EDA_+_Pre_Processing.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "ivAb_Eb0hGTi",
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      },
      "source": [
        "### **Exploratory Analysis + Pre-Processing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nN6Twjj7I_P5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "65bc1f82-b4a6-427a-deb3-1f38f5ad1ced"
      },
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "import spacy\n",
        "import wordcloud \n",
        "from wordcloud import WordCloud\n",
        "import statistics\n",
        "from collections import Counter\n",
        "import datetime\n",
        "import textblob\n",
        "from textblob import TextBlob\n",
        "import sklearn"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEy-MxEjDZDT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "e239d72d-91f1-4da5-a051-5889ca000eae"
      },
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "\n",
        "# Mount google drive\n",
        "DRIVE_MOUNT='/content/gdrive'\n",
        "drive.mount(DRIVE_MOUNT)\n",
        "\n",
        "# create folder to write data to\n",
        "CIS545_FOLDER=os.path.join(DRIVE_MOUNT, 'My Drive', 'CIS545_2020')\n",
        "HOMEWORK_FOLDER=os.path.join(CIS545_FOLDER, 'Project')\n",
        "os.makedirs(HOMEWORK_FOLDER, exist_ok=True)"
      ],
      "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": "5Yob44DdITv0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fake = pd.read_csv('/content/gdrive/My Drive/CIS545_2020/Project/Fake.csv')\n",
        "true = pd.read_csv('/content/gdrive/My Drive/CIS545_2020/Project/True.csv')\n",
        "with open('/content/gdrive/My Drive/CIS545_2020/Project/scraped_articles.json') as json_data:\n",
        "    scraped = json.load(json_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OrOQC_oXS-7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i, site in enumerate((list(scraped['newspapers']))):\n",
        "    articles = list(scraped['newspapers'][site]['articles'])\n",
        "    if i == 0:\n",
        "        df = pd.DataFrame.from_dict(articles)\n",
        "        df[\"site\"] = site\n",
        "    else:\n",
        "        new_df = pd.DataFrame.from_dict(articles)\n",
        "        new_df[\"site\"] = site\n",
        "        df = pd.concat([df, new_df], ignore_index = True)     "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUHBCoD1XjMo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scraped = df\n",
        "scraped[\"label\"] = scraped[\"site\"].apply(lambda x: \"fake\" if(x == \"breitbart\" or x == \"infowars\" or x == \"theonion\") else \"true\")\n",
        "scraped.drop(labels=[\"link\", \"site\"], axis = 1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RSTSVwsRcMwZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create column with labels\n",
        "fake[\"label\"] = \"fake\"\n",
        "true[\"label\"] = \"true\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "rw8fIwR7cTbP",
        "colab_type": "code",
        "outputId": "16ef5ed8-843e-467b-9514-894a32e87a6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "#Check shapes of dataframes. I scraped 1844 articles to add to this dataset.\n",
        "print (fake.shape)\n",
        "print (true.shape)\n",
        "print (scraped.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(23450, 5)\n",
            "(21417, 5)\n",
            "(1844, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9U9EIx0DGxq-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#True news has location and publisher, which fake doesn't have. Remove for consistency\n",
        "true[\"text\"] = true['text'].apply(lambda x: x.partition(\"-\")[2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOq5FtwHL7az",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Convert dates to right format\n",
        "true[\"date\"] = true[\"date\"].apply(pd.to_datetime)\n",
        "fake[\"date\"] = fake[\"date\"].apply(pd.to_datetime)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBhXx_ZpaAOw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scraped[\"published\"] = scraped[\"published\"].apply(lambda x: x[0:10])\n",
        "scraped[\"published\"] = scraped[\"published\"].apply(pd.to_datetime)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pNanbCGSDcC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "84272452-746b-4334-a690-4d758f08b248"
      },
      "source": [
        "#There aren't clear categories, especially within Fake news. It is probably best to remove these categories and use methods such\n",
        "  #as LDA to see if we can get a better sense of the topic\n",
        "print (fake[\"subject\"].unique())\n",
        "print (true[\"subject\"].unique())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['News' 'politics' 'Government News' 'left-news' 'US_News' 'Middle-east']\n",
            "['politicsNews' 'worldnews']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-osxeW9bWsZ-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fake.drop(\"subject\", axis=1, inplace=True)\n",
        "true.drop(\"subject\", axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SXxfblimcENq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Combine scraped with current datasets\n",
        "scraped.rename(columns={\"published\": \"date\"}, inplace=True)\n",
        "scraped_f = scraped[scraped[\"label\"] == \"fake\"]\n",
        "fake = pd.concat([fake,scraped_f], axis=0, ignore_index=True)\n",
        "\n",
        "scraped_t = scraped[scraped[\"label\"] == \"true\"]\n",
        "true = pd.concat([true,scraped_t], axis=0, ignore_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zg7AYtRMxr9B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Remove articles with only pictures / no text\n",
        "true = true[true[\"text\"] != \"\"] \n",
        "fake = fake[fake[\"text\"] != \"\"] "
      ],
      "execution_count": 0,
      "outputs": []
    },
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          "height": 52
        },
        "outputId": "a4828cbb-b673-4d33-f667-c03c6389f9c2"
      },
      "source": [
        "#The datasets are pretty balanced, which is good!\n",
        "print(fake.shape)\n",
        "print(true.shape)"
      ],
      "execution_count": 17,
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            "(24197, 4)\n",
            "(22506, 4)\n"
          ],
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            "f1b844a58ab246a0ada9aca1208476fe",
            "1fce6feee0794d93a6e76b3cf4016585"
          ]
        },
        "outputId": "8fd8386e-ba1d-456b-c288-0433a8200dc7"
      },
      "source": [
        "import ipywidgets as widgets\n",
        "from IPython import display\n",
        "widget1 = widgets.Output()\n",
        "widget2 = widgets.Output()\n",
        "\n",
        "# render in output widgets\n",
        "with widget1:\n",
        "    display.display(fake.head(5))\n",
        "with widget2:\n",
        "    display.display(true.head(5))\n",
        "\n",
        "# create HBox\n",
        "hbox = widgets.HBox([widget1, widget2])\n",
        "\n",
        "# render hbox\n",
        "hbox"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1356fb32773a43b495ff2bf6fc59038d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(Output(), Output()))"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Llsmc14MpmA1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fake.to_csv('fakeForModeling.csv')\n",
        "!cp fakeForModeling.csv \"/content/gdrive/My Drive/CIS545_2020/Project/\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Favfxwk_rz5Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "true.to_csv('trueForModeling.csv')\n",
        "!cp trueForModeling.csv \"/content/gdrive/My Drive/CIS545_2020/Project/\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r52cJRWidP6k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Get number of articles published by day\n",
        "daily_true = pd.DataFrame(true.groupby(\"date\").size())\n",
        "daily_fake = pd.DataFrame(fake.groupby(\"date\").size())\n",
        "daily_true.reset_index(inplace=True)\n",
        "daily_fake.reset_index(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwqpXbWjdhDi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "daily_true.rename(columns={0: \"Number of Articles_true\"}, inplace=True)\n",
