFnw",
        "colab_type": "text"
      },
      "source": [
        "#### Visualize the true and false allocation\n",
        "* In the following bar graph we can see that there are more true values compared to false\n",
        "* However, there are few exceptions such as candidate-biography as they have more false compared to true. You can these values in the table given below verdict bar graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8F5tReTnLFnx",
        "colab_type": "code",
        "outputId": "41ea74f5-2610-47c5-ec32-7c392e81e7f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "dfTwoCols.groupby(\"verdict\").count().plot(kind='bar')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4b52782d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k9r5AS2G9nW3MzKwMirl7p1rSCWm5F3ABsI628J+auk0HFqXlxWmd1P67iIhUvzzd3TMM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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Z_2ijSALFn2",
        "colab_type": "code",
        "outputId": "1f449f37-646e-4123-e821-5148334075b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "tab1=pd.crosstab(dfTwoCols[\"subject\"],dfTwoCols[\"verdict\"],margins=True)\n",
        "tab1.sort_values('All', ascending=False)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>verdict</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>subject</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>4486</td>\n",
              "      <td>5752</td>\n",
              "      <td>10238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>health-care</th>\n",
              "      <td>198</td>\n",
              "      <td>183</td>\n",
              "      <td>381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>taxes</th>\n",
              "      <td>124</td>\n",
              "      <td>184</td>\n",
              "      <td>308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>immigration</th>\n",
              "      <td>120</td>\n",
              "      <td>133</td>\n",
              "      <td>253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>elections</th>\n",
              "      <td>114</td>\n",
              "      <td>138</td>\n",
              "      <td>252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>crime,criminal-justice,food-safety</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>crime,criminal-justice,government-efficiency,job-accomplishments,state-finances</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>crime,criminal-justice,government-regulation,guns,military,public-health,recreation,market-regulation</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bankruptcy,economy,health-care</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diversity,families,gays-and-lesbians,legal-issues,marriage,religion</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3828 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "verdict                                                0     1    All\n",
              "subject                                                              \n",
              "All                                                 4486  5752  10238\n",
              "health-care                                          198   183    381\n",
              "taxes                                                124   184    308\n",
              "immigration                                          120   133    253\n",
              "elections                                            114   138    252\n",
              "...                                                  ...   ...    ...\n",
              "crime,criminal-justice,food-safety                     0     1      1\n",
              "crime,criminal-justice,government-efficiency,jo...     0     1      1\n",
              "crime,criminal-justice,government-regulation,gu...     1     0      1\n",
              "bankruptcy,economy,health-care                         0     1      1\n",
              "diversity,families,gays-and-lesbians,legal-issu...     1     0      1\n",
              "\n",
              "[3828 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Zu3FFkSLFn5",
        "colab_type": "text"
      },
      "source": [
        "#### NLP processing like tokeninsing, stemming\n",
        "\n",
        "From the above analysis, we can proceed further to analyze the sensational analsysi based on news will have extremely polarised graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyAWNEWBLFn6",
        "colab_type": "code",
        "outputId": "2284e673-21b1-45ef-bde0-890d17a753fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "import gensim\n",
        "from gensim.utils import simple_preprocess\n",
        "from gensim.parsing.preprocessing import STOPWORDS\n",
        "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
        "from nltk.stem.porter import *\n",
        "import numpy as np\n",
        "np.random.seed(2018)\n",
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "# reference https://towardsdatascience.com/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZdB-dDYLFoA",
        "colab_type": "text"
      },
      "source": [
        "### Data cleaning and stemming function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45gTmUBLLFoC",
        "colab_type": "code",
        "outputId": "242a8952-02a6-4e23-d7f8-d299a61709d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "source": [
        "stemmer = SnowballStemmer('english')\n",
        "original_words = ['caresses', 'flies', 'dies', 'mules', 'denied','died', 'agreed', 'owned', \n",
        "           'humbled', 'sized','meeting', 'stating', 'siezing', 'itemization','sensational', \n",
        "           'traditional', 'reference', 'colonizer','plotted']\n",
        "singles = [stemmer.stem(plural) for plural in original_words]\n",
        "pd.DataFrame(data = {'original word': original_words, 'stemmed': singles})"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>original word</th>\n",
              "      <th>stemmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>caresses</td>\n",
              "      <td>caress</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>flies</td>\n",
              "      <td>fli</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>dies</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>mules</td>\n",
              "      <td>mule</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>denied</td>\n",
              "      <td>deni</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>died</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>agreed</td>\n",
              "      <td>agre</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>owned</td>\n",
              "      <td>own</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>humbled</td>\n",
              "      <td>humbl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>sized</td>\n",
              "      <td>size</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>meeting</td>\n",
              "      <td>meet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>stating</td>\n",
              "      <td>state</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>siezing</td>\n",
              "      <td>siez</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>itemization</td>\n",
              "      <td>item</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>sensational</td>\n",
              "      <td>sensat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>traditional</td>\n",
              "      <td>tradit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>reference</td>\n",
              "      <td>refer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>colonizer</td>\n",
              "      <td>colon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>plotted</td>\n",
              "      <td>plot</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   original word stemmed\n",
              "0       caresses  caress\n",
              "1          flies     fli\n",
              "2           dies     die\n",
              "3          mules    mule\n",
              "4         denied    deni\n",
              "5           died     die\n",
              "6         agreed    agre\n",
              "7          owned     own\n",
              "8        humbled   humbl\n",
              "9          sized    size\n",
              "10       meeting    meet\n",
              "11       stating   state\n",
              "12       siezing    siez\n",
              "13   itemization    item\n",
              "14   sensational  sensat\n",
              "15   traditional  tradit\n",
              "16     reference   refer\n",
              "17     colonizer   colon\n",
              "18       plotted    plot"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3zhhIJ7iWrX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Create_pipeline(cv, counter, clf, instance, x_train, y_train, x_test, y_test):\n",
        "    pipe = Pipeline([(cv, counter),(clf, instance)])\n",
        "    pipe.fit(x_train,y_train)\n",
        "    predict = pipe.predict(x_test)\n",
        "    return np.mean(predict == y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZ6ruDI0LFoE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def lemmatizeStemming(text):\n",
        "        return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos='v'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DEVZ-HiDLFoI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preProcess(text):\n",
        "    result = []\n",
        "    sdds= gensim.utils.simple_preprocess(text)\n",
        "    for token in sdds:\n",
        "        if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3:\n",
        "            result.append(lemmatizeStemming(token))\n",
        "    return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqeagdROLFoN",
        "colab_type": "code",
        "outputId": "91c06fd1-6222-42ce-ca56-0bd24a69227e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# testing on a single document\n",
        "doc_sample = train_news[train_news['jsonid'] == '2635.json'][\"headline_text\"][0]\n",
        "\n",
        "print('original document: ')\n",
        "words = []\n",
        "for word in doc_sample.split(' '):\n",
        "    words.append(word)\n",
        "print(words)\n",
        "print('\\n\\n tokenized and lemmatized document: ')\n",
        "print(preProcess(doc_sample))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "original document: \n",
            "['Says', 'the', 'Annies', 'List', 'political', 'group', 'supports', 'third-trimester', 'abortions', 'on', 'demand.']\n",
            "\n",
            "\n",
            " tokenized and lemmatized document: \n",
            "['say', 'anni', 'list', 'polit', 'group', 'support', 'trimest', 'abort', 'demand']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YOeli8mmLFoR",
        "colab_type": "code",
        "outputId": "19d77c61-45f3-44b5-de95-9a6d958ff9b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# do for all documents\n",
        "reviewed_docs = train_news['headline_text'].map(preProcess)\n",
        "reviewed_docs[:10]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    [say, anni, list, polit, group, support, trime...\n",
              "1    [declin, coal, start, start, natur, take, star...\n",
              "2    [hillari, clinton, agre, john, mccain, vote, g...\n",
              "3    [health, care, reform, legisl, like, mandat, f...\n",
              "4                    [econom, turnaround, start, term]\n",
              "5    [chicago, bear, start, quarterback, year, tota...\n",
              "6               [dunnam, live, district, repres, year]\n",
              "7    [person, stage, work, activ, year, pass, russ,...\n",
              "8    [take, million, oregon, lotteri, fund, port, n...\n",
              "9    [say, primari, oppon, glenn, grothman, leibham...\n",
              "Name: headline_text, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJvwqVRoLFoU",
        "colab_type": "code",
        "outputId": "198652fc-2657-42f6-e459-d021d829c83d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# create the dictionary\n",
        "dict = gensim.corpora.Dictionary(reviewed_docs)\n",
        "count = 0\n",