        "daily_fake.rename(columns={0: \"Number of Articles_fake\"}, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZ-LcZwDc0gz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "e6a27e8e-9964-4172-b90c-a4b1c31c8bac"
      },
      "source": [
        "#Election day Nov. 8, 2016 - true and fake have peaks there\n",
        "#Distribution \n",
        "from plotly.graph_objs import *\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "layout = Layout(\n",
        "    title='Number of Articles Published, Daily',\n",
        "    xaxis = go.layout.XAxis(title = go.layout.xaxis.Title(text='Date')),\n",
        "    yaxis = go.layout.YAxis(title = go.layout.yaxis.Title(text='Number of Articles')),\n",
        "    paper_bgcolor='rgba(0,0,0,0)',\n",
        "    plot_bgcolor='rgba(0,0,0,0)')\n",
        "\n",
        "fig = go.Figure(layout=layout)\n",
        "fig.add_scatter(x=daily_true['date'], y=daily_true['Number of Articles_true'],mode='lines', line_color='rgb(0,176,246)', name = \"True News\")\n",
        "fig.add_scatter(x=daily_fake['date'], y=daily_fake['Number of Articles_fake'],mode='lines', name = \"Fake News\")\n",
        "fig.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"fe2f9604-5b91-4906-9e79-69516a447490\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "2uLwuuyRnJNi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "6e0ef04e-6a5a-4e83-ab87-87fda9fd1a53"
      },
      "source": [
        "#Both types of articles are around the same length in words, on average\n",
        "words_per_article_plot_t = true[\"text\"].apply(lambda x: len(x.split()))\n",
        "stdev_t = statistics.stdev(words_per_article_plot_t)\n",
        "words_per_article_t = words_per_article_plot_t.sum()/len(true[\"text\"])\n",
        "\n",
        "words_per_article_plot_f = fake[\"text\"].apply(lambda x: len(x.split()))\n",
        "stdev_f = statistics.stdev(words_per_article_plot_f)\n",
        "words_per_article_f = words_per_article_plot_f.sum()/len(fake[\"text\"])\n",
        "\n",
        "print (\"The average number of words in a real news article is \", words_per_article_t)\n",
        "print (\"The average number of words in a fake news article is \", words_per_article_f)\n",
        "\n",
        "print (\"The standard deviation in real news article lengths is \", stdev_t)\n",
        "print (\"The standard deviation in fake news article lengths is \", stdev_f)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The average number of words in a real news article is  390.1627204477811\n",
            "The average number of words in a fake news article is  420.55004132231403\n",
            "The standard deviation in real news article lengths is  338.07536894105294\n",
            "The standard deviation in fake news article lengths is  363.50637962548774\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sF3nf7HFwhmX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "c5c15e77-65c3-4c5c-f96a-a0146dc0ab09"
      },
      "source": [
        "%matplotlib inline\n",
        "fig, ax = plt.subplots(1, 2, figsize=(10, 6))\n",
        "sns.distplot(words_per_article_plot_t, ax = ax[0], color = \"darkblue\", rug = True).set_title(\"Number of Words in Real News Article\")\n",
        "sns.distplot(words_per_article_plot_f, ax = ax[1], color = \"red\", rug = True).set_title(\"Number of Words in Fake News Article\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of Words in Fake News Article')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uylLH2I0ES2l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "article_polarity_true = pd.DataFrame(columns=[\"article\", \"sentiment\"])\n",
        "for article in true[\"text\"]:\n",
        "    article = TextBlob(article)\n",
        "    article_polarity_true = article_polarity_true.append(pd.Series([article,article.sentiment.polarity], index=article_polarity_true.columns), ignore_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Ko2bPlZEWwV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "article_polarity_fake = pd.DataFrame(columns=[\"article\", \"sentiment\"])\n",
        "for article in fake[\"text\"]:\n",
        "    article = TextBlob(article)\n",
        "    article_polarity_fake = article_polarity_fake.append(pd.Series([article,article.sentiment.polarity], index=article_polarity_fake.columns), ignore_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkHOGP2VEbnk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "083e871a-d648-40ed-d13f-464c0e3299f8"
      },
      "source": [
        "print(statistics.mean(article_polarity_true[\"sentiment\"]))\n",
        "print(statistics.mean(article_polarity_fake[\"sentiment\"]))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.054945330608243234\n",
            "0.059747308109913806\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLiBI6QMEa7G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "5b8c5501-58a7-4ddd-fea0-9bfc17e93e35"
      },
      "source": [
        "%matplotlib inline\n",
        "fig, ax = plt.subplots(1, 2, figsize=(10, 6))\n",
        "sns.distplot(article_polarity_true[\"sentiment\"], ax = ax[0], color = \"darkblue\", rug = True).set_title(\"Real News Article Sentiments\")\n",
        "sns.distplot(article_polarity_fake[\"sentiment\"], ax = ax[1], color = \"red\", rug = True).set_title(\"Fake News Article Sentiments\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Fake News Article Sentiments')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iIiJFKCSJiIiIFKGQJCIiIlKEQpKIiIhIEQpJIiIiIkUoJImIiIgUoZAkIiIiUkQs524TEWkUZnYAmAQWgHnn3I5kWyQizUKVJIndJz7xAE88MZZ0M2R1e7lz7iIFJBGphCpJEquxsTne+tZv8M537uADH9iZdHNERJY3OHjq7V27kmmHNAxVkiRWP/zhKABPPTWRcEtkFXPAN83sXjMr+l/PzHaZ2R4z2zMyMlLn5olIo1JIklgpJEkDuMI592LgNcDvmNnLli7gnBt0zu1wzu0YGBiofwtFpCEpJEmsbrzxYQAefvgEg4P7Em6NrEbOuaHgchi4CXhJsi0SkWahMUkSmWIhaHh4GoCJiSy53EK9mySrnJmtAVLOucng+quB9ybcLBFpEgpJEqujR2dOXj9xYi7BlsgqdRZwk5mB39/9s3Pu68k2SUSahUKSxMY5x/DwDJs39zI0NKWQJHXnnHsC2J50O0SkOWlMksRmYiLL3NwCL3jBBgCOH1dIEhGR5qGQJLEJu9rOP38DZnDixGzCLRIRESmfQpLEJhy0ffbZvfT3d6q7TUREmopCksTm6NFZ2tqMDRu62LChS91tIiLSVBSSJDbDw9MMDPSQShkbNnSrkiQiIk1FIUliMzw8y5ln9gCwcWMXJ07MsbCQT7hVIiIi5VFIktiMj2fo7+8EYMOGLvJ5x+HD0wm3SkREpDwKSRKLXG6B6encyZC0cWM3oHO4iYhI81BIkliMj2cB6OvzISkMS4cPTyXWJhERkUooJEksxsczwGJI6uryk7tPTGQTa5OIiEglFJIkFktDUne3D0mTkwpJIiLSHBSSJBZjYz4khd1sXV1pQJUkERFpHgpJEovx8QyplLFmTTsA6XSK9vaUKkkiItI0FJIkFuPjGfr6Okml7OR9XV1tqiSJiEjTUEiSWIyNLc6RFOrqSjMxkUmoRSIiJYyMwMJC0q2QBqKQJLEYH8/S19dxyn1dXW3qbhORxjQ6CtddB/fck3RLpIG0lbOQmR0AJoEFYN45tyPORknzGx/P8Lzn9Z9yX3e3uttEpEEdO+arSMeOJd0SaSBlhaTAy51zevdISblcnunp3MnD/0OdnWmFJBFpTBPB2QAmJ5NthzQUdbdJ5MI5kpaOSeruVnebiDQohSQpotyQ5IBvmtm9ZrYrzgZJ81s6kWTIH92mgdsi0oDCcKSQJAXK7W67wjk3ZGZnArea2cPOue8ULhCEp10Az372syNupjST5UNSmsnJXBJNEhFZmSpJUkRZlSTn3FBwOQzcBLykyDKDzrkdzrkdAwMD0bZSmsrS2bZDXV1tzM3Nk83qEFsRaTAKSVJEyZBkZmvMbG14HXg18GDcDZPmtXS27VB4kluNSxKRhhOGpOlpzZUkJ5VTSToLuMPM9gH3AP/unPt6vM2SZnb8+Oxps23D4vnbFJJEpOEUVpCmppJrhzSUkmOSnHNPANvr0BZpAQsLefbvP86LXnR6l2t3t3+7aRoAEWk4YSUJfGDq64PBwdOX26Vjl1YTTQEgkXr00TFmZua56KIzT3tM3W0i0rCWhiQRFJIkYvfdN0x7e4oLLth42mNhd5umARCRhjMxAW1B54pCkgQUkiQyzjn27Rvmggs20tmZPu3xsJKk7jYRaTiTk3DGGYvXQ9/8Jnz4w/7cbrLqKCRJZJ56aoLR0QwXX3x6Vxuou01EGtjEBGzYAKnUYkjKZuGWW+AHP4D3vx8OHEi0iVJ/CkkSmUcfHQPgwgvPKPp4d3fY3aaQJCINZmICuruht3cxJN17L8zMwC//sr/97/+eXPskEQpJEpm5uXkAenvbiz7e2Rl2t2lMkog0mDAkrVu3GJK++1046yx42ctg2zZ1ua1CCkkSmWw2T1tb6rT5kULhBJPqbhORhjM5CZ2dsHatvz40BI8/Di99KZjB+vUwNpZ0K6XOFJIkMtnsAh0dK7+l1q7tUHebiDSWhQU/gWR392JI+s53/NFul13ml+nv9/dnVAlfTRSSJDK53AIdHacf1VZo3boOVZJEpLGEM2x3dfkxSePjcPfd8OIX+9vgQxLAoUPJtFESoZAkkfGVpNIhSZUkEWko4USSYSUpm4XZWd/VFlq/3l8ODdW/fZIYhSSJTDabV3ebiDSfcKB2Z6cfuA2waRM873mLy4SVJIWkVUUhSSJTXiWpU91tItJYllaSYHHAdkghaVUqeYJbkXKVE5J8JUkDH0WkgYQhqasLtmyB17wGrrji1GW6u6GjA555pv7tk8QoJElkstk8a9YUnyMp5Adu5+rUIhGRMoTdbV1d/ueNbzx9mXAaAFWSVhV1t0lkyh+4ncE5V6dWiYiUUFhJWkl/v0LSKqOQJJEpt7stl8uTySzUqVUiIiUUjklaiULSqqOQJJHxR7eVriSBTnIrIg2k0kpSPh9/m6QhKCRJZMqdcRt0klsRaSCTkz4gpVf+ksf69ZDLwbFj9WmXJE4hSSKRzztyudKVpDAkTU0pJIlIg5iYWJwfaSWaBmDVUUiSSMzNzQOUDEm9vepuE5EGo5Aky1BIkkjMzPjD+svtbpua0jQAItIgyg1J4alJNFfSqqGQJJGYmSm3kuTnUVIlSUQaxuTk4kzbK1m3zs+XdORI/G2ShqCQJJFYrCRpTJKINJlyK0mplA9TY2Pxt0kagkKSRGKxkrTyW2qxkqTuNhFpEOWGJPDjksbH422PNAyFJImEKkki0rSmpmDNmvKW7etTJWkVUUiSSJQ7JqmjI01bW0pjkkSkceRy/uS15ejvV0haRRSSJBLlVpLMjLVrO3R0m4g0jlwO2lc+OfdJ6m5bVRSSJBLljkkCPy5JlSQRaRiVhCR1t60qCkkSiXIrSUBQSVJIkvoxs7SZ3WdmNyfdFmlAqiTJMhSSJBLljkkCX0lSd5vU2duBh5JuhDSgfN7/VFpJci7edklDUEiSSJQ74zb4SpK626RezGwL8NPAx5NuizSgXPCFrZJKUj7vj4iTlqeQJJGYmclhBm1t5YxJ0sBtqasPAX8E5JdbwMx2mdkeM9szMjJSv5ZJ8ioNSX19/lJdbquCQpJEYmZmno6ONGZWcllVkqRezOx1wLBz7t6VlnPODTrndjjndgwMDNSpddIQqqkkgQZvrxIKSRKJmZlcWV1tEI5JUkiSurgcuNrMDgA3Aq8ws88k2yRpKNWGJFWSVgWFJIlEWEkqh58CQN1tEj/n3Lucc1ucc9uANwG3Oed+NeFmSSOptrtNlaRVQSFJIuErSeWFpLVrO5ibm2d+ftkhIiIi9aHuNlmBQpJEYmZmnvb2crvbdP42qT/n3G7n3OuSboc0GA3clhUoJEkkKq0kATrCTUSSp+42WYFCkkSi0jFJgI5wE5HkVRqSurqgs1OVpFVCIUkiUV0lSSFJRBJWaUgaHPQh6c47/XVpaQpJEglfSSp/CgBAR7iJSPKywZe1ckMSQHc3zMzE0x5pKApJEolyK0mDg/u49danAPiXf3mEwcF9cTdNRGR5lVaSwIek2dl42iMNRSFJIlHJmKTOTr/c3NxCnE0SESktDEkdHeU/p6dHIWmVUEiSSFQy43ZXlw9JmYxCkogkrNpKkrrbVgWFJKlZLrdALpevoJLUBsDc3HyczRIRKU3dbbIChSSp2eysDzuVdrepkiQiiVNIkhUoJEnNZmbCkFTe2ymVMjo6UqokiUjyqglJPT3+eTkdodvqFJKkZjMzfkdRbiUJoKurTZUkEUletZUkUDVpFSg7JJlZ2szuM7Ob42yQNJ9qQlJnZ1pHt4lI8hSSZAWVVJLeDjwUV0OkeVXa3QZhJUndbSKSMIUkWUFZ/9XMbAvw08DH422ONKNqK0nqbhORxFU7Jgk0DcAqUO5X/w8BfwTkl1vAzHaZ2R4z2zMyMhJJ46Q5hN1mbW2VVJLU3SYiDUCVJFlByf9qZvY6YNg5d+9KyznnBp1zO5xzOwYGBiJroDS+bNaHnfb28kNSZ6cGbotIA1BIkhWU81/tcuBqMzsA3Ai8wsw+E2urpKmEYSedrrSSpDFJIpKwWkLS3Fz07ZGGUvK/mnPuXc65Lc65bcCbgNucc78ae8ukaYSVpLY2K/s5OrpNRBpCNSGpq8tfqpLU8jRPktQsrCRVMiapoyNNNruAcy6uZomIlJbLQToNVv6XPFIp6OxUSFoF2ipZ2Dm3G9gdS0ukaS1WkioZk5Qmn3fMzyskiUiCcrnKqkih7m51t60CqiRJzbJZf9BjpZUk/1x1uYlIgmoJSaoktTyFJKlZOClkOl3ZmCT/XIUkEUlQtSGpq0shaRVQSJKaVVNJCkOSKkkikihVkmQFCklSszDoqJIkIk1HIUlWoJAkNctkFujoSGMVHB0SjklSSBKRRGngtqxAIUlqls0uVHRyW1B3m4g0CI1JkhUoJEnNstkFOjsrmk1C3W0i0hhqqSRls4uTUUpLUkiSmvnutsreSpoCQEQaQi0hCWByMtr2SENRSJKa+e62dEXPUSVJRBpCLd1tAOPj0bZHGopCktQsm82fDD3l0sBtEWkItVaSFJJamkKS1Cw8uq0SCkki0hBqDUkTE9G2RxqKQpLUzA/criwkpVJGe3tKY5JEJFm5HHR0VP48VZJWBYUkqVk1Y5LAj0tSJUlEEqXuNlmBQpLUrJqj28CHJFWSRCRRtQ7cVndbS1NIkppVM08S+HFJqiSJSKJUSZIVKCRJzbLZfNWVJIUkEUlUtSGpvR3SaVWSWpxCktQsk5mvekySuttEJFHVhiQz3+WmSlJLU0iSmlUzTxKou01EGkC1IQl8l5tCUktTSJKa1XJ0mypJIpKoWkOSuttamkKS1KyaySRBY5JEpAGokiQrUEiSmlUzmST47jZVkkQkUdmsQpIsSyFJaua723R0m4g0IXW3yQoUkqRmmUz18yQtLDhyOQUlEUlILSFJR7e1PIUkqYlzjlyu+nmSAKanc1E3S0SktIUFcK72SpJz0bZLGoZCktQkl8sDVDVwO3yOQpKIJCIX7HtqCUkLCzAzE12bpKEoJElNwjFF1QzcViVJRBJVa0gKz9+mLreWpZAkNQmPTqt2CgBQSBKRhERRSQIN3m5hCklSk7CSVEtImppSSBKRBEQVklRJalkKSVKTsJJU7TxJoEqSiCREIUlKUEiSmqi7TUSalrrbpASFJKnJYnebpgAQkSajgdtSgkKS1GSxu626ySRBIUlEEqLuNilBIUlqks2G8ySpkiSNx8y6zOweM9tnZvvN7D1Jt0kaSFSVJHW3tazKv/6LFMhk5gFNJikNKwO8wjk3ZWbtwB1mdotz7q6kGyYNoNaQlEpBb68qSS1MIUlqElaSqjm6LZUy2ttTCkkSG+ecA6aCm+3Bj84hIV6tIQmgr0+VpBam7japSS1Ht4XPU0iSOJlZ2sz2AsPArc65u5NukzSIqEKSKkktSyFJalLLaUnC5ykkSZyccwvOuYuALcBLzOyFS5cxs11mtsfM9oyMjNS/kZKMKELSunUKSS1MIUlqUmslSSFJ6sU5NwZ8G7iqyGODzrkdzrkdAwMD9W+cJCMMSR0d1a9D3W0tTSFJarIYkqp7KykkSZzMbMDM+oPr3cCrgIeTbZU0DHW3SQkauC01Wexuq+6tpDFJErOzgU+ZWRr/pfALzrmbE26TNAp1t0kJCklSk1orSR0daWZmFJIkHs65+4GLk26HNCgd3SYlqLtNarI4mWS1R7elmJmZj7JJIiLliaqSNDOzuC5pKQpJUpNaj27r6EgzO6uQJCIJiKqSBKomtSiFJKlJ2N3W3l5LJUnfwEQkAQpJUoJCktQkm12grS1FKmVVPb+9Pa3uNhFJRlTdbaDB2y1KIUlqksksVN3VBhq4LSIJUiVJSlBIkppkswtVD9oGH5IWFhy53EKErRIRKUOUIUmVpJakkCQ1yWbzNVaS/FtQXW4iUnfqbpMSSoYkM+sys3vMbJ+Z7Tez99SjYdIcMpn5qudIgsWpA9TlJiJ1F4akthqmDFR3W0sr552RAV7hnJsys3bgDjO7xTl3V8xtkyaQzeZr7G5TJUlEEpLL+YBk1R14Aqi7rcWVDEnOOQdMBTfbgx8XZ6OkeWSztQ/cBlWSRCQBuVxtXW0AnZ1+HQpJLamsfhIzS5KuWS0AACAASURBVJvZXmAYuNU5d3eRZXaZ2R4z2zMyMhJ1O6VBZTK1Ddxub/dvQU0oKSJ1F0VIMtOpSVpYWSHJObfgnLsI2AK8xMxeWGSZQefcDufcjoGBgajbKQ0qiqPbQJUkEUlAFCEJfEhSJaklVTTi1jk3BnwbuCqe5kiziWKeJNCYJBFJQFQhad06haQWVc7RbQNm1h9c7wZeBTwcd8OkOaiSJCJNK8pKkrrbWlI5laSzgW+b2f3A9/Fjkm6Ot1nSLHxIqmUKAI1JEpGEqJIkJZRzdNv9wMV1aIs0Id/dVv0cI+puE5HEZLOqJMmKNOO21MTPk1R7JUndbSJSdxq4LSUoJElNohuTpEqSiNRZlN1tExPgNIVgq1FIkprUenRbOp2irS2lMUkiUn9RVpIWFmBmpvZ1SUNRSJKa1FpJAujublN3m4jU3+ws9PTUvh6dmqRlKSRJTWo9LQlAT0+buttEpP5mZqIJSevW+UuFpJajkCQ1qfW0JAA9Pe2qJIlI/c3MwJo1ta8nrCTpCLeWo5AkVXPO1TxPEoSVJIUkEamzKCpJg4Owe7e//rnP+dvSMqqf4EZWvYUFh3PUNE8S+EqSBm6LSF0NDsLx43DgQO3BprvbX87O1twsaSyqJEnVMhkfbGqtJPmB2wpJIlJn2Sx0dNS+HoWklqWQJFXLZvMAEYxJUnebiNSZcwpJUpJCklQt7CLr6qq9u02VJBGpq/l5H5SiCEmdnf5ybq72dUlDUUiSqk1P++pPb29tk7FpTJKI1F026y+jCEmpFHR1qZLUgjRwW6r2qU89CMD3vjd0MjBVQ5NJikjdRRmSQCGpRamSJFXLZhcANJmkiDSfqENSd7e621qQQpJUbW4uqpCkySRFpM7iCEmqJLUchSSpWpSVpFwuz/x8PopmiYiUpu42KYNCklQtkwlDUu1HtwEavC0i9aNKkpRBIUmqFoakWudJ6u72IUtdbiJSN5mMv1RIkhUoJEnVwpDU1VV7dxsoJIlIHWngtpRBIUmqlsnMYwbt7bWe4NZ3t+kINxGpmzhCUjYLCwvRrE8agkKSVC2TWaCjI42Z1bSesJKkMUkiUjdxhCRQl1uLUUiSqmUyCzUf2QbQ3R1WktTdJiJ1EsfRbaAutxajkCRViyokLY5JUiVJROokmyUYLxDN+lRJakkKSVK16EKSKkkiUmfZrK8i1Thc4CSFpJakkCRVy2ZVSRKRJhWGpKiE3W0KSS1FIUmqNje3UPNEkrA4T5IGbotI3UQdklRJakkKSVK1bHah5okkQd1tIpIAhSQpg0KSVC2TWah5IklQd5uIJCCbjW7QNiyGJB3d1lIUkqRqmcx8JJWkjo40qZSpkiQi9RN1Jam9HdraVElqMQpJUrWojm4zM3p62jQmSUTqJ+qQBH7wtipJLUUhSaqSzzuy2XwkIQn8uKSpKVWSRKRO4ghJOslty1FIkqqEXWNRhaTe3nampxWSRKROFJKkDApJUpWw6hNVSFq7toPJyWwk6xIRKSmbhc7OaNepkNRyFJKkKmHVJ7pKUgdTUwpJIlInmUw8laSZmWjXKYlSSJKqhIEmiskkBwf3MTo6xxNPjDM4uK/m9YmIlBRHd1tPjypJLUYhSaoSdSWpqyvN3JyObhOROsjlIJ+PJySpktRSFJKkKlGPSersbCOTWYhkXSIiKwqDTBwhKZPxIUxagkKSVCWsJEUxmST4sKWQJFEzs2eZ2bfN7Admtt/M3p50m6QBxBWSwlm3x8ejXa8kRiFJqhKOSYritCThejIZdbdJ5OaBdzjnLgAuBX7HzC5IuE2StDgrSQCjo9GuVxKjkCRVCbvboqwkzc875ufzkaxPBMA5d9g591/B9UngIWBzsq2SxMUdksbGol2vJEYhSaoS/cBtf5ScutwkLma2DbgYuDvZlkjiVEmSMikkSVXC7rYoK0mAutwkFmbWC/wL8AfOuYkij+8ysz1mtmdkZKT+DZT6mp72l6okSQkKSVKV6ekcHR0pUimLZH1hSJqbUyVJomVm7fiA9Fnn3L8WW8Y5N+ic2+Gc2zEwMFDfBkr9xT1wWyGpZSgkSVWmpnKRdbVBYSVJIUmiY2YGfAJ4yDn3d0m3RxqEutukTApJUpXp6Vwks22HwjFJmlBSInY58GvAK8xsb/Dz2qQbJQmLKyR1dEA6rUpSC4nuv5ysKlNTucjGI4EqSRIP59wdQDR9wtI64gpJZr6apEpSy1AlSariK0nRhySNSRKR2MUVksCHJFWSWoZCklRlaiob2USSUDgFgLrbRCRmcYak7m6FpBZSMiRpWn8pxh/dpu42EWlCU1PQ3g6pGOoE6m5rKeW8QzStv5wm6qPbwsCl7jYRid3wMPT2xrNudbe1lJIhSdP6SzGTk9F2t6VSFpzkVt1tIhKzI0egry+edauS1FIqqjWuNK2/ZqxdXcbHM3R3t0e6Th+SVEkSkZgdPQrr1sWz7nBMknPxrF/qquyQVGpaf81Yu3rMzc2TySzQ0xPtDBJdXQpJIlIHR47EF5J6eiCXg9nZeNYvdVVWSCpnWn9ZPcbGMgB0d0cbkjo72zSZpIjEa2EBRkbiDUmgLrcWUc7RbZrWX04xNjYHQE+PuttEpMkcP+6DUpzdbaDB2y2inEqSpvWXU8RXSVJIEpGYHTniL+MKSWvW+EtVklpCyf9ymtZfloorJHV1tXH8+Fyk6xQROcXRo/4yrqPbVElqKZpxWyoWhqSoB25rCgARiV1YSVq7Np71h2OSFJJagkKSVExjkkSkaYUhKc55kkDdbS1CIUkqFueYpLm5BZzmFxGRuBw96oNMZ2c861clqaUoJEnFxsYydHSkaW+P9u3T1dVGPu9UTRKR+Bw5AmedBRbTUNt02g/eViWpJSgkScXGxjL093diEe9kwnPBTU1lI12viMhJR47Apk3xbmP9ejhxIt5tSF0oJEnFxsbm6O+PvlS9GJJyka9bRATw3W1xh6SNG/18TNL0FJKkYmElKWpdXX6M0+SkKkkiEpOwuy1OGzeqktQiFJKkYj4kdUW+XlWSRCRWuZyv8KiSJGVSSJKKxVdJ0pgkEYnRyAg4F38lacMGhaQWoZAkFYtvTJLvblMlSURiEc6RVI9K0okTkM/Hux2JnUKSVGx8PBvrwG2NSRKRWISnJKlHSMrnYWIi3u1I7BSSpCJzc/PMzc3HMiYpnJxyfDwT+bpFRE5WkuoxcBvU5dYCFJKkImGAiaOSFJ4L7sQJneRWRGJQr5C0YYO/VEhqegpJUpHwlCRxhKR0OkV3dxvHj89Gvm4REY4ehXXrFk8dEpewkqRpAJqeQpJUJM6QBLBmTbsqSSISj3rMkQTqbmshCklSkbExH2DiGJMEvstNIUlEIjc4CPfd56cAGByMd1sKSS1DIUkqEnclqbe3QyFJROIxMeG72+K2fr2/VEhqegpJUpH4u9s0JklEYlKvkJROQ3+/xiS1AIUkqchid1s8IamnR2OSRCQGuRzMzEBfX/zbGhyE9na4++74u/YkVgpJUpGxsQwdHemTJ6ONWm9vO6Ojc+TzLpb1i8gqNTnpL+tRSQJYswamp+uzLYmNQpJUJDxvm5nFsv6ennac04SSIhKx8XF/Wa+Q1NurkNQCFJKkInGd3Da0Zk07oAklRSRi4SlCVEmSCigkSUVGR+fo64s/JGnwtohEKomQNDVVn21JbBSSpCKjo3Ns2BDPHEmgSpKIxCQMSWvX1md7a9bA3BwsLNRnexILhSSpyOhohvXrFZJEpMlMTPjg0t5en+2tWeMv1eXW1BSSpCInTtSrkqTuNhGJUL3mSAr19vpLhaSmppAkZcvnHaOjc7FWknp6/NQCx4+rkiQiERofr29IUiWpJSgkSdkmJ7Pk8y7WSlI6naKvr1PdbSISrcnJZEKSBm83tXhmBJSWMzi4j2PHfBfYvn3DDA7ui21bGzZ0KSSJSLTqXUkKu9sUkpqaKklStpmZHLA4biguGzd2aUySiERnehoymfqGpHBb4VF10pQUkqRs09M+JPX0xBuSNmzo1pgkEYnO0aP+sp4hqb3dd7mNjdVvmxI5hSQp28zMPBB/JUndbSISqUOH/GU9Tm5bqL9/8XQo0pQUkqRsi5WkeIeyKSSJSKSGhvxlf399t9vXp0pSk1NIkrLVc0zS6Ogc+byLdTsiskokFZL6+xWSmpxCkpRtenqetrYUHR3pWLezYUM3+bxjfDwT63ZEZJUYGvJjhHp66rvdvj4/cFunJmlaCklStpmZXOxdbcDJeZjU5SYikRgagvXrway+2+3vh3weRkbqu12JjEKSlG1mJhd7Vxv47jaA48c1DYCIRGBoqP5dbbC4zXDguDQdhSQp2/T0fOyH/wMMDPiS+MjITOzbEpFVQCFJqqSQJGXzlaT4u9sGBroBGBlRJUlEauScDylJhKRwyoFw4Lg0HYUkKZsfkxR/JenMM30laXhYlSQRqdGJE3627SRC0rp1fhyUKklNSyFJyjY9XZ8xSWvWdNDd3abuNhGpXVKH/wOk07B2rUJSE1NIkrIsLOSZm1uoy9Ft4LvcVEkSkZolGZLC7SokNS2FJClLvU5JEjrzzB6NSZJImNknzWzYzB5Mui2SAIUkqYFCkpQlnG27HmOSwB/hpu42icgNwFVJN0ISEoakep+3LaSQ1NQUkqQs4Xnb6llJUnebRME59x3gRNLtkIQMDcGZZ0JbfYYKnKavD4aHIZtNZvtSE4UkKUvY3VbPMUkjI7M4p/O3iUgNhoZg8+bkth928x05klwbpGoKSVKWeleSBgZ6mJ2dP7ldkTiZ2S4z22Nme0Z0ConW0igh6eDB5NogVVNIkrLUe0xSOFeSxiVJPTjnBp1zO5xzOwYGBpJujkQp6ZB0xhn+8sknk2uDVK1kSNKRIQKLlaR6drcBDA/rCDcRqVImA8eOJRuSNm70lwpJTamcStIN6MiQVW9yMkdPTxvpdH2Kj6okSVTM7HPAncDzzewZM/vNpNskdRIeVXbOOcm1ob3dh7QnnkiuDVK1kmUB59x3zGxb/E2RRjY2lqG/v7Nu29NJbiUqzrk3J90GSUgYkjZvhqefTq4d552nkNSkIus7MbNdwC6AZz/72VGtVhrE+HiGvr76hKTBwX1kMgsAfPWrj5PL5dm1a3tdti0iLSScIynpkHTuuXDbbcltX6oWWd+JBj62Nl9J6qrb9jo703R0pJia0tFtIlKlwpCUpPPO823JZJJth1RMR7dJSfm8Y3y8vt1tAGvXdjA5qQnYRKRKQ0PQ1QXr1yfbjvPOA+fgqaeSbYdUTCFJShoZmSGfd3XrbgspJIlITcLD/82Sbcd55/lLjUtqOuVMAaAjQ1a5Q4emAFi/vr4hqbdXIUlEapD0HEmhc8/1lwpJTaeco9t0ZMgqNzTkQ1ISlaRw2yIiFRsagksuSboVsGmT7/ZTSGo66m6TksJKUv3HJLUzNZXV+dtEpHLO+ZCU5BxJoVTKV5M0oWTTUUiSkg4dmsIM1q3rqOt2+/u7yOXyTE7qCDcRqdCJE/5oskbobgPNldSkFJKkpKGhKdau7ajbbNuhTZv8hJJHjkzXdbsi0uQGB+HDH/bXH3rI3066PdPT8PDD8LGPJdsWqYhCkpR06NBU3bvaADZtWgMoJIlIFcbG/GV/f7LtCG3aBHNzi+2SpqCQJCUNDSUTktav76KjI8XhwwpJIlKhRgtJW7f6S82V1FQUkqSkpCpJqZSxadMaVZJEpHKNFpK2bPEDuA8cSLolUgGFJFlRNrvAyMhs3Q//DykkiUhVxsZg7Vpoi+wUpbXp6PCDyFVJaioKSbKiw4fDw//rd962Qps2reHEiTmmpjSppIhUYGyscapIoa1bfUjStCZNQyFJVnTokK/iJNHdBnD22X7w9iOPnEhk+yLSpE6caMyQND2tLrcmopAkKzp4cAKo/2zbofAIt4ceUkgSkTItLMDRo3D22Um35FTh4O09e5Jth5RNIUlWdOedh+nqajsZVurtzDN7SKWMhx9WSBKRMg0Pw/x8Y8y2XWjzZj9G6vvfT7olUiaFJFnR7t0H+YmfOIf29mTeKm1tKQYGunnooeOJbF9EmtChQ/6yUWbbDrW1+aPcFJKahkKSLGt0dI59+4a58sotibZj06Y1/OAHCkkiUqZDh8DMT+DYaLZt8yFpfj7plkgZFJJkWd/97jM4B1de+axE27FlSy8//OEos7M6h5uIlOHQIRgY8IfdN5rnPtcP3t63L+mWSBkUkmRZt99+kM7ONJdckuzgxy1b1pLPOx588Fii7RCRJnHoUOONRwo997n+8nvfS7YdUhaFJClqcHAfX/rSD9m6dR2f/vT+RNvyrGetBWDv3pFE2yEiTSCT8QO3G+3IttD69f4otzvuSLolUgaFJClqdjbHwYOTPO9565NuChs3drN2bQf79g0n3RQRaXSPPAL5fOMN2i50xRU+JGlSyYankCRFPfnkBM7Bc5+b/GRsqZSxffuAKkkiUtqDD/rLRu1uA7j8cjh8GJ58MumWSAkKSVLUgQPjAGzbti7hlnjbtw9w//0j5PP65iUiK9i/359I9qyzkm7J8q64wl9qXFLDU0iSog4cmOCss3ro6WlPuikAXHTRmUxOZnnyyfGkmyIijWzfPn/of6Oc2LaYCy/0p0zRuKSGp5Akp3HO8eST45x7bl/STTnpiSfGAPjAB+5hcFCHzorIMu67D56V7LQlJaVS8OIXw969SbdESlBIktM888wkExPZhulqAzjnnF7MfNtERIo6etQf/t/oIQngRS+CBx7w55mThqWQJKe5554jAGzb1jiVpI6ONJs39+pEtyKyvPvu85fPfnay7SjH9u0wOwuPP550S2QFCklymnvuOUxbm7Fly9qkm3KKSy45myeeGOfQoamkmyIijSgMSc1SSQK4//5k2yErUkiS09xzzxG2bFmb2Eltl3PppeeQTht33DGUdFNEpBHddx+cey709CTdkpUNDsKdd/qxSTfc4G9LQ2qs/4KSuHzesWfPkYbqagutW9fB9u0D3HXXYebmdHJIEVnivvvg4ouTbkV52tv9NAXPPJN0S2QFCklyiscfH2NqKnfyVCCN5qUv3cL0dI7PfvahpJsiIo1kfBwee8wfNdYstmxRSGpwCklyivDUH1u29CbckuLOP38D553Xx+///re4/37NwC0igX3B1CDNUkkCf+qU48f9AG5pSApJcop9+0ZIpYxzzmnMkJRKGW9723b6+7u4+uqbGB2dS7pJIpK0wUF4//v99f3JnpC7Ilu2+MshjbNsVApJcop9+0Z4/vPX09GRTropy+rr6+Tzn38dTz01wRe/+EjSzRGRpB09Ct/6Flx2GfQ13njKZYUh6eDBZNshy1JIklPs2zfMRRedmXQzSrr88s1s3tzLrbc+lXRTRCRJzsGNN/qB0D/7s0m3pjL9/XDGGYsn5ZWGo5AkJ42OzvH005Ns3z6QdFNKMjNe9aqtfOtbT7OwkE+6OSKSlG9/G37wA3j962Fd45wloCxmfgzVww/7gefScBSS5KRwIPT27Y1fSRoc3Ec6bYyOzvHnf36HzucmsloNDvp5ka68MumWVOfii2F+Hr72taRbIkUoJMlJ+/aFIanxK0kA55+/EUCnKhFZrY4dg5tugksv9d1tzejcc30F7F//NemWSBHmnIt8pTt27HB79uyp6rk7d97I7t1virhFy6+31u1df/33uP76y4vev3j98pP3vec9d+LcO9m27WMcOPBb7Nx5I3v3DjM29vunPS9cFmDbto9x7bUvPOWxK6/cwt69w0xNZU+eIzGdPvV8iUtvi2fmhzIAbN26lqeemsTM306l/O/suusu40Mfupe5uXkuvfQcbr/9Ga677jL+6q/u5tJLz2Hv3sXxWzt3Lp4GYffug9xxxzPMz/u/Xfg3LfY+CRV7H4bvkXKWLddy79da12tm9zrndlT15AZTy/7rlDfWzp2we/fKy19/vf8pZ9n+fhgbO/W+bdvgwIHy2nb99X4bO3cu3t650z//wIFT27Bzp/+5/vpT1xG+vsJ1ha8h/PnQh3w7d+5cPMv9xAS87GX++u23w9at8NRTp67zuuv8c+fmIJMp7zXJqb/LdHpx7qUtW07/Hff1+d9vNuvPb3fttX7Gb/DP+bM/89d374Y77vAVLjNfpQvfZwcOnP4337bNr6vw/VL4WQjvv/56v4PN5xdvFz4nfG+G78NSjxcq3F4VltuHNVxIMvvbk8EgSsutt9btrbTeUPh4eJ9z7zz5vML7lj5vpful8RX7uy+n2PsojvfsSs+tbb0KScCpO+pydtrhMpUsW+q+lZ5fKNxu4fXCtof3L9fepesptj5pbsX+jkv/3kv/7qHl3k8rfUaWrqfU44ViCknqbhMREREpQiFJREREpAiFJBEREZEiFJJEREREilBIEhERESlCIUlERESkCIUkERERkSIUkkRERESKUEgSERERKUIhSURERKQIhSQRERGRIsoKSWZ2lZk9YmaPmdmfxN0oEZGoaP8lItUqGZLMLA38A/Aa4ALgzWZ2QdwNExGpVV33X3Nzsay2ISwswIc/7K9/4xvJtkXqZ3g46RaUZ2zMX+bzka+6rYxlXgI85px7AsDMbgTeAPwg8tZEzDnH9HSO0VG/85qczNLd3YZzjrGxDLmc/4WOjs7R1paivT1FW1uKdNqwCs9g7ZzDOcjnHWNjp+4sb7rpUfbvP3by9rvf/V0A/u//ve/kfbfd9jT9/Z2nrffv/m4Pz3lOf0Vtkcbw8Y/fz+bNvSdvO+dOeV8558hmF5idnQdgZiZHW1uKycksExMZAA4cGOfss9fgHMzOzjM355cdHZ2jt7ed9vZ0ze3MZOY5fHj6ZBt6etprXmcDiXf/tbAABw7ATTfB3/yNv2/7dnjlK/31gwfh7LOhbcmuNp+H2Vl//emn/eXwMGzcCKnU4tnMx8f9OsJln3gCNmzw2w1D2YEDcOaZ0NNzevvyef8PZGTk9Me+9rXF61/5ir+8807o7l68/+mn4ZvfhPe+17cD4KqrTl3PI4/4y+9/f/G+q68+fXvSfM466/T7wvfK/v3+8j/+Y/GxO+6A73wH7r3X377mGnjhCxcfDz8jN98ML3iBvz466t+jJ04sLvfYY4vvwyNH/Ps9DEIAjz8O6bR/v375y/Bv/+bv/7Efg/e8By6/3H+WImAu/DAut4DZzwNXOefeGtz+NeAS59zvLvecHTt2uD179pTc+G/8xtf5whf8Byxsx8zMPN3dizuUwuYVtrWc+/N5Rz6/8utbThiaUikjn/cBqDAIFV4vh9li28J1yuqTThuplP/JZhco8fErqaMjTXt76uRnwL9PCa4X3uev53J50mk7eT+c/h5Op42OjjQf+MCV/M7vXFxWO8zsXufcjtpeTfTi3H9xyy3wMz8DGR9oedWr4NZb4fzz/U5+fn5x2bY2v1NPpXxwCZ8TpTCAmy1ez+ep+U0GPoS94Q3wj/8Ir33tqQGrmHPOgUOHat+uJOvqqxdDUSXOOguOHq3P+2DtWh+Odu+GrVvhqaf8/Zdf7kNbmZbbh0UWksxsF7AruPl84JGyW3eqM4BjJZdqXq38+lr5tYFe30q2OucGomxMFLT/ipxeX/Nq5dcGtb++ovuwcrrbhoBnFdzeEtx3CufcIDBYdfMCZranEb+RRqWVX18rvzbQ62tS2n9FSK+vebXya4P4Xl85R7d9H3iemZ1rZh3Am4Aq6m8iInWn/ZeIVK1kJck5N29mvwt8A0gDn3TO7Y+9ZSIiNdL+S0RqUU53G865rwElRupFpuaSd4Nr5dfXyq8N9PqakvZfkdLra16t/NogptdXcuC2iIiIyGqk05KIiIiIFJF4SDKzXzCz/WaWN7NlR6Y366kFzGyDmd1qZo8Gl+uXWW7BzPYGPw09sLTU38LMOs3s88Hjd5vZtvq3snplvL5rzWyk4O/11iTaWQ0z+6SZDZvZg8s8bmb298Frv9/MXlzvNjYT7b9OLtc0+y9o7X2Y9l8R77/8pIjJ/QAvwM9LshvYscwyaeBx4DygA9gHXJB028t8fX8D/Elw/U+Av15muamk21rm6yn5twD+O/DR4PqbgM8n3e6IX9+1wEeSbmuVr+9lwIuBB5d5/LXALYABlwJ3J93mRv7R/uvkck2x/yr379Gs+zDtv6LffyVeSXLOPeScKzVx28lTCzjnskB4aoFm8AbgU8H1TwFvTLAtUSjnb1H4mr8E/KRVep6X5DTze60k59x3gBMrLPIG4NPOuwvoN7Oz69O65qP9V1Nq5X1YM7/XSkpi/5V4SCrTZuBgwe1ngvuawVnOucPB9SNAkZPhANBlZnvM7C4za+QdUTl/i5PLOOfmgXEgmhPpxK/c99rPBeXcL5nZs4o83qya+bPWqJr5d9pq+y9o7X2Y9l8Rf9bKmgKgVmb2H8CmIg+92zn3b/VoQ5xWen2FN5xzzsyWO5xwq3NuyMzOA24zswecc49H3VaJxFeBzznnMmb2W/hvnK9IuE0SE+2/PO2/Wob2XxWoS0hyzr2yxlWUdWqBpKz0+szsqJmd7Zw7HJT9hpdZx1Bw+YSZ7QYuxvctN5py/hbhMs+YWRvQBxyvT/NqVvL1OecKX8vH8eM2WkVDf9aSoP1XS+2/oLX3Ydp/RfxZa5butmY+tcBXgGuC69cAp33zNLP1ZtYZXD8DuBz4Qd1aWJly/haFr/nngdtcMKquCZR8fUv6uK8GHqpj++L2FeDXg6NELgXGC7pbpDrafzWWVt6Haf8V9f6rAUar/wy+3zADHAW+Edx/DvC1JaPWf4j/dvLupNtdwevbCHwLeBT4D2BDcP8O4OPB9Z8AHsAfifAA8JtJt7vEazrtbwG8F7g6uN4FfBF4DLgHOC/pNkf8+v4S2B/8vb4NnJ90myt4bZ8DDgO54HP3m8DbgLcFjxvwbwhmNwAAAsdJREFUD8Frf4BljtjSz8nfp/ZfTbb/Wu7v0Sr7MO2/ot1/acZtERERkSKapbtNREREpK4UkkRERESKUEgSERERKUIhSURERKQIhSQRERGRIhSSpCJmdpGZvbbg9tVxn9XczHaa2U/EuQ0RaX3af0mlFJKkUhfh5+EAwDn3FefcX8W8zZ34uVhERGqh/ZdURPMkrSJmtgb4An6q9jTwF/jJ0v4O6AWOAdc6fwqC3cDdwMuBfvykXXcHy3fjp3r/y+D6Dufc75rZDcAs/pQEZwK/Afw6cBlwt3Pu2qAdrwbeA3TiJ/16i3NuyswO4M8j9HqgHfgFYA64C1gARoDfc859N47fj4g0Lu2/JAmqJK0uVwGHnHPbnXMvBL4O/H/Azzvnfgz4JPD+guXbnHMvAf4AuM45lwX+F/B559xFzrnPF9nGevxO5X/gp4j/IHAh8KNBqfsM4M+AVzrnXgzsAf6w4PnHgvv/H/BO59wB4KPAB4Ntagcjsjpp/yV1V5cT3ErDeAD4P2b218DNwCjwQuBWMwP/7azwPDf/GlzeC2wrcxtfdc45M3sAOOqcewDAzPYH69gCXAB8L9hmB3DnMtv82Qpem4i0Nu2/pO4UklYR59wPzezF+D759wG3Afudc5ct85RMcLlA+e+V8Dn5guvh7bZgXbc6594c4TZFpMVp/yVJUHfbKmJm5wAzzrnPAB8ALgEGzOyy4PF2M7uwxGomgbU1NOMu4HIze26wzTVm9iMxb1NEmpz2X5IEhaTV5UeBe8xsL3Advn/+54G/NrN9wF5KH4XxbeACM9trZr9UaQOccyPAtcDnzOx+fKn6/BJP+yrwM8E2X1rpNkWkJWj/JXWno9tEREREilAlSURERKQIhSQRERGRIhSSRERERIpQSBIREREpQiFJREREpAiFJBEREZEiFJJEREREilBIEhERESni/wfBN+XwJQc/mQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uNwsDWPz-6qz",
        "colab_type": "text"
      },
      "source": [
        "# Headlines"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tgA43MFd5aDu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "d9c2b469-aeb2-4025-d18a-58943b225375"
      },
      "source": [
        "#Both types of articles are around the same length in words, on average\n",
        "words_per_headline_plot_t = true[\"title\"].apply(lambda x: len(x.split()))\n",
        "stdev_t_head = statistics.stdev(words_per_headline_plot_t)\n",
        "words_per_headline_t = words_per_headline_plot_t.sum()/len(true[\"title\"])\n",
        "\n",
        "words_per_headline_plot_f = fake[\"title\"].apply(lambda x: len(x.split()))\n",
        "stdev_f_head = statistics.stdev(words_per_headline_plot_f)\n",
        "words_per_headline_f = words_per_headline_plot_f.sum()/len(fake[\"title\"])\n",
        "\n",
        "print (\"The average number of words in a real news article is \", words_per_headline_t)\n",
        "print (\"The average number of words in a fake news article is \", words_per_headline_f)\n",
        "\n",
        "print (\"The standard deviation in real news article lengths is \", stdev_t_head)\n",
        "print (\"The standard deviation in fake news article lengths is \", stdev_f_head)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The average number of words in a real news article is  10.009017813513394\n",
            "The average number of words in a fake news article is  14.615743801652892\n",
            "The standard deviation in real news article lengths is  1.8442361540026926\n",
            "The standard deviation in fake news article lengths is  4.341147766763075\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQ5I_4X4-r1G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "c9aa088d-7ff3-4995-a4a7-19acef459ab8"
      },
      "source": [