        "for key, value in dict.iteritems():\n",
        "    print(key, value)\n",
        "    count += 1\n",
        "    if count > 10:\n",
        "        break"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 abort\n",
            "1 anni\n",
            "2 demand\n",
            "3 group\n",
            "4 list\n",
            "5 polit\n",
            "6 say\n",
            "7 support\n",
            "8 trimest\n",
            "9 administr\n",
            "10 begin\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2HaK788xLFoX",
        "colab_type": "code",
        "outputId": "3ad8347b-7391-429a-c1fa-b0cb73abd11c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# delete the words which appear many times and limit to 100000 words\n",
        "dict.filter_extremes(no_below=15, no_above=0.5, keep_n=100000)\n",
        "print(dict)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dictionary(1072 unique tokens: ['abort', 'demand', 'group', 'list', 'polit']...)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhqDvJhULFod",
        "colab_type": "code",
        "outputId": "ccccf9de-b1e0-4869-8409-82040b7b542e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "bcorpus = [dict.doc2bow(doc) for doc in reviewed_docs]\n",
        "bcorpus[10][0]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(24, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uekBxiBBLFok",
        "colab_type": "code",
        "outputId": "8ac4553c-b190-49b0-c3f8-d9578e577b0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "# count frequency for each document\n",
        "bdoc4310 = bcorpus[4310]\n",
        "for i in range(len(bdoc4310)):\n",
        "    print(\"Word {} (\\\"{}\\\") appears {} time.\".format(bdoc4310[i][0], \n",
        "                                               dict[bdoc4310[i][0]], \n",
        "bdoc4310[i][1]))\n",
        "print(len(bcorpus))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Word 7 (\"administr\") appears 1 time.\n",
            "Word 85 (\"economi\") appears 1 time.\n",
            "Word 127 (\"scott\") appears 1 time.\n",
            "Word 128 (\"walker\") appears 1 time.\n",
            "Word 182 (\"member\") appears 2 time.\n",
            "Word 254 (\"job\") appears 1 time.\n",
            "Word 277 (\"public\") appears 1 time.\n",
            "Word 323 (\"hear\") appears 1 time.\n",
            "Word 542 (\"charg\") appears 1 time.\n",
            "Word 792 (\"talk\") appears 1 time.\n",
            "Word 1023 (\"field\") appears 1 time.\n",
            "10240\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KH0CFzRLLFoo",
        "colab_type": "text"
      },
      "source": [
        "### Naive Bayes Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iS2ySN9pLFoo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "\n",
        "pipeline = Pipeline([\n",
        "    ('bow',CountVectorizer(analyzer=preProcess)), # converts strings to integer counts\n",
        "    ('tfidf',TfidfTransformer()), # converts integer counts to weighted TF-IDF scores\n",
        "    ('classifier',MultinomialNB()) # train on TF-IDF vectors with Naive Bayes classifier\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XEWIPnumLFor",
        "colab_type": "code",
        "outputId": "8416f568-5797-4d51-e78c-bd97a053f8ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "ctrain=np.array(dfTwoCols[\"verdict\"])\n",
        "print(ctrain)\n",
        "ctrain=ctrain.reshape(-1,1)\n",
        "print(ctrain)\n",
        "\n",
        "pipeline.fit(train_news['headline_text'],ctrain)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 1 ... 1 0 0]\n",
            "[[0]\n",
            " [1]\n",
            " [1]\n",
            " ...\n",
            " [1]\n",
            " [0]\n",
            " [0]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('bow',\n",
              "                 CountVectorizer(analyzer=<function preProcess at 0x7f4b53fc0d08>,\n",
              "                                 binary=False, decode_error='strict',\n",
              "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
              "                                 input='content', lowercase=True, max_df=1.0,\n",
              "                                 max_features=None, min_df=1,\n",
              "                                 ngram_range=(1, 1), preprocessor=None,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
              "                                 tokenizer=None, vocabulary=None)),\n",
              "                ('tfidf',\n",
              "                 TfidfTransformer(norm='l2', smooth_idf=True,\n",
              "                                  sublinear_tf=False, use_idf=True)),\n",
              "                ('classifier',\n",
              "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKEd6OcZLFot",
        "colab_type": "code",
        "outputId": "a39e8986-0317-46f2-b8bc-ad582a44afac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "mtest=test_news[\"headline_text\"]\n",
        "dfTwoCols_test= test_news[['label','subject']]\n",
        "dfTwoCols_test['verdict'] = dfTwoCols_test['label'].map({'false':0,'true': 1,'barely-true':0,'half-true':1,'mostly-true':1,'pants-fire':0})\n",
        "dfTwoCols_test\n",
        "ctest=np.array(dfTwoCols_test[\"verdict\"])\n",
        "print(ctest)\n",
        "ctest=ctest.reshape(-1,1)\n",
        "print(ctest)\n",
        "predictions = pipeline.predict(mtest)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 0 0 ... 0 0 0]\n",
            "[[1]\n",
            " [0]\n",
            " [0]\n",
            " ...\n",
            " [0]\n",
            " [0]\n",
            " [0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWXGytqhLFox",
        "colab_type": "text"
      },
      "source": [
        "## Accuracy\n",
        "\n",
        "Here we can see that accuracy level is 60%"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7KbrUIT5LFox",
        "colab_type": "code",
        "outputId": "04f0f0a1-31e2-41cf-c403-7dcd74a512a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "print(classification_report(ctest,predictions))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.61      0.30      0.40       553\n",
            "           1       0.61      0.85      0.71       714\n",
            "\n",
            "    accuracy                           0.61      1267\n",
            "   macro avg       0.61      0.58      0.56      1267\n",
            "weighted avg       0.61      0.61      0.58      1267\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQ-TikcpLFo0",
        "colab_type": "code",
        "outputId": "c14e563b-7a11-443f-b611-c473fc0848a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "sns.heatmap(confusion_matrix(ctest,predictions),annot=True)\n",
        "# references: https://www.kaggle.com/astandrik/simple-spam-filter-using-naive-bayes"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4b4d8f4b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dd_I7-nNLFqD",
        "colab_type": "text"
      },
      "source": [
        "### Sensational Quantification\n",
        "\n",
        "* Based on Punctuation, Length, Similarity to Sensationalism dictionary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oe8kAnoYLFqE",
        "colab_type": "code",
        "outputId": "70bfa357-b0e3-4c2b-98d4-3dadd794f710",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        }
      },
      "source": [
        "dtrain_sensa= train_news\n",
        "dtrain_sensa['verdict']=dfTwoCols['verdict']\n",
        "dtrain_sensa"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>jsonid</th>\n",
              "      <th>label</th>\n",
              "      <th>headline_text</th>\n",
              "      <th>subject</th>\n",
              "      <th>speaker</th>\n",
              "      <th>speakerjobtitle</th>\n",
              "      <th>stateinfo</th>\n",
              "      <th>partyaffiliation</th>\n",
              "      <th>barelytruecounts</th>\n",
              "      <th>falsecounts</th>\n",
              "      <th>halftruecounts</th>\n",
              "      <th>mostlytrueocunts</th>\n",
              "      <th>pantsonfirecounts</th>\n",
              "      <th>context</th>\n",
              "      <th>verdict</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2635.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Says the Annies List political group supports ...</td>\n",
              "      <td>abortion</td>\n",
              "      <td>dwayne-bohac</td>\n",
              "      <td>State representative</td>\n",
              "      <td>Texas</td>\n",
              "      <td>republican</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>a mailer</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10540.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>When did the decline of coal start? It started...</td>\n",
              "      <td>energy,history,job-accomplishments</td>\n",
              "      <td>scott-surovell</td>\n",
              "      <td>State delegate</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>democrat</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>a floor speech.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>324.json</td>\n",
              "      <td>mostly-true</td>\n",
              "      <td>Hillary Clinton agrees with John McCain \"by vo...</td>\n",
              "      <td>foreign-policy</td>\n",
              "      <td>barack-obama</td>\n",
              "      <td>President</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>democrat</td>\n",
              "      <td>70.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>163.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Denver</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1123.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Health care reform legislation is likely to ma...</td>\n",
              "      <td>health-care</td>\n",
              "      <td>blog-posting</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>none</td>\n",
              "      <td>7.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>a news release</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9028.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>The economic turnaround started at the end of ...</td>\n",
              "      <td>economy,jobs</td>\n",
              "      <td>charlie-crist</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>democrat</td>\n",
              "      <td>15.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>an interview on CNN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10235</th>\n",
              "      <td>5473.json</td>\n",
              "      <td>mostly-true</td>\n",
              "      <td>There are a larger number of shark attacks in ...</td>\n",
              "      <td>animals,elections</td>\n",
              "      <td>aclu-florida</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>none</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>interview on \"The Colbert Report\"</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10236</th>\n",
              "      <td>3408.json</td>\n",
              "      <td>mostly-true</td>\n",
              "      <td>Democrats have now become the party of the [At...</td>\n",
              "      <td>elections</td>\n",
              "      <td>alan-powell</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Georgia</td>\n",
              "      <td>republican</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>an interview</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10237</th>\n",
              "      <td>3959.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>Says an alternative to Social Security that op...</td>\n",
              "      <td>retirement,social-security</td>\n",
              "      <td>herman-cain</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Georgia</td>\n",
              "      <td>republican</td>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>a Republican presidential debate</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10238</th>\n",
              "      <td>2253.json</td>\n",
              "      <td>false</td>\n",
              "      <td>On lifting the U.S. Cuban embargo and allowing...</td>\n",
              "      <td>florida,foreign-policy</td>\n",
              "      <td>jeff-greene</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>democrat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>a televised debate on Miami's WPLG-10 against ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10239</th>\n",
              "      <td>1155.json</td>\n",
              "      <td>pants-fire</td>\n",
              "      <td>The Department of Veterans Affairs has a manua...</td>\n",
              "      <td>health-care,veterans</td>\n",
              "      <td>michael-steele</td>\n",
              "      <td>chairman of the Republican National Committee</td>\n",
              "      <td>Maryland</td>\n",
              "      <td>republican</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>a Fox News interview</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10240 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           jsonid  ... verdict\n",
              "0       2635.json  ...       0\n",
              "1      10540.json  ...       1\n",
              "2        324.json  ...       1\n",
              "3       1123.json  ...       0\n",
              "4       9028.json  ...       1\n",
              "...           ...  ...     ...\n",
              "10235   5473.json  ...       1\n",
              "10236   3408.json  ...       1\n",
              "10237   3959.json  ...       1\n",
              "10238   2253.json  ...       0\n",
              "10239   1155.json  ...       0\n",
              "\n",
              "[10240 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4LBDWczWLFqN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#profanity list\n",
        "PROFANITY=['anus', 'arse', 'arsehole', 'ass', 'ass-hat', 'ass-jabber', 'ass-pirate', 'assbag', 'assbandit', 'assbanger', 'assbite', 'assclown', 'asscock', 'asscracker', 'asses', 'assface', 'assfuck', 'assfucker', 'assgoblin', 'asshat', 'asshead', 'asshole', 'asshopper', 'assjacker', 'asslick', 'asslicker', 'assmonkey', 'assmunch', 'assmuncher', 'assnigger', 'asspirate', 'assshit', 'assshole', 'asssucker', 'asswad', 'asswipe', 'balls', 'bampot', 'bastard', 'beaner', 'bint', 'bitch', 'bitchass', 'bitches', 'bitchtits', 'bitchy', 'bloody', 'blowjob', 'blowjob', 'bollocks', 'bollox', 'boner', 'brotherfucker', 'bugger', 'bullshit', 'bumblefuck', 'butt plug', 'butt-pirate', 'buttfucka', 'buttfucker', 'camel toe', 'carpetmuncher', 'chinc', 'chink', 'choad', 'chode', 'clit', 'clitface', 'clitfuck', 'clusterfuck', 'cock', 'cockass', 'cockbite', 'cockburger', 'cockface', 'cockfucker', 'cockhead', 'cockjockey', 'cockknoker', 'cockmaster', 'cockmongler', 'cockmongruel', 'cockmonkey', 'cockmuncher', 'cocknose', 'cocknugget', 'cockshit', 'cocksmith', 'cocksmoke', 'cocksmoker', 'cocksniffer', 'cocksucker', 'cockwaffle', 'coochie', 'coochy', 'coon', 'cooter', 'cracker', 'cum', 'cumbubble', 'cumdumpster', 'cumguzzler', 'cumjockey', 'cumslut', 'cumtart', 'cunnie', 'cunnilingus', 'cunt', 'cuntass', 'cuntface', 'cunthole', 'cuntlicker', 'cuntrag', 'cuntslut', 'dago', 'damn', 'deggo', 'dick', 'dickbag', 'dickbeaters', 'dickface', 'dickfuck', 'dickfucker', 'dickhead', 'dickhole', 'dickjuice', 'dickmilk', 'dickmonger', 'dicks', 'dickslap', 'dicksucker', 'dicksucking', 'dickwad', 'dickweasel', 'dickweed', 'dickwod', 'dike', 'dildo', 'dipshit', 'doochbag', 'dookie', 'douche', 'douche-fag', 'douchebag', 'douchewaffle', 'dumass', 'dumb ass', 'dumbass', 'dumbfuck', 'dumbshit', 'dumshit', 'dyke', 'fag', 'fagbag', 'fagfucker', 'faggit', 'faggot', 'faggotcock', 'fagtard', 'fatass', 'fellatio', 'feltch', 'flamer', 'fool', 'frickin', 'friggin', 'f*ck', 'fuck', 'fuckass', 'fuckbag', 'fuckboy', 'fuckbrain', 'fuckbutt', 'fucked', 'fucker', 'fuckersucker', 'fuckface', 'fuckhead', 'fuckhole', 'fuckin', 'fucking', 'fucknut', 'fucknutt', 'fuckoff', 'fucks', 'fuckstick', 'fucktard', 'fucktart', 'fuckup', 'fuckwad', 'fuckwit', 'fuckwitt', 'fudgepacker', 'gay', 'gayass', 'gaybob', 'gaydo', 'gayfuck', 'gayfuckist', 'gaylord', 'gaytard', 'gaywad', 'goddamn', 'goddamnit', 'gooch', 'gook', 'gringo', 'guido', 'handjob', 'hard on', 'heeb', 'helminth', 'hell', 'ho', 'hoe', 'hoebag', 'homo', 'homodumbshit', 'honkey', 'humping', 'idiot', 'imbecile', 'jackass', 'jap', 'jerk off', 'jerk wad', 'jigaboo', 'jizz', 'jungle bunny', 'junglebunny', 'kike', 'kooch', 'kootch', 'kraut', 'kunt', 'kyke', 'lameass', 'lesbian', 'lesbo', 'lezzie', 'mcfagget', 'mick', 'midget', 'minge', 'moron', 'mothafucka', 'mothafuckin', 'motherfucker', 'motherfucking', 'muff', 'muffdiver', 'munging', 'negro', 'nigaboo', 'nigga', 'nigger', 'niggers', 'niglet', 'nutter', 'nut sack', 'nutsack', 'paki', 'panooch', 'pecker', 'peckerhead', 'penis', 'penisbanger', 'penisfucker', 'penispuffer', 'piss', 'pissed', 'pissed off', 'pissflaps', 'polesmoker', 'pollock', 'poon', 'poonani', 'poonany', 'poontang', 'porch monkey', 'porchmonkey', 'prick', 'punanny', 'punta', 'pussies', 'pussy', 'pussylicking', 'puto', 'queef', 'queer', 'queerbait', 'queerhole', 'renob', 'retard', 'rimjob', 'ruski', 'sand nigger', 'sandnigger', 'schlong', 'schmuck', 'scrote', 'scullion', 'shag', 'shit', 'shitass', 'shitbag', 'shitbagger', 'shitbrains', 'shitbreath', 'shitcanned', 'shitcunt', 'shitdick', 'shitface', 'shitfaced', 'shithead', 'shithole', 'shithouse', 'shitspitter', 'shitstain', 'shitter', 'shittiest', 'shitting', 'shitty', 'shiz', 'shiznit', 'skank', 'skeet', 'skullfuck', 'slag', 'slapper', 'slut', 'slutbag', 'slubberdegullion', 'smeg', 'snatch', 'sodding', 'sonofabitch', 'spastic', 'spic', 'spick', 'splooge', 'spook', 'suckass', 'tard', 'testicle', 'thundercunt', 'tit', 'titfuck', 'tits', 'tittyfuck', 'trollop', 'twat', 'twatlips', 'twats', 'twatwaffle', 'unclefucker', 'va-j-j', 'vag', 'vagina', 'vajayjay', 'vjayjay', 'wank', 'wanker', 'wankjob', 'wetback', 'whore', 'whorebag', 'whoreface', 'wop']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHHhDV9lLFqR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sensationalist_phrases = [\n",
        "\n",
        "\t# From Analyst's Desktop Binder of Homeland Security https://www.scribd.com/doc/82701103/Analyst-Desktop-Binder-REDACTED\n",
        "\t\n",
        "\t'Assassination',\n",
        "\t'Attack',\n",
        "\t'Domestic security',\n",
        "\t'Law enforcement',\n",
        "\t'Disaster',\n",
        "\t'National preparedness',\n",
        "\t'Response',\n",
        "\t'Recovery',\n",
        "\t'Emergency response',\n",
        "\t'First responder',\n",
        "\t'Militia',\n",
        "\t'Shooting',\n",
        "\t'Evacuation',\n",
        "\t'Hostage',\n",
        "\t'Explosion',\n",
        "\t'Organized crime',\n",
        "\t'Gangs',\n",
        "\t'National security',\n",
        "\t'State of emergency',\n",
        "\t'Security breach',\n",
        "\t'Threat',\n",
        "\t'Standoff',\n",
        "\t'Lockdown',\n",
        "\t'Bomb',\n",
        "\t'Riot',\n",
        "\t'Emergency Landing',\n",
        "\t'Incident',\n",
        "\t'Suspicious',\n",
        "\t'Nuclear threat',\n",
        "\t'Hazardous',\n",
        "\t'Infection',\n",
        "\t'Outbreak',\n",
        "\t'Contamination',\n",
        "\t'Terror',\n",
        "\t'Epidemic',\n",
        "\t'Critical Infrastructure',\n",
        "\t'National infrastructure',\n",
        "\t'Transportation security',\n",
        "\t'Grid',\n",
        "\t'Outage',\n",
        "\t'Disruption',\n",
        "\t'Violence',\n",
        "\t'Drug cartel',\n",
        "\t'Narcotics',\n",
        "\t'Shootout',\n",
        "\t'Trafficking',\n",
        "\t'Kidnap',\n",
        "\t'Illegal',\n",
        "\t'Smuggling', \n",
        "\t'Al Qaeda',\n",
        "\t'Terror attack',\n",
        "\t'Weapon',\n",
        "\t'Improvised explosive device',\n",
        "\t'Suicide bomber',\n",
        "\t'Suicide attack',\n",
        "\t'Hurricane',\n",
        "\t'Tornado',\n",
        "\t'Tsunami',\n",
        "\t'Earthquake',\n",
        "\t'Tremor',\n",
        "\t'Flood',\n",
        "\t'Storm',\n",
        "\t'Extreme weather',\n",
        "\t'Forest fire',\n",
        "\t'Ice',\n",
        "\t'Stranded',\n",
        "\t'Wildfire',\n",
        "\t'Avalanche',\n",
        "\t'Blizzard',\n",
        "\t'Lightening',\n",
        "\t'Emergency Broadcast System',\n",
        "\t'Cyber Security',\n",
        "\t'DDOS',\n",
        "\t'Denial of service',\n",
        "\t'Malware',\n",
        "\t'Phishing',\n",
        "\t'Cyber attack',\n",
        "\t'Cyber terror'\n",
        "]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9o5epKLOLFqW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sensphrasedetect(str):\n",
        "    sum =0\n",
        "    for x in sensationalist_phrases:\n",
        "        if x.lower() in str.lower():\n",
        "            sum+=1\n",
        "    return sum"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLaq4hhqLFqZ",
        "colab_type": "code",
        "outputId": "2b19d5f8-7ce3-4b23-c551-4b90b910ab99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 983
        }
      },
      "source": [
        "## extract punctuation, capital letters, digits, length\n",
        "import string\n",
        "count = lambda l1,l2: sum([1 for x in l1 if x in l2])\n",
        "#s = 'abcd!!?)().'\n",
        "\n",
        "#print(count(s,set(string.punctuation))                                                                                                      )\n",
        "#print(\"Capital Letters: \", sum(1 for c in \"ahbcdsSJDH\" if c.isupper()))\n",
        "#print(\"Capital Letters: \", sum(1 for c in \"ahbcdsSJDs2H\" if c.isdigit()))\n",
        "pcCount=[]\n",
        "capCount=[]\n",
        "digCount=[]\n",
        "lenCount=[]\n",
        "profanCount=[]\n",
        "sensphrCount=[]\n",
        "for x in dtrain_sensa['headline_text']:\n",
        "    pcCount.append(sum(1 for c in x if c==\"!\" or c==\"?\"))\n",
        "    capCount.append(sum(1 for c in x if c.isupper()))\n",
        "    digCount.append(sum(1 for c in x if c.isdigit()))\n",
        "    lenCount.append(len(x))\n",
        "    sensphrCount.append(sensphrasedetect(x))\n",
        "for x in reviewed_docs:\n",
        "    try:\n",
        "        profanCount.append(float(len([w for w in x if w.lower() in PROFANITY]))/len(x))\n",
        "    except:\n",
        "        profanCount.append(0)\n",
        "dtrain_sensa['puncCount']=pcCount\n",
        "dtrain_sensa['capCount']=capCount\n",
        "dtrain_sensa['digCount']=digCount\n",
        "dtrain_sensa['lenCount']=lenCount\n",
        "dtrain_sensa['profanCount']=profanCount\n",
        "dtrain_sensa['sensPhrCount']=sensphrCount\n",
        "dtrain_sensa"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>jsonid</th>\n",
              "      <th>label</th>\n",
              "      <th>headline_text</th>\n",
              "      <th>subject</th>\n",
              "      <th>speaker</th>\n",
              "      <th>speakerjobtitle</th>\n",
              "      <th>stateinfo</th>\n",
              "      <th>partyaffiliation</th>\n",
              "      <th>barelytruecounts</th>\n",
              "      <th>falsecounts</th>\n",
              "      <th>halftruecounts</th>\n",
              "      <th>mostlytrueocunts</th>\n",
              "      <th>pantsonfirecounts</th>\n",
              "      <th>context</th>\n",
              "      <th>verdict</th>\n",
              "      <th>puncCount</th>\n",
              "      <th>capCount</th>\n",
              "      <th>digCount</th>\n",
              "      <th>lenCount</th>\n",
              "      <th>profanCount</th>\n",
              "      <th>sensPhrCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2635.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Says the Annies List political group supports ...</td>\n",
              "      <td>abortion</td>\n",
              "      <td>dwayne-bohac</td>\n",
              "      <td>State representative</td>\n",
              "      <td>Texas</td>\n",
              "      <td>republican</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>a mailer</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>82</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10540.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>When did the decline of coal start? It started...</td>\n",