        "%matplotlib inline\n",
        "fig, ax = plt.subplots(1, 2, figsize=(10, 6))\n",
        "sns.distplot(words_per_headline_plot_t, ax = ax[0], color = \"darkblue\", rug = True).set_title(\"Number of Words in Real News Headline\")\n",
        "sns.distplot(words_per_headline_plot_f, ax = ax[1], color = \"red\", rug = True).set_title(\"Number of Words in Fake News Headline\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of Words in Fake News Headline')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqPnpQi7_4OU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "headline_polarity_true = pd.DataFrame(columns=[\"headline\", \"sentiment\"])\n",
        "for headline in true[\"title\"]:\n",
        "    headline = TextBlob(headline)\n",
        "    headline_polarity_true = headline_polarity_true.append(pd.Series([headline,headline.sentiment.polarity], index=headline_polarity_true.columns), ignore_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFxcCxShD4KY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "headline_polarity_fake = pd.DataFrame(columns=[\"headline\", \"sentiment\"])\n",
        "for headline in fake[\"title\"]:\n",
        "    headline = TextBlob(headline)\n",
        "    headline_polarity_fake = headline_polarity_fake.append(pd.Series([headline,headline.sentiment.polarity], index=headline_polarity_fake.columns), ignore_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vF-bLeBnLB4J",
        "colab_type": "text"
      },
      "source": [
        "1 means positive, -1 means negative"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rcwd8LqIEn23",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "46893a8a-42d0-4d38-8f4b-b811da5d4bcb"
      },
      "source": [
        "#Most negative headlines\n",
        "a = str(headline_polarity_true.sort_values(by = [\"sentiment\"], ascending = True, ignore_index=True)[\"headline\"][0])\n",
        "b = str(headline_polarity_true.sort_values(by = [\"sentiment\"], ascending = True, ignore_index=True)[\"headline\"][1])\n",
        "c = str(headline_polarity_true.sort_values(by = [\"sentiment\"], ascending = True, ignore_index=True)[\"headline\"][2])\n",
        "\n",
        "fig = go.Figure(data=[go.Table(header=dict(values=['Headline']),\n",
        "                 cells=dict(values=[[a, b, c]]))])\n",
        "fig.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"d77accb5-e61d-41a7-ad94-ab4bdea04673\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"d77accb5-e61d-41a7-ad94-ab4bdea04673\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        'd77accb5-e61d-41a7-ad94-ab4bdea04673',\n",
              "                        [{\"cells\": {\"values\": [[\"Belgian mayor's throat slashed in cemetery, shocking country\", \"Caribbean islands fear grim tourist season in Irma's wake\", \"LinkedIn's Hoffman: Trump presidency would be 'terrifying' for policy\"]]}, \"header\": {\"values\": [\"Headline\"]}, \"type\": \"table\"}],\n",
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              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lp59Gd_sKMtk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "2eeec9c1-f73e-4770-c57a-895a80e1f384"
      },
      "source": [
        "#Most positive headlines\n",
        "a = str(headline_polarity_true.sort_values(by = [\"sentiment\"], ascending = False, ignore_index=True)[\"headline\"][0])\n",
        "b = str(headline_polarity_true.sort_values(by = [\"sentiment\"], ascending = False, ignore_index=True)[\"headline\"][1])\n",
        "c = str(headline_polarity_true.sort_values(by = [\"sentiment\"], ascending = False, ignore_index=True)[\"headline\"][2])\n",
        "\n",
        "fig = go.Figure(data=[go.Table(header=dict(values=['Headline']),\n",
        "                 cells=dict(values=[[a, b, c]]))])\n",
        "fig.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('01da371d-f271-42c1-9dab-7e848b30cb3c');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1AdYTK2ELtD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "6dfe1668-426d-429f-856e-a8b6bf129300"
      },
      "source": [
        "print(statistics.mean(headline_polarity_true[\"sentiment\"]))\n",
        "print(statistics.mean(headline_polarity_fake[\"sentiment\"]))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0261135291773924\n",
            "0.00342687144801556\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6BTjW_REEiP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "30657a57-4c85-46b7-98f0-7ff5e9bba6fe"
      },
      "source": [
        "%matplotlib inline\n",
        "fig, ax = plt.subplots(1, 2, figsize=(10, 6))\n",
        "sns.distplot(headline_polarity_true[\"sentiment\"], ax = ax[0], color = \"darkblue\", rug = True).set_title(\"Real News Headline Sentiments\")\n",
        "sns.distplot(headline_polarity_fake[\"sentiment\"], ax = ax[1], color = \"red\", rug = True).set_title(\"Fake News Headline Sentiments\")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Fake News Headline Sentiments')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHZWPDiLLh1h",
        "colab_type": "text"
      },
      "source": [
        "# Tokenizing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQ0GiTEN6NIF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "8729b9ca-694f-4125-95ac-3375233e7ff5"
      },
      "source": [
        "import string\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "  \n",
        "from nltk.corpus import stopwords\n",
        "stop_words = list(stopwords.words('english', 'arabic'))\n",
        "stop_words.extend([\"said\", \"reuters\", \"CNN\", \"would\", \"image\", \"via\"]) #extend stopwords to include these words too"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "azm-YMuOmir2",
        "colab_type": "text"
      },
      "source": [
        "**Cleaning Text**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Z2MIgE70jxC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from nltk.stem import WordNetLemmatizer \n",
        "  \n",
        "lemmatizer = WordNetLemmatizer()  \n",
        "\n",
        "words_in_t = [] #all tokens in true articles\n",
        "words_in_f = [] #all tokens in fake articles\n",
        "words_in_each_article_t = [] #list of true articles, each in tokenized form\n",
        "words_in_each_article_f = [] #list of fake articles, each in tokenized form\n",
        "\n",
        "def process(df, word_list, by_article_list):\n",
        "  for article in (df[\"text\"]):\n",
        "    words = word_tokenize(article)\n",
        "    words = [word.lower() for word in words if word.isalpha()] #lowercase\n",
        "    words = [word for word in words if word not in string.punctuation and word not in stop_words] #punctuation, stopwords\n",
        "    words = [lemmatizer.lemmatize(word) for word in words] #convert word to root form\n",
        "\n",
        "    by_article_list.append(words)\n",
        "    for w in words:\n",
        "      word_list.append(w)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpBNXYy7-Zz-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#NOTE: If rerunning, this cell will take a while\n",
        "process(fake, words_in_f, words_in_each_article_f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9VpJl5RxPHk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#NOTE: If rerunning, this cell will take a while\n",
        "process(true, words_in_t, words_in_each_article_t)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60p7YP_HmhXR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "outputId": "c4c54452-dde1-46f0-87b0-7219404e9047"
      },
      "source": [
        "from collections import Counter\n",
        "\n",
        "bigrams_true = zip(words_in_t, words_in_t[1:])\n",
        "bigram_counts_true = Counter(bigrams_true)\n",
        "bigrams_true = pd.DataFrame(bigram_counts_true.most_common(20), columns=[\"Bigram\", \"Count\"])\n",
        "bigrams_true"
      ],
      "execution_count": 25,
      "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>Bigram</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(united, state)</td>\n",
              "      <td>12269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(donald, trump)</td>\n",
              "      <td>10124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(white, house)</td>\n",
              "      <td>8462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(president, donald)</td>\n",