              "      <td>energy,history,job-accomplishments</td>\n",
              "      <td>scott-surovell</td>\n",
              "      <td>State delegate</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>democrat</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>a floor speech.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>141</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>324.json</td>\n",
              "      <td>mostly-true</td>\n",
              "      <td>Hillary Clinton agrees with John McCain \"by vo...</td>\n",
              "      <td>foreign-policy</td>\n",
              "      <td>barack-obama</td>\n",
              "      <td>President</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>democrat</td>\n",
              "      <td>70.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>163.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Denver</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>105</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1123.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Health care reform legislation is likely to ma...</td>\n",
              "      <td>health-care</td>\n",
              "      <td>blog-posting</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9028.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>The economic turnaround started at the end of ...</td>\n",
              "      <td>economy,jobs</td>\n",
              "      <td>charlie-crist</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>democrat</td>\n",
              "      <td>15.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>an interview on CNN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>54</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10235</th>\n",
              "      <td>5473.json</td>\n",
              "      <td>mostly-true</td>\n",
              "      <td>There are a larger number of shark attacks in ...</td>\n",
              "      <td>animals,elections</td>\n",
              "      <td>aclu-florida</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>none</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>interview on \"The Colbert Report\"</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10236</th>\n",
              "      <td>3408.json</td>\n",
              "      <td>mostly-true</td>\n",
              "      <td>Democrats have now become the party of the [At...</td>\n",
              "      <td>elections</td>\n",
              "      <td>alan-powell</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Georgia</td>\n",
              "      <td>republican</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>an interview</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10237</th>\n",
              "      <td>3959.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>Says an alternative to Social Security that op...</td>\n",
              "      <td>retirement,social-security</td>\n",
              "      <td>herman-cain</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Georgia</td>\n",
              "      <td>republican</td>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>a Republican presidential debate</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>176</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10238</th>\n",
              "      <td>2253.json</td>\n",
              "      <td>false</td>\n",
              "      <td>On lifting the U.S. Cuban embargo and allowing...</td>\n",
              "      <td>florida,foreign-policy</td>\n",
              "      <td>jeff-greene</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>democrat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>a televised debate on Miami's WPLG-10 against ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>62</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10239</th>\n",
              "      <td>1155.json</td>\n",
              "      <td>pants-fire</td>\n",
              "      <td>The Department of Veterans Affairs has a manua...</td>\n",
              "      <td>health-care,veterans</td>\n",
              "      <td>michael-steele</td>\n",
              "      <td>chairman of the Republican National Committee</td>\n",
              "      <td>Maryland</td>\n",
              "      <td>republican</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>a Fox News interview</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>181</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10240 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           jsonid        label  ... profanCount sensPhrCount\n",
              "0       2635.json        false  ...         0.0            0\n",
              "1      10540.json    half-true  ...         0.0            0\n",
              "2        324.json  mostly-true  ...         0.0            0\n",
              "3       1123.json        false  ...         0.0            0\n",
              "4       9028.json    half-true  ...         0.0            0\n",
              "...           ...          ...  ...         ...          ...\n",
              "10235   5473.json  mostly-true  ...         0.0            1\n",
              "10236   3408.json  mostly-true  ...         0.0            0\n",
              "10237   3959.json    half-true  ...         0.0            0\n",
              "10238   2253.json        false  ...         0.0            0\n",
              "10239   1155.json   pants-fire  ...         0.0            0\n",
              "\n",
              "[10240 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9OuilPzWLFqd",
        "colab_type": "text"
      },
      "source": [
        "#### Observation\n",
        "\n",
        "##### we observed\n",
        "* On an average true stories have lower punctuations like (!,?), lower profanity. lower sensational phrases and lower number of capital letters.\n",
        "* They are more specific with digits and are longer than fake news"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ongL_aoZLFqe",
        "colab_type": "code",
        "outputId": "d12349e1-e19f-4345-87f2-6503f71f938a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "dtrain_sensa_true=dtrain_sensa[dtrain_sensa['verdict']==1]\n",
        "dtrain_sensa_false=dtrain_sensa[dtrain_sensa['verdict']==0]\n",
        "print(\"true: \"+str(dtrain_sensa_true.shape[0]))\n",
        "print(\"avg punc: \"+str(dtrain_sensa_true[\"puncCount\"].mean()))\n",
        "print(\"avg cap: \"+str(dtrain_sensa_true[\"capCount\"].mean()))\n",
        "print(\"avg dig: \"+str(dtrain_sensa_true[\"digCount\"].mean()))\n",
        "print(\"avg len: \"+str(dtrain_sensa_true[\"lenCount\"].mean()))\n",
        "print(\"avg profan: \"+str(dtrain_sensa_true[\"profanCount\"].mean()))\n",
        "print(\"avg sensation phr: \"+str(dtrain_sensa_true[\"sensPhrCount\"].mean()))\n",
        "print(\"false: \"+str(dtrain_sensa_false.shape[0]))\n",
        "print(\"avg punc: \"+str(dtrain_sensa_false[\"puncCount\"].mean()))\n",
        "print(\"avg cap: \"+str(dtrain_sensa_false[\"capCount\"].mean()))\n",
        "print(\"avg dig: \"+str(dtrain_sensa_false[\"digCount\"].mean()))\n",
        "print(\"avg len: \"+str(dtrain_sensa_false[\"lenCount\"].mean()))\n",
        "print(\"avg profan: \"+str(dtrain_sensa_false[\"profanCount\"].mean()))\n",
        "print(\"avg sensation phr: \"+str(dtrain_sensa_false[\"sensPhrCount\"].mean()))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "true: 5752\n",
            "avg punc: 0.011648122392211404\n",
            "avg cap: 3.4838317107093184\n",
            "avg dig: 1.8666550764951322\n",
            "avg len: 109.04363699582754\n",
            "avg profan: 0.0002856388222347888\n",
            "avg sensation phr: 0.12969401947148818\n",
            "false: 4488\n",
            "avg punc: 0.01804812834224599\n",
            "avg cap: 3.821524064171123\n",
            "avg dig: 1.3279857397504455\n",
            "avg len: 104.1954099821747\n",
            "avg profan: 0.000435591653840317\n",
            "avg sensation phr: 0.1468360071301248\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1YF8ltiLFqh",
        "colab_type": "code",
        "outputId": "44b4e884-cc5a-49f8-bcb7-fcd74318ae6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 878
        }
      },
      "source": [
        "dtest_sensa=test_news\n",
        "dtest_sensa['verdict']=dfTwoCols_test['verdict']\n",
        "dtest_sensa"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>4</th>\n",
              "      <td>9524.json</td>\n",
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              "      <td>campaign-finance,legal-issues,campaign-adverti...</td>\n",
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              "      <td>education</td>\n",
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              "      <td>Florida</td>\n",
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              "      <td>democrat</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>on ABC's \"This Week\"</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1264</th>\n",
              "      <td>10710.json</td>\n",
              "      <td>barely-true</td>\n",
              "      <td>In the early 1980s, Sen. Edward Kennedy secret...</td>\n",
              "      <td>bipartisanship,congress,foreign-policy,history</td>\n",
              "      <td>mackubin-thomas-owens</td>\n",
              "      <td>senior fellow, Foreign Policy Research Institute</td>\n",
              "      <td>Rhode Island</td>\n",
              "      <td>columnist</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a commentary in The Providence Journal</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1265</th>\n",
              "      <td>3186.json</td>\n",
              "      <td>barely-true</td>\n",
              "      <td>Says an EPA permit languished under Strickland...</td>\n",
              "      <td>environment,government-efficiency</td>\n",
              "      <td>john-kasich</td>\n",
              "      <td>Governor of Ohio as of Jan. 10, 2011</td>\n",
              "      <td>Ohio</td>\n",
              "      <td>republican</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>a news conference</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1266</th>\n",
              "      <td>6743.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Says the governor is going around the state ta...</td>\n",
              "      <td>state-budget,state-finances,taxes</td>\n",
              "      <td>john-burzichelli</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>democrat</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>an interview with NJToday</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1267 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          jsonid        label  ...                                 context verdict\n",
              "0     11972.json         true  ...                         Radio interview       1\n",
              "1     11685.json        false  ...                       a news conference       0\n",
              "2     11096.json        false  ...            comments on ABC's This Week.       0\n",
              "3      5209.json    half-true  ...                            a radio show       1\n",
              "4      9524.json   pants-fire  ...                             a web video       0\n",
              "...          ...          ...  ...                                     ...     ...\n",
              "1262   7334.json    half-true  ...                       a news conference       1\n",
              "1263   9788.json  barely-true  ...                    on ABC's \"This Week\"       0\n",
              "1264  10710.json  barely-true  ...  a commentary in The Providence Journal       0\n",
              "1265   3186.json  barely-true  ...                       a news conference       0\n",
              "1266   6743.json        false  ...               an interview with NJToday       0\n",
              "\n",
              "[1267 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXH2Sx5YLFqm",
        "colab_type": "code",
        "outputId": "be5b94e9-3e1c-4a23-bf8d-26e34621db19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "processed_docs_test = test_news['headline_text'].map(preProcess)\n",
        "processed_docs_test[:10]"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           [build, wall, mexico, border, liter, year]\n",
              "1       [wisconsin, pace, doubl, number, layoff, year]\n",
              "2                       [say, john, mccain, help, vet]\n",
              "3    [suzann, bonamici, support, plan, choic, medic...\n",
              "4    [ask, report, center, crimin, scheme, violat, ...\n",
              "5    [past, year, feder, govern, pay, million, reti...\n",
              "6    [say, tennesse, requir, school, receiv, half, ...\n",
              "7    [say, vice, presid, biden, admit, american, pe...\n",
              "8                [donald, trump, marriag, equal, want]\n",
              "9    [know, half, hillari, clinton, meet, secretari...\n",
              "Name: headline_text, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_Fj92ATLFqo",
        "colab_type": "code",
        "outputId": "90bf439d-791a-4a90-dfd1-b40adbf2892e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 966
        }
      },
      "source": [
        "## extract punctuation, capital letters, digits, length\n",
        "import string\n",
        "tpcCount=[]\n",
        "tcapCount=[]\n",
        "tdigCount=[]\n",
        "tlenCount=[]\n",
        "tprfanCount=[]\n",
        "tsensphrCount=[]\n",
        "for x in dtest_sensa['headline_text']:\n",
        "    tpcCount.append(sum(1 for c in x if c==\"!\" or c==\"?\"))\n",
        "    tcapCount.append(sum(1 for c in x if c.isupper()))\n",
        "    tdigCount.append(sum(1 for c in x if c.isdigit()))\n",
        "    tlenCount.append(len(x))\n",
        "    tsensphrCount.append(sensphrasedetect(x))\n",
        "for x in processed_docs_test:\n",
        "    try:\n",
        "        tprfanCount.append(float(len([w for w in x if w.lower() in PROFANITY]))/len(x))\n",
        "    except:\n",
        "        tprfanCount.append(0)\n",
        "\n",
        "dtest_sensa['puncCount']=tpcCount\n",
        "dtest_sensa['capCount']=tcapCount\n",
        "dtest_sensa['digCount']=tdigCount\n",
        "dtest_sensa['lenCount']=tlenCount\n",
        "dtest_sensa['profanCount']=tprfanCount\n",
        "dtest_sensa['sensPhrCount']=tsensphrCount\n",
        "dtest_sensa"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>jsonid</th>\n",
              "      <th>label</th>\n",
              "      <th>headline_text</th>\n",
              "      <th>subject</th>\n",
              "      <th>speaker</th>\n",
              "      <th>speakerjobtitle</th>\n",
              "      <th>stateinfo</th>\n",
              "      <th>partyaffiliation</th>\n",
              "      <th>barelytruecounts</th>\n",
              "      <th>falsecounts</th>\n",
              "      <th>halftruecounts</th>\n",
              "      <th>mostlytrueocunts</th>\n",
              "      <th>pantsonfirecounts</th>\n",
              "      <th>context</th>\n",
              "      <th>verdict</th>\n",
              "      <th>puncCount</th>\n",
              "      <th>capCount</th>\n",
              "      <th>digCount</th>\n",
              "      <th>lenCount</th>\n",
              "      <th>profanCount</th>\n",
              "      <th>sensPhrCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11972.json</td>\n",
              "      <td>true</td>\n",
              "      <td>Building a wall on the U.S.-Mexico border will...</td>\n",
              "      <td>immigration</td>\n",
              "      <td>rick-perry</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Texas</td>\n",
              "      <td>republican</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>42</td>\n",
              "      <td>23</td>\n",
              "      <td>18</td>\n",
              "      <td>Radio interview</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>68</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11685.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Wisconsin is on pace to double the number of l...</td>\n",
              "      <td>jobs</td>\n",
              "      <td>katrina-shankland</td>\n",
              "      <td>State representative</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>democrat</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a news conference</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11096.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Says John McCain has done nothing to help the ...</td>\n",
              "      <td>military,veterans,voting-record</td>\n",
              "      <td>donald-trump</td>\n",
              "      <td>President-Elect</td>\n",
              "      <td>New York</td>\n",
              "      <td>republican</td>\n",
              "      <td>63</td>\n",
              "      <td>114</td>\n",
              "      <td>51</td>\n",
              "      <td>37</td>\n",
              "      <td>61</td>\n",
              "      <td>comments on ABC's This Week.</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5209.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>Suzanne Bonamici supports a plan that will cut...</td>\n",
              "      <td>medicare,message-machine-2012,campaign-adverti...</td>\n",
              "      <td>rob-cornilles</td>\n",
              "      <td>consultant</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>republican</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>a radio show</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>85</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9524.json</td>\n",
              "      <td>pants-fire</td>\n",
              "      <td>When asked by a reporter whether hes at the ce...</td>\n",
              "      <td>campaign-finance,legal-issues,campaign-adverti...</td>\n",
              "      <td>state-democratic-party-wisconsin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>democrat</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>a web video</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>127</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1262</th>\n",
              "      <td>7334.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>Says his budget provides the highest state fun...</td>\n",
              "      <td>education</td>\n",
              "      <td>rick-scott</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Florida</td>\n",
              "      <td>republican</td>\n",
              "      <td>28</td>\n",
              "      <td>23</td>\n",
              "      <td>38</td>\n",
              "      <td>34</td>\n",
              "      <td>7</td>\n",
              "      <td>a news conference</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>82</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1263</th>\n",
              "      <td>9788.json</td>\n",
              "      <td>barely-true</td>\n",
              "      <td>Ive been here almost every day.</td>\n",
              "      <td>civil-rights,crime,criminal-justice</td>\n",
              "      <td>jay-nixon</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Missouri</td>\n",
              "      <td>democrat</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>on ABC's \"This Week\"</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1264</th>\n",
              "      <td>10710.json</td>\n",
              "      <td>barely-true</td>\n",
              "      <td>In the early 1980s, Sen. Edward Kennedy secret...</td>\n",
              "      <td>bipartisanship,congress,foreign-policy,history</td>\n",
              "      <td>mackubin-thomas-owens</td>\n",
              "      <td>senior fellow, Foreign Policy Research Institute</td>\n",
              "      <td>Rhode Island</td>\n",
              "      <td>columnist</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a commentary in The Providence Journal</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>147</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1265</th>\n",
              "      <td>3186.json</td>\n",
              "      <td>barely-true</td>\n",
              "      <td>Says an EPA permit languished under Strickland...</td>\n",
              "      <td>environment,government-efficiency</td>\n",
              "      <td>john-kasich</td>\n",
              "      <td>Governor of Ohio as of Jan. 10, 2011</td>\n",
              "      <td>Ohio</td>\n",
              "      <td>republican</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>a news conference</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>96</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1266</th>\n",
              "      <td>6743.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Says the governor is going around the state ta...</td>\n",
              "      <td>state-budget,state-finances,taxes</td>\n",
              "      <td>john-burzichelli</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>democrat</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>an interview with NJToday</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>161</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1267 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          jsonid        label  ... profanCount sensPhrCount\n",
              "0     11972.json         true  ...         0.0            0\n",
              "1     11685.json        false  ...         0.0            0\n",
              "2     11096.json        false  ...         0.0            0\n",
              "3      5209.json    half-true  ...         0.0            1\n",
              "4      9524.json   pants-fire  ...         0.0            0\n",
              "...          ...          ...  ...         ...          ...\n",
              "1262   7334.json    half-true  ...         0.0            0\n",
              "1263   9788.json  barely-true  ...         0.0            0\n",
              "1264  10710.json  barely-true  ...         0.0            0\n",
              "1265   3186.json  barely-true  ...         0.0            0\n",
              "1266   6743.json        false  ...         0.0            0\n",
              "\n",
              "[1267 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNK3GHKvLFqr",
        "colab_type": "code",
        "outputId": "a9dffbe9-a24b-494c-9796-c2ad9605429e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "dtest_sensa.columns[15:21]"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['puncCount', 'capCount', 'digCount', 'lenCount', 'profanCount',\n",
              "       'sensPhrCount'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZYuZ7mYvEIH",
        "colab_type": "code",
        "outputId": "7e2763bc-5999-445d-96f3-c5ddea1a86c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "dtrain_sensa.columns[15:21]"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['puncCount', 'capCount', 'digCount', 'lenCount', 'profanCount',\n",
              "       'sensPhrCount'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjT9_y58LFqu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train=dtrain_sensa[dtrain_sensa.columns[15:21]]\n",
        "y_train=dtrain_sensa['verdict']\n",
        "x_test= dtest_sensa[dtest_sensa.columns[15:21]]\n",
        "y_test = dtest_sensa['verdict']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFwRPP_wpEZL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Create_pipeline1(cv, counter, clf, instance, x_train, y_train, x_test, y_test):\n",
        "    pipe = Pipeline([(clf, instance)])\n",
        "    pipe.fit(x_train.dropna(),y_train.dropna())\n",
        "    predict = pipe.predict(x_test)\n",
        "    cls_report=classification_report(y_test,predict)\n",
        "    cnfmtx=confusion_matrix(y_test,predict)\n",
        "    mse=mean_squared_error(y_test, predict)\n",
        "    mae=mean_absolute_error(y_test, predict)\n",
        "    msse=np.sqrt(mean_squared_error(y_test, predict))\n",
        "    #print(\"values\")\n",
        "    #print(mse,mae,msse)\n",
        "    return np.mean(predict == y_test), cls_report, cnfmtx, mse, mae, msse, pipe"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-Nu1ZfPi338",
        "colab_type": "code",