              "      <td>5886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(north, korea)</td>\n",
              "      <td>5632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(prime, minister)</td>\n",
              "      <td>4277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(new, york)</td>\n",
              "      <td>3945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(last, year)</td>\n",
              "      <td>3523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(last, week)</td>\n",
              "      <td>3412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(islamic, state)</td>\n",
              "      <td>3389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(barack, obama)</td>\n",
              "      <td>3352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>(told, reporter)</td>\n",
              "      <td>3208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(president, barack)</td>\n",
              "      <td>2908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>(hide, caption)</td>\n",
              "      <td>2684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(caption, photo)</td>\n",
              "      <td>2657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>(last, month)</td>\n",
              "      <td>2558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(hillary, clinton)</td>\n",
              "      <td>2482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>(trump, administration)</td>\n",
              "      <td>2475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(supreme, court)</td>\n",
              "      <td>2471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(secretary, state)</td>\n",
              "      <td>2306</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Bigram  Count\n",
              "0           (united, state)  12269\n",
              "1           (donald, trump)  10124\n",
              "2            (white, house)   8462\n",
              "3       (president, donald)   5886\n",
              "4            (north, korea)   5632\n",
              "5         (prime, minister)   4277\n",
              "6               (new, york)   3945\n",
              "7              (last, year)   3523\n",
              "8              (last, week)   3412\n",
              "9          (islamic, state)   3389\n",
              "10          (barack, obama)   3352\n",
              "11         (told, reporter)   3208\n",
              "12      (president, barack)   2908\n",
              "13          (hide, caption)   2684\n",
              "14         (caption, photo)   2657\n",
              "15            (last, month)   2558\n",
              "16       (hillary, clinton)   2482\n",
              "17  (trump, administration)   2475\n",
              "18         (supreme, court)   2471\n",
              "19       (secretary, state)   2306"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gugrLqp0prfj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "outputId": "63d69b35-a9ad-4574-9742-45e4f895bedb"
      },
      "source": [
        "bigrams_false = zip(words_in_f, words_in_f[1:])\n",
        "bigram_counts_false = Counter(bigrams_false)\n",
        "bigrams_false = pd.DataFrame(bigram_counts_false.most_common(20), columns=[\"Bigram\", \"Count\"])\n",
        "bigrams_false"
      ],
      "execution_count": 26,
      "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>Bigram</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(donald, trump)</td>\n",
              "      <td>15515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(hillary, clinton)</td>\n",
              "      <td>6726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(white, house)</td>\n",
              "      <td>6447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(united, state)</td>\n",
              "      <td>6299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(new, york)</td>\n",
              "      <td>4373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(president, trump)</td>\n",
              "      <td>3920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(president, obama)</td>\n",
              "      <td>3836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(fox, news)</td>\n",
              "      <td>3291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(barack, obama)</td>\n",
              "      <td>2254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(trump, supporter)</td>\n",
              "      <td>1909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(century, wire)</td>\n",
              "      <td>1902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>(supreme, court)</td>\n",
              "      <td>1781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(trump, campaign)</td>\n",
              "      <td>1724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>(trump, realdonaldtrump)</td>\n",
              "      <td>1704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(fake, news)</td>\n",
              "      <td>1695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>(social, medium)</td>\n",
              "      <td>1695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(obama, administration)</td>\n",
              "      <td>1682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>(police, officer)</td>\n",
              "      <td>1657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(national, security)</td>\n",
              "      <td>1632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(secretary, state)</td>\n",
              "      <td>1604</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Bigram  Count\n",
              "0            (donald, trump)  15515\n",
              "1         (hillary, clinton)   6726\n",
              "2             (white, house)   6447\n",
              "3            (united, state)   6299\n",
              "4                (new, york)   4373\n",
              "5         (president, trump)   3920\n",
              "6         (president, obama)   3836\n",
              "7                (fox, news)   3291\n",
              "8            (barack, obama)   2254\n",
              "9         (trump, supporter)   1909\n",
              "10           (century, wire)   1902\n",
              "11          (supreme, court)   1781\n",
              "12         (trump, campaign)   1724\n",
              "13  (trump, realdonaldtrump)   1704\n",
              "14              (fake, news)   1695\n",
              "15          (social, medium)   1695\n",
              "16   (obama, administration)   1682\n",
              "17         (police, officer)   1657\n",
              "18      (national, security)   1632\n",
              "19        (secretary, state)   1604"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VQWH6JMrRfy",
        "colab_type": "text"
      },
      "source": [
        "Trigrams"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MaPttYZCrStA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "outputId": "b509d380-cfc0-4d8f-95f3-bb7380b213ac"
      },
      "source": [
        "trigrams_true = zip(words_in_t, words_in_t[1:], words_in_t[2:])\n",
        "trigram_counts_true = Counter(trigrams_true)\n",
        "trigrams_true = pd.DataFrame(trigram_counts_true.most_common(20), columns=[\"Trigram\", \"Count\"])\n",
        "trigrams_true"
      ],
      "execution_count": 27,
      "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>Trigram</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(president, donald, trump)</td>\n",
              "      <td>5824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(president, barack, obama)</td>\n",
              "      <td>2907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(hide, caption, photo)</td>\n",
              "      <td>2656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(novel, coronavirus, outbreak)</td>\n",
              "      <td>1732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(photo, novel, coronavirus)</td>\n",
              "      <td>1729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(caption, photo, novel)</td>\n",
              "      <td>1723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(respond, request, comment)</td>\n",
              "      <td>858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(president, vladimir, putin)</td>\n",