        "outputId": "619e3e3c-3be0-4ac6-a1a4-0f81530446b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from xgboost import XGBClassifier\n",
        "from prettytable import PrettyTable\n",
        "funcDict = {\"DescionTree\": DecisionTreeClassifier(),\"Randon Forest\": RandomForestClassifier(n_estimators=100),\"naive Bayes\": GaussianNB(),\"SVC\": SVC(),\"xgb_clf\": XGBClassifier()}\n",
        "\n",
        "p = PrettyTable([\"Algorithim\", \"Accuracy\",\"Confusion Matrix\", \"Classification Report\"])\n",
        "p.padding_width = 1\n",
        "p.float_format='.8'\n",
        "for key in p.align.keys():\n",
        "    p.align[key] = 'l'\n",
        "\n",
        "for key,value1 in funcDict.items():\n",
        "    data,cls_report,cfs1,mse,mae,msse,pipe = Create_pipeline1('PIPELINE', CountVectorizer(), 'nb_clf', value1, x_train,y_train,x_test,y_test)\n",
        "    p.add_row([key,data,cfs1,cls_report])\n",
        "    p.add_row([\"---\",\"---\",\"---\",\"---\"])\n",
        "\n",
        "print(p)\n",
        "\n"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "+---------------+------------+------------------+-------------------------------------------------------+\n",
            "| Algorithim    | Accuracy   | Confusion Matrix | Classification Report                                 |\n",
            "+---------------+------------+------------------+-------------------------------------------------------+\n",
            "| DescionTree   | 0.53196527 | [[264 289]       |               precision    recall  f1-score   support |\n",
            "|               |            |  [304 410]]      |                                                       |\n",
            "|               |            |                  |            0       0.46      0.48      0.47       553 |\n",
            "|               |            |                  |            1       0.59      0.57      0.58       714 |\n",
            "|               |            |                  |                                                       |\n",
            "|               |            |                  |     accuracy                           0.53      1267 |\n",
            "|               |            |                  |    macro avg       0.53      0.53      0.53      1267 |\n",
            "|               |            |                  | weighted avg       0.53      0.53      0.53      1267 |\n",
            "|               |            |                  |                                                       |\n",
            "| ---           | ---        | ---              | ---                                                   |\n",
            "| Randon Forest | 0.54538279 | [[237 316]       |               precision    recall  f1-score   support |\n",
            "|               |            |  [260 454]]      |                                                       |\n",
            "|               |            |                  |            0       0.48      0.43      0.45       553 |\n",
            "|               |            |                  |            1       0.59      0.64      0.61       714 |\n",
            "|               |            |                  |                                                       |\n",
            "|               |            |                  |     accuracy                           0.55      1267 |\n",
            "|               |            |                  |    macro avg       0.53      0.53      0.53      1267 |\n",
            "|               |            |                  | weighted avg       0.54      0.55      0.54      1267 |\n",
            "|               |            |                  |                                                       |\n",
            "| ---           | ---        | ---              | ---                                                   |\n",
            "| naive Bayes   | 0.56985004 | [[ 23 530]       |               precision    recall  f1-score   support |\n",
            "|               |            |  [ 15 699]]      |                                                       |\n",
            "|               |            |                  |            0       0.61      0.04      0.08       553 |\n",
            "|               |            |                  |            1       0.57      0.98      0.72       714 |\n",
            "|               |            |                  |                                                       |\n",
            "|               |            |                  |     accuracy                           0.57      1267 |\n",
            "|               |            |                  |    macro avg       0.59      0.51      0.40      1267 |\n",
            "|               |            |                  | weighted avg       0.58      0.57      0.44      1267 |\n",
            "|               |            |                  |                                                       |\n",
            "| ---           | ---        | ---              | ---                                                   |\n",
            "| SVC           | 0.56353591 | [[152 401]       |               precision    recall  f1-score   support |\n",
            "|               |            |  [152 562]]      |                                                       |\n",
            "|               |            |                  |            0       0.50      0.27      0.35       553 |\n",
            "|               |            |                  |            1       0.58      0.79      0.67       714 |\n",
            "|               |            |                  |                                                       |\n",
            "|               |            |                  |     accuracy                           0.56      1267 |\n",
            "|               |            |                  |    macro avg       0.54      0.53      0.51      1267 |\n",
            "|               |            |                  | weighted avg       0.55      0.56      0.53      1267 |\n",
            "|               |            |                  |                                                       |\n",
            "| ---           | ---        | ---              | ---                                                   |\n",
            "| xgb_clf       | 0.56906077 | [[142 411]       |               precision    recall  f1-score   support |\n",
            "|               |            |  [135 579]]      |                                                       |\n",
            "|               |            |                  |            0       0.51      0.26      0.34       553 |\n",
            "|               |            |                  |            1       0.58      0.81      0.68       714 |\n",
            "|               |            |                  |                                                       |\n",
            "|               |            |                  |     accuracy                           0.57      1267 |\n",
            "|               |            |                  |    macro avg       0.55      0.53      0.51      1267 |\n",
            "|               |            |                  | weighted avg       0.55      0.57      0.53      1267 |\n",
            "|               |            |                  |                                                       |\n",
            "| ---           | ---        | ---              | ---                                                   |\n",
            "+---------------+------------+------------------+-------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nq00CwWqLFq9",
        "colab_type": "text"
      },
      "source": [
        "### Sensational Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mwAxMvwLFq-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Import all the dependencies\n",
        "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
        "from nltk.tokenize import word_tokenize"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wg6sYPUcLFq_",
        "colab_type": "code",
        "outputId": "878f7c31-4df5-4748-a939-27b0d0d5aeb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_XZvKBALFrC",
        "colab_type": "text"
      },
      "source": [
        "##### Using data enrichment from kaggle dataset to get train data for sensationalism to be added as a component to improve accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBM300yCgBz1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sense_link = \"https://drive.google.com/open?id=1VVbzIx8JARjuqcuMNQ4wQrheyD5AidV9\"\n",
        "sensefluff, sense_id = sense_link.split('=')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7eFCSm8GLFrC",
        "colab_type": "code",
        "outputId": "13eb2229-533d-4433-9524-fd6cc5d5e196",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "downloaded=googleDrive.CreateFile({'id':sense_id})\n",
        "downloaded.GetContentFile('sense.csv')\n",
        "df_Sensational = pd.read_csv(\"sense.csv\")\n",
        "df_Sensational"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>headline</th>\n",
              "      <th>article</th>\n",
              "      <th>source</th>\n",
              "      <th>issensational</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>VIDEO: Host Breaks Down As He Learns What Musl...</td>\n",
              "      <td>June 27, 2017Emily AnnReligion, World News\\nA ...</td>\n",
              "      <td>Madworld</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chinese city leadership shake-up signals Xi po...</td>\n",
              "      <td>Beijing – A sudden leadership change in one of...</td>\n",
              "      <td>Brief.News</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Miami judge rules Florida’s new ‘Stand Your Gr...</td>\n",
              "      <td>Brandon Morse 2 hours\\nA Miami-Dade judge rule...</td>\n",
              "      <td>TheBlaze</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Trump, reversing course, wants Senate Republic...</td>\n",
              "      <td>Michael Reynolds / Getty Images\\nPresident Tru...</td>\n",
              "      <td>LA Times – Politics</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pro-life group alleges Twitter is suppressing ...</td>\n",
              "      <td>Kate Scanlon Jun 27, 2017 5:36 pm\\nTwitter has...</td>\n",
              "      <td>TheBlaze</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>No 3rd trial for former Ohio police officer wh...</td>\n",
              "      <td>July 18 (UPI) -- A Cincinnati prosecutor annou...</td>\n",
              "      <td>United Press International – US</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>Putin And Trump Discussed Election Interferenc...</td>\n",
              "      <td>Home Uncategorized  \\n \\nPresident Donald Trum...</td>\n",
              "      <td>US Politics Info</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>White College Employees Fired for Parody Rap A...</td>\n",
              "      <td>July 25, 2017July 25, 2017 Christopher Kemmett...</td>\n",
              "      <td>The Real Strategy</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>Man Stuck Inside Of ATM Begs For Help As Peopl...</td>\n",
              "      <td>Posted by Asia Mayfield | Jul 17, 2017 | Ameri...</td>\n",
              "      <td>Conservative Daily Post</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>Dear NeverTrumpers: Trump Signs VA Accountabil...</td>\n",
              "      <td>Published on June 23, 2017\\n \\nSince the media...</td>\n",
              "      <td>Clash Daily</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              headline  ... issensational\n",
              "0    VIDEO: Host Breaks Down As He Learns What Musl...  ...             1\n",
              "1    Chinese city leadership shake-up signals Xi po...  ...             0\n",
              "2    Miami judge rules Florida’s new ‘Stand Your Gr...  ...             1\n",
              "3    Trump, reversing course, wants Senate Republic...  ...             0\n",
              "4    Pro-life group alleges Twitter is suppressing ...  ...             1\n",