              "      <td>796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(state, rex, tillerson)</td>\n",
              "      <td>673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(secretary, state, rex)</td>\n",
              "      <td>672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(national, security, adviser)</td>\n",
              "      <td>638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>(former, president, barack)</td>\n",
              "      <td>629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(russian, president, vladimir)</td>\n",
              "      <td>619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>(speaker, paul, ryan)</td>\n",
              "      <td>607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(minister, theresa, may)</td>\n",
              "      <td>587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>(prime, minister, theresa)</td>\n",
              "      <td>584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(chancellor, angela, merkel)</td>\n",
              "      <td>576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>(leader, mitch, mcconnell)</td>\n",
              "      <td>569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(new, york, time)</td>\n",
              "      <td>536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(republican, presidential, candidate)</td>\n",
              "      <td>536</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  Trigram  Count\n",
              "0              (president, donald, trump)   5824\n",
              "1              (president, barack, obama)   2907\n",
              "2                  (hide, caption, photo)   2656\n",
              "3          (novel, coronavirus, outbreak)   1732\n",
              "4             (photo, novel, coronavirus)   1729\n",
              "5                 (caption, photo, novel)   1723\n",
              "6             (respond, request, comment)    858\n",
              "7            (president, vladimir, putin)    796\n",
              "8                 (state, rex, tillerson)    673\n",
              "9                 (secretary, state, rex)    672\n",
              "10          (national, security, adviser)    638\n",
              "11            (former, president, barack)    629\n",
              "12         (russian, president, vladimir)    619\n",
              "13                  (speaker, paul, ryan)    607\n",
              "14               (minister, theresa, may)    587\n",
              "15             (prime, minister, theresa)    584\n",
              "16           (chancellor, angela, merkel)    576\n",
              "17             (leader, mitch, mcconnell)    569\n",
              "18                      (new, york, time)    536\n",
              "19  (republican, presidential, candidate)    536"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3X58k3lSrcKy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "outputId": "96edc4c9-f8ea-411a-dc76-ab381ce17bea"
      },
      "source": [
        "trigrams_false = zip(words_in_f, words_in_f[1:], words_in_f[2:])\n",
        "trigram_counts_false = Counter(trigrams_false)\n",
        "trigrams_false = pd.DataFrame(trigram_counts_false.most_common(20), columns=[\"Trigram\", \"Count\"])\n",
        "trigrams_false"
      ],
      "execution_count": 28,
      "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>Trigram</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(donald, trump, realdonaldtrump)</td>\n",
              "      <td>1694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(new, york, time)</td>\n",
              "      <td>1555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(black, life, matter)</td>\n",
              "      <td>1246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(president, donald, trump)</td>\n",
              "      <td>1148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(news, century, wire)</td>\n",
              "      <td>927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(president, united, state)</td>\n",
              "      <td>925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(president, barack, obama)</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(video, screen, capture)</td>\n",
              "      <td>729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(century, wire, say)</td>\n",
              "      <td>727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(new, york, city)</td>\n",
              "      <td>631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(image, donald, trump)</td>\n",
              "      <td>602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>(director, james, comey)</td>\n",
              "      <td>493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(subscribe, become, member)</td>\n",
              "      <td>475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>(fbi, director, james)</td>\n",
              "      <td>471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(make, america, great)</td>\n",
              "      <td>418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>(affordable, care, act)</td>\n",
              "      <td>395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(former, secretary, state)</td>\n",
              "      <td>393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>(white, house, press)</td>\n",
              "      <td>378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(member, century, wire)</td>\n",
              "      <td>353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(featured, video, screen)</td>\n",
              "      <td>341</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             Trigram  Count\n",
              "0   (donald, trump, realdonaldtrump)   1694\n",
              "1                  (new, york, time)   1555\n",
              "2              (black, life, matter)   1246\n",
              "3         (president, donald, trump)   1148\n",
              "4              (news, century, wire)    927\n",
              "5         (president, united, state)    925\n",
              "6         (president, barack, obama)    889\n",
              "7           (video, screen, capture)    729\n",
              "8               (century, wire, say)    727\n",
              "9                  (new, york, city)    631\n",
              "10            (image, donald, trump)    602\n",
              "11          (director, james, comey)    493\n",
              "12       (subscribe, become, member)    475\n",
              "13            (fbi, director, james)    471\n",
              "14            (make, america, great)    418\n",
              "15           (affordable, care, act)    395\n",
              "16        (former, secretary, state)    393\n",
              "17             (white, house, press)    378\n",
              "18           (member, century, wire)    353\n",
              "19         (featured, video, screen)    341"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ACVxS6cjsdvt",
        "colab_type": "text"
      },
      "source": [
        "Unigrams"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95nCkN3vyICA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wordcounts_t = Counter(words_in_t)\n",
        "wordcounts_f = Counter(words_in_f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Qkj0VtlgPZ9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mostcommon_t = Counter(wordcounts_t).most_common(20)\n",
        "mostcommon_f = Counter(wordcounts_f).most_common(20)\n",
        "\n",
        "t_plot = dict(mostcommon_t)\n",
        "mostcommon_t = pd.DataFrame(mostcommon_t, columns = [\"Word\", \"Count\"])\n",
        "mostcommon_t = mostcommon_t.reset_index(drop=True)\n",
        "mostcommon_t = mostcommon_t['Word'].tolist()\n",
        "\n",
        "f_plot = dict(mostcommon_f)\n",
        "mostcommon_f = pd.DataFrame(mostcommon_f, columns = [\"Word\", \"Count\"])\n",
        "mostcommon_f = mostcommon_f.reset_index(drop=True)\n",
        "mostcommon_f = mostcommon_f['Word'].tolist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouiRnFV2hJAf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "2ff1722a-ad78-4c02-8cc9-fa681cd09364"
      },
      "source": [
        "t_wc = WordCloud(max_words=25,relative_scaling=1,background_color ='white', normalize_plurals=False).generate_from_frequencies(t_plot)\n",
        "\n",
        "plt.imshow(t_wc)\n",