              "..                                                 ...  ...           ...\n",
              "995  No 3rd trial for former Ohio police officer wh...  ...             0\n",
              "996  Putin And Trump Discussed Election Interferenc...  ...             1\n",
              "997  White College Employees Fired for Parody Rap A...  ...             1\n",
              "998  Man Stuck Inside Of ATM Begs For Help As Peopl...  ...             1\n",
              "999  Dear NeverTrumpers: Trump Signs VA Accountabil...  ...             1\n",
              "\n",
              "[1000 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AL3_XHsBLFrI",
        "colab_type": "code",
        "outputId": "0fa0e33c-3131-417d-dee0-f8b51effbaed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data = df_Sensational['article'].values.tolist()\n",
        "print(type(data))\n",
        "tagged_data = [TaggedDocument(words=word_tokenize(_d.lower()), tags=[str(df_Sensational.loc[i,'issensational'])]) for i, _d in enumerate(data)]\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'list'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJeTj6dOLFrW",
        "colab_type": "code",
        "outputId": "e543aa46-1e5c-454e-dd62-c1053d3f1cb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "max_epochs = 100\n",
        "vec_size = 20\n",
        "alpha = 0.025\n",
        "\n",
        "model = Doc2Vec(size=vec_size,\n",
        "                alpha=alpha, \n",
        "                min_alpha=0.00025,\n",
        "                min_count=1,\n",
        "                dm =0)\n",
        "  \n",
        "model.build_vocab(tagged_data)\n",
        "\n",
        "for epoch in range(max_epochs):\n",
        "    print('iteration {0}'.format(epoch))\n",
        "    model.train(tagged_data,total_examples=model.corpus_count,epochs=model.iter)\n",
        "    # decrease the learning rate\n",
        "    model.alpha -= 0.0002\n",
        "    # fix the learning rate, no decay\n",
        "    model.min_alpha = model.alpha\n",
        "savedModel = \"/content/gdrive/My Drive/Drifters/Models/sensationalism.model\"\n",
        "model.save(savedModel)\n",
        "print(\"Model Saved\")"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/models/doc2vec.py:570: UserWarning: The parameter `size` is deprecated, will be removed in 4.0.0, use `vector_size` instead.\n",
            "  warnings.warn(\"The parameter `size` is deprecated, will be removed in 4.0.0, use `vector_size` instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "iteration 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: DeprecationWarning: Call to deprecated `iter` (Attribute will be removed in 4.0.0, use self.epochs instead).\n",
            "  from ipykernel import kernelapp as app\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "iteration 1\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:402: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model Saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2vY3OQliNOdr",
        "colab_type": "text"
      },
      "source": [
        "### Testing the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "or4J_bFZNIHe",
        "colab_type": "code",
        "outputId": "8fc5047f-d57a-46cf-ef03-0ba5ee762638",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from gensim.models.doc2vec import Doc2Vec\n",
        "\n",
        "model= Doc2Vec.load(savedModel)\n",
        "test_data = word_tokenize('A woman named Dakhill (left) tells the story of sex slaves held hostage by ISIS (right).A woman who was held hostage by ISIS was unknowingly forced to eat the unthinkable while being kept in a cell by the fanatics. After ISIS had starved the mother for three days, the Iraqi men served her what she thought was meat and rice, but it was something else entirely — something far worse.ISIS held a Yazidi Christian mother captive in a cell for three days with no food or water before she was tricked into eating something absolutely cringe-worthy by an evil guard. When she finished her meal, the guard said, “We cooked your one-year-old son that we took from you, and this is what you just ate.”The faces behind this gruesome act of terror are devil-worshiping men who believe they are worshiping god and carry out unspeakably cruel acts against minorities in the Middle East daily. These men are taking women and children as sex slaves and massacring thousands every day. Anyone who can do something so unimaginable and follow a religion that allows it is one sick individual.The interviewer wiped away his tears, like anyone being told this horrific story would do, as his guest, identified only as Dakhill, continued with heart-wrenching stories of the captured woman in the Middle East.'.lower())\n",
        "v1 = model.infer_vector(test_data)\n",
        "similar_doc = model.docvecs.most_similar([v1])\n",
        "print(similar_doc[0][0])\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:402: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n",
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9keUd0CLFrY",
        "colab_type": "text"
      },
      "source": [
        "### Adding New Dataset\n",
        "* Additional Kaggle dataset for sensational documents apply doc2vec on it\n",
        "* New dataset added to liar dataset as testdata to find out if a document is sensational or not. \n",
        "* Compare sensationalism result and verdict to check if sesationalism is related to verdict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fidmNfdJLFrY",
        "colab_type": "code",
        "outputId": "6bb438e8-aefd-4a04-a97f-8105e029043e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#computing for entire data\n",
        "sensationCol=[]\n",
        "for row in dtrain_sensa['headline_text']:\n",
        "    test_data = word_tokenize(row.lower())\n",
        "    v1 = model.infer_vector(test_data)\n",
        "    similar_doc = model.docvecs.most_similar([v1])\n",
        "    sensationCol.append(similar_doc[0][0])\n",
        "sensationCol=list(map(int, sensationCol))\n",
        "sensationCol"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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        "dtrain_sensa['sensationCol']=sensationCol\n",
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      "execution_count": 52,
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              "           jsonid        label  ... sensPhrCount sensationCol\n",
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              "2        324.json  mostly-true  ...            0            0\n",
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              "...           ...          ...  ...          ...          ...\n",
              "10235   5473.json  mostly-true  ...            1            1\n",
              "10236   3408.json  mostly-true  ...            0            1\n",
              "10237   3959.json    half-true  ...            0            0\n",
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              "10239   1155.json   pants-fire  ...            0            0\n",
              "\n",
              "[10240 rows x 22 columns]"
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      "source": [
        "# same for testing data\n",
        "testsensationCol=[]\n",
        "for row in dtest_sensa['headline_text']:\n",
        "    test_data = word_tokenize(row.lower())\n",
        "    v1 = model.infer_vector(test_data)\n",
        "    similar_doc = model.docvecs.most_similar([v1])\n",
        "    testsensationCol.append(similar_doc[0][0])\n",
        "testsensationCol=list(map(int, testsensationCol))\n",
        "dtest_sensa['sensationCol']=testsensationCol\n",
        "dtest_sensa"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
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              "      <td>23</td>\n",
              "      <td>18</td>\n",
              "      <td>Radio interview</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>68</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11685.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Wisconsin is on pace to double the number of l...</td>\n",
              "      <td>jobs</td>\n",
              "      <td>katrina-shankland</td>\n",
              "      <td>State representative</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>democrat</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a news conference</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11096.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Says John McCain has done nothing to help the ...</td>\n",
              "      <td>military,veterans,voting-record</td>\n",
              "      <td>donald-trump</td>\n",
              "      <td>President-Elect</td>\n",
              "      <td>New York</td>\n",
              "      <td>republican</td>\n",
              "      <td>63</td>\n",
              "      <td>114</td>\n",
              "      <td>51</td>\n",
              "      <td>37</td>\n",
              "      <td>61</td>\n",
              "      <td>comments on ABC's This Week.</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5209.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>Suzanne Bonamici supports a plan that will cut...</td>\n",
              "      <td>medicare,message-machine-2012,campaign-adverti...</td>\n",
              "      <td>rob-cornilles</td>\n",
              "      <td>consultant</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>republican</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>a radio show</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>85</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9524.json</td>\n",
              "      <td>pants-fire</td>\n",
              "      <td>When asked by a reporter whether hes at the ce...</td>\n",
              "      <td>campaign-finance,legal-issues,campaign-adverti...</td>\n",
              "      <td>state-democratic-party-wisconsin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>democrat</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>a web video</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>127</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1262</th>\n",
              "      <td>7334.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>Says his budget provides the highest state fun...</td>\n",
              "      <td>education</td>\n",
              "      <td>rick-scott</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Florida</td>\n",
              "      <td>republican</td>\n",
              "      <td>28</td>\n",
              "      <td>23</td>\n",
              "      <td>38</td>\n",
              "      <td>34</td>\n",
              "      <td>7</td>\n",
              "      <td>a news conference</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>82</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1263</th>\n",
              "      <td>9788.json</td>\n",
              "      <td>barely-true</td>\n",
              "      <td>Ive been here almost every day.</td>\n",
              "      <td>civil-rights,crime,criminal-justice</td>\n",
              "      <td>jay-nixon</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Missouri</td>\n",
              "      <td>democrat</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>on ABC's \"This Week\"</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1264</th>\n",
              "      <td>10710.json</td>\n",
              "      <td>barely-true</td>\n",
              "      <td>In the early 1980s, Sen. Edward Kennedy secret...</td>\n",
              "      <td>bipartisanship,congress,foreign-policy,history</td>\n",
              "      <td>mackubin-thomas-owens</td>\n",
              "      <td>senior fellow, Foreign Policy Research Institute</td>\n",
              "      <td>Rhode Island</td>\n",
              "      <td>columnist</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a commentary in The Providence Journal</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>147</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1265</th>\n",
              "      <td>3186.json</td>\n",
              "      <td>barely-true</td>\n",
              "      <td>Says an EPA permit languished under Strickland...</td>\n",
              "      <td>environment,government-efficiency</td>\n",
              "      <td>john-kasich</td>\n",
              "      <td>Governor of Ohio as of Jan. 10, 2011</td>\n",
              "      <td>Ohio</td>\n",
              "      <td>republican</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>a news conference</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>96</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1266</th>\n",
              "      <td>6743.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Says the governor is going around the state ta...</td>\n",
              "      <td>state-budget,state-finances,taxes</td>\n",
              "      <td>john-burzichelli</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>democrat</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>an interview with NJToday</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>161</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1267 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          jsonid        label  ... sensPhrCount sensationCol\n",
              "0     11972.json         true  ...            0            0\n",
              "1     11685.json        false  ...            0            0\n",
              "2     11096.json        false  ...            0            0\n",
              "3      5209.json    half-true  ...            1            0\n",
              "4      9524.json   pants-fire  ...            0            1\n",
              "...          ...          ...  ...          ...          ...\n",
              "1262   7334.json    half-true  ...            0            0\n",
              "1263   9788.json  barely-true  ...            0            1\n",
              "1264  10710.json  barely-true  ...            0            0\n",
              "1265   3186.json  barely-true  ...            0            1\n",
              "1266   6743.json        false  ...            0            0\n",
              "\n",
              "[1267 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pa-hyivtLFre",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train=dtrain_sensa[dtrain_sensa.columns[15:22]]\n",
        "y_train=dtrain_sensa['verdict']\n",
        "x_test= dtest_sensa[dtest_sensa.columns[15:22]]\n",
        "y_test = dtest_sensa['verdict']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiXVUB_ZLFrg",
        "colab_type": "code",
        "outputId": "bb4e1567-370b-404e-f646-502ee1e69521",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "x_train"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>10238</th>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>10240 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       puncCount  capCount  digCount  ...  profanCount  sensPhrCount  sensationCol\n",
              "0              0         3         0  ...          0.0             0             0\n",
              "1              1         6         0  ...          0.0             0             0\n",
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              "...          ...       ...       ...  ...          ...           ...           ...\n",
              "10235          0         2         0  ...          0.0             1             1\n",
              "10236          0         2         0  ...          0.0             0             1\n",
              "10237          0         8         0  ...          0.0             0             0\n",
              "10238          0         5         0  ...          0.0             0             0\n",
              "10239          1         6         0  ...          0.0             0             0\n",
              "\n",
              "[10240 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fw2KrilTtfvu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pipedict={}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYot7tXawAoL",
        "colab_type": "code",
        "outputId": "2c5095a3-b2e0-494d-d87b-f9b961578b5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.svm import SVC\n",
        "from prettytable import PrettyTable\n",
        "funcDict = {\"DescionTree\": DecisionTreeClassifier(),\"Randon Forest\": RandomForestClassifier(n_estimators=100),\"naive Bayes\": GaussianNB(),\"SVC\": SVC(),\"xgb_clf\": XGBClassifier()}\n",
        "\n",
        "p = PrettyTable([\"Algorithim\", \"Accuracy\",\"Confusion Matrix\", \"Classification Report\"])\n",
        "p.padding_width = 1\n",
        "p.float_format='.8'\n",
        "for key in p.align.keys():\n",
        "    p.align[key] = 'l'\n",
        "\n",
        "for key,value1 in funcDict.items():\n",
        "    data,cls_report,cfs1,mse,mae,msse,pipe = Create_pipeline1('PIPELINE', CountVectorizer(), 'nb_clf', value1, x_train,y_train,x_test,y_test)\n",
        "    p.add_row([key,data,cfs1,cls_report])\n",
        "    p.add_row([\"---\",\"---\",\"---\",\"---\"])\n",
        "    pipedict[key]=pipe\n",
        "\n",
        "print(p)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "+---------------+------------+------------------+-------------------------------------------------------+\n",
            "| Algorithim    | Accuracy   | Confusion Matrix | Classification Report                                 |\n",
            "+---------------+------------+------------------+-------------------------------------------------------+\n",
            "| DescionTree   | 0.52407261 | [[266 287]       |               precision    recall  f1-score   support |\n",
            "|               |            |  [316 398]]      |                                                       |\n",
            "|               |            |                  |            0       0.46      0.48      0.47       553 |\n",
            "|               |            |                  |            1       0.58      0.56      0.57       714 |\n",
            "|               |            |                  |                                                       |\n",
            "|               |            |                  |     accuracy                           0.52      1267 |\n",
            "|               |            |                  |    macro avg       0.52      0.52      0.52      1267 |\n",
            "|               |            |                  | weighted avg       0.53      0.52      0.53      1267 |\n",
            "|               |            |                  |                                                       |\n",
            "| ---           | ---        | ---              | ---                                                   |\n",
            "| Randon Forest | 0.52249408 | [[230 323]       |               precision    recall  f1-score   support |\n",
            "|               |            |  [282 432]]      |                                                       |\n",
            "|               |            |                  |            0       0.45      0.42      0.43       553 |\n",
            "|               |            |                  |            1       0.57      0.61      0.59       714 |\n",
            "|               |            |                  |                                                       |\n",
            "|               |            |                  |     accuracy                           0.52      1267 |\n",
            "|               |            |                  |    macro avg       0.51      0.51      0.51      1267 |\n",
            "|               |            |                  | weighted avg       0.52      0.52      0.52      1267 |\n",
            "|               |            |                  |                                                       |\n",
            "| ---           | ---        | ---              | ---                                                   |\n",
            "| naive Bayes   | 0.56985004 | [[ 24 529]       |               precision    recall  f1-score   support |\n",
            "|               |            |  [ 16 698]]      |                                                       |\n",
            "|               |            |                  |            0       0.60      0.04      0.08       553 |\n",
            "|               |            |                  |            1       0.57      0.98      0.72       714 |\n",
            "|               |            |                  |                                                       |\n",
            "|               |            |                  |     accuracy                           0.57      1267 |\n",
            "|               |            |                  |    macro avg       0.58      0.51      0.40      1267 |\n",
            "|               |            |                  | weighted avg       0.58      0.57      0.44      1267 |\n",
            "|               |            |                  |                                                       |\n",
            "| ---           | ---        | ---              | ---                                                   |\n",
            "| SVC           | 0.56906077 | [[151 402]       |               precision    recall  f1-score   support |\n",
            "|               |            |  [144 570]]      |                                                       |\n",
            "|               |            |                  |            0       0.51      0.27      0.36       553 |\n",
            "|               |            |                  |            1       0.59      0.80      0.68       714 |\n",
            "|               |            |                  |                                                       |\n",
            "|               |            |                  |     accuracy                           0.57      1267 |\n",
            "|               |            |                  |    macro avg       0.55      0.54      0.52      1267 |\n",
            "|               |            |                  | weighted avg       0.55      0.57      0.54      1267 |\n",
            "|               |            |                  |                                                       |\n",
            "| ---           | ---        | ---              | ---                                                   |\n",
            "| xgb_clf       | 0.58405683 | [[161 392]       |               precision    recall  f1-score   support |\n",
            "|               |            |  [135 579]]      |                                                       |\n",
            "|               |            |                  |            0       0.54      0.29      0.38       553 |\n",
            "|               |            |                  |            1       0.60      0.81      0.69       714 |\n",
            "|               |            |                  |                                                       |\n",
            "|               |            |                  |     accuracy                           0.58      1267 |\n",
            "|               |            |                  |    macro avg       0.57      0.55      0.53      1267 |\n",
            "|               |            |                  | weighted avg       0.57      0.58      0.55      1267 |\n",
            "|               |            |                  |                                                       |\n",
            "| ---           | ---        | ---              | ---                                                   |\n",
            "+---------------+------------+------------------+-------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vFEJYUEfwJ0p",
        "colab_type": "text"
      },
      "source": [
        "## Create and Save the model using Pickle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IGxOZxo2wI98",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "def createModel():\n",
        "  pickleModel = \"/content/gdrive/My Drive/Drifters/Models/sensational_Model.pkl\"\n",
        "  pickle_out = open(pickleModel, \"wb\")\n",
        "  pickle.dump(pipedict[\"Randon Forest\"], pickle_out)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkRJSt7EwgYi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "createModel()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}