        "plt.title(\"Plot of Most Frequent Words in Real News\")\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHxWDIXLhr51",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "465f7009-f067-47f2-abb5-6a8d2701324a"
      },
      "source": [
        "f_wc = WordCloud(max_words=25,relative_scaling=1,background_color ='white', normalize_plurals=False).generate_from_frequencies(f_plot)\n",
        "\n",
        "plt.imshow(f_wc)\n",
        "plt.title(\"Plot of Most Frequent Words in Fake News\")\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chQMxa5sCmND",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b46b1d5a-2942-4e28-bb31-0a943a84e974"
      },
      "source": [
        "#Of the top 20 words in each class, 9 words are common \n",
        "len(set(mostcommon_t) & set(mostcommon_f))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fqSi7-_Z_arQ",
        "colab_type": "text"
      },
      "source": [
        "**Split into Train-Test Sets**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9-8Z451EmTR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Get tokenized forms of all articles (true and fake) in one dataframe\n",
        "X = []\n",
        "for a in words_in_each_article_t:\n",
        "  X.append(a)\n",
        "for b in words_in_each_article_f:\n",
        "  X.append(b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6A-SCvx0_fvu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Get labels of all articles (true and fake) in one dataframe\n",
        "y = pd.concat([true[\"label\"], fake[\"label\"]])\n",
        "y = [1 if x == \"fake\" else 0 for x in y] #1 and 0 for ease of tokenizing"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "68tVXhbs2vC_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "articles = pd.DataFrame(X)\n",
        "y = pd.DataFrame(y, columns=[\"label\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z1QGPOik2gXZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "articles.to_csv('articles.csv')\n",
        "!cp articles.csv \"/content/gdrive/My Drive/CIS545_2020/Project/\"\n",
        "\n",
        "y.to_csv('labels.csv')\n",
        "!cp labels.csv \"/content/gdrive/My Drive/CIS545_2020/Project/\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xe0cvmAQ_aMh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sklearn.model_selection as ms\n",
        "\n",
        "#Create 80-20 train test split\n",
        "X_train, X_test, y_train, y_test = ms.train_test_split(X, y, test_size = 0.2, random_state=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcWi3PJPc2de",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "#Keep words that appear in greater than 10% of documents. Override preprocessor and implement on list of words\n",
        "#in each article in order to keep the words we have cleaned for earlier\n",
        "tfidf = TfidfVectorizer(min_df = 0.1, preprocessor = ' '.join)\n",
        "\n",
        "#Implement on train dataset\n",
        "response_train = tfidf.fit_transform(X_train) #This is the Sparse Document-Term Matrix\n",
        "feature_names_train = tfidf.get_feature_names()\n",
        "dense_train = response_train.todense() #This is the Dense Document-Term Matrix\n",
        "denselist_train = dense_train.tolist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nce9RqnJjnl3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "2084d401-e211-453c-fe34-06a1bf99b8af"
      },
      "source": [
        "df_train = pd.DataFrame(denselist_train, columns=feature_names_train)\n",
        "df_train"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37362 rows × 215 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       according  act    action  added  ...     world      year  yet      york\n",
              "0       0.000000  0.0  0.000000    0.0  ...  0.000000  0.073312  0.0  0.000000\n",
              "1       0.000000  0.0  0.000000    0.0  ...  0.000000  0.000000  0.0  0.000000\n",
              "2       0.000000  0.0  0.000000    0.0  ...  0.000000  0.074320  0.0  0.000000\n",
              "3       0.000000  0.0  0.000000    0.0  ...  0.158714  0.000000  0.0  0.000000\n",
              "4       0.000000  0.0  0.000000    0.0  ...  0.000000  0.339018  0.0  0.000000\n",
              "...          ...  ...       ...    ...  ...       ...       ...  ...       ...\n",
              "37357   0.000000  0.0  0.000000    0.0  ...  0.078820  0.111762  0.0  0.000000\n",
              "37358   0.000000  0.0  0.147347    0.0  ...  0.000000  0.000000  0.0  0.000000\n",
              "37359   0.000000  0.0  0.000000    0.0  ...  0.000000  0.000000  0.0  0.000000\n",
              "37360   0.077642  0.0  0.092327    0.0  ...  0.000000  0.116044  0.0  0.284487\n",
              "37361   0.000000  0.0  0.000000    0.0  ...  0.084806  0.000000  0.0  0.000000\n",
              "\n",
              "[37362 rows x 215 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5E6Cm4DuDsX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "tfidf = TfidfVectorizer(preprocessor = ' '.join, vocabulary = feature_names_train)\n",
        "\n",
        "#Implement on test dataset\n",
        "response_test = tfidf.fit_transform(X_test) #This is the Sparse Document-Term Matrix\n",
        "feature_names_test = tfidf.get_feature_names()\n",
        "dense_test = response_test.todense() #This is the Dense Document-Term Matrix\n",
        "denselist_test = dense_test.tolist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLcpDYuqxYka",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "e1ff8cfe-9f0d-42e2-e9cc-225690b164f1"
      },
      "source": [
        "df_test = pd.DataFrame(denselist_test, columns=feature_names_test)\n",
        "df_test"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>according</th>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9341 rows × 215 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      according      act  action  ...      year       yet      york\n",
              "0      0.000000  0.00000     0.0  ...  0.000000  0.000000  0.000000\n",
              "1      0.000000  0.00000     0.0  ...  0.061231  0.000000  0.000000\n",
              "2      0.125399  0.00000     0.0  ...  0.000000  0.000000  0.000000\n",
              "3      0.000000  0.00000     0.0  ...  0.000000  0.000000  0.000000\n",
              "4      0.000000  0.00000     0.0  ...  0.000000  0.000000  0.000000\n",
              "...         ...      ...     ...  ...       ...       ...       ...\n",
              "9336   0.000000  0.00000     0.0  ...  0.000000  0.000000  0.000000\n",
              "9337   0.000000  0.00000     0.0  ...  0.000000  0.000000  0.115135\n",
              "9338   0.000000  0.00000     0.0  ...  0.000000  0.000000  0.000000\n",
              "9339   0.000000  0.08066     0.0  ...  0.048285  0.000000  0.000000\n",
              "9340   0.000000  0.00000     0.0  ...  0.000000  0.214845  0.000000\n",
              "\n",
              "[9341 rows x 215 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTj9O9ffOmNF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#This is the final data to be used for training models\n",
        "df_train.to_csv('training_data.csv')\n",
        "!cp training_data.csv \"/content/gdrive/My Drive/CIS545_2020/Project/\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rR0-RPKhO2cr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#This is the final data to be used for testing models\n",
        "df_test.to_csv('testing_data.csv')\n",
        "!cp testing_data.csv \"/content/gdrive/My Drive/CIS545_2020/Project\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOV1G3KbsMiU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train = pd.DataFrame(y_train, columns=[\"label\"])\n",
        "y_test = pd.DataFrame(y_test, columns=[\"label\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2z7uQk_qTQK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Final labels to be used for training models\n",
        "y_train.to_csv('train_labels.csv')\n",
        "!cp train_labels.csv \"/content/gdrive/My Drive/CIS545_2020/Project/\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CItRwEqqb1I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Final labels to be used for testing models\n",
        "y_test.to_csv('test_labels.csv')\n",
        "!cp test_labels.csv \"/content/gdrive/My Drive/CIS545_2020/Project/\""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}