Sentiment_analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "Sentiment_analysis.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/blondeincode/Trumps_tweet_sentiment/blob/main/Sentiment_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPYS1oQ0IlfK"
      },
      "source": [
        "Sentiment analysis and search for the most frequently used words in Donald Trump's tweets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RX-cp_LfmY46",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "413bba16-a447-4ad6-a95b-d50e4b43e539"
      },
      "source": [
        "!pip install -q langid\r\n",
        "!pip install -q emoji\r\n",
        "!pip install -q pyLDAvis"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.9MB 6.6MB/s \n",
            "\u001b[?25h  Building wheel for langid (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 133kB 5.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.7MB 6.6MB/s \n",
            "\u001b[?25h  Building wheel for pyLDAvis (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhZZTefgAxqm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ae5d106-6d67-4e4b-ca58-ca5ec11122f3"
      },
      "source": [
        "import os\n",
        "import urllib.request\n",
        "import zipfile\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import matplotlib.ticker as mtick\n",
        "plt.style.use('fast')\n",
        "import string\n",
        "import re\n",
        "import emoji\n",
        "import langid\n",
        "from textblob import TextBlob\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize \n",
        "from nltk.stem import WordNetLemmatizer\n",
        "nltk.download('punkt') #for tokenization\n",
        "nltk.download('wordnet') #for lemmatization\n",
        "nltk.download('stopwords') #to remove stopwords\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.decomposition import LatentDirichletAllocation\n",
        "import pyLDAvis\n",
        "import pyLDAvis.sklearn\n",
        "stop_words = set(stopwords.words('english'))"
      ],
      "execution_count": null,
      "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": "code",
      "metadata": {
        "id": "sCl4MENpqJ-Y"
      },
      "source": [
        "#Create a path to a data file\n",
        "DATA_DIR = os.path.join('data')\n",
        "DATA_URL = 'https://www.dropbox.com/s/qp4g1kvqwpggios/tweety_trump.zip?dl=1'\n",
        "DATA_FILENAME = 'data.zip'\n",
        "DATA_FILEPATH = 'os.path.join(DATA_DIR, DATA_FILENAME)'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uV5tvHmqVXB"
      },
      "source": [
        "def init_directories():\n",
        "  if not os.path.exists(DATA_DIR):\n",
        "    os.makedirs(DATA_DIR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-FIsZa1qX_W"
      },
      "source": [
        "data = urllib.request.urlretrieve(DATA_URL, DATA_FILEPATH)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMgIP0PbqbAi"
      },
      "source": [
        "init_directories()\n",
        "\n",
        "with zipfile.ZipFile(DATA_FILEPATH, 'r') as zip_ref:\n",
        "    zip_ref.extractall(DATA_DIR) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WzeUxr2Jqg6z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db9446cb-08ca-403b-de55-dea289c95f0f"
      },
      "source": [
        "for path, subdirs, files in os.walk('data'):\n",
        "    for name in files:\n",
        "        print(os.path.join(path, name))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data/tweety_trump.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0b89JjxionL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "b397c44b-d082-426d-e3f0-9cf1f221217f"
      },
      "source": [
        "#Open csv file with data\n",
        "tweets = pd.read_csv('data/tweety_trump.csv', usecols=['text','date'], header=0, sep=',', parse_dates=['date'])\n",
        "tweets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Republicans and Democrats have both created ou...</td>\n",
              "      <td>2011-08-02 18:07:48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I was thrilled to be back in the Great city of...</td>\n",
              "      <td>2020-03-03 01:34:50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @CBS_Herridge: READ: Letter to surveillance...</td>\n",
              "      <td>2020-01-17 03:22:47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The Unsolicited Mail In Ballot Scam is a major...</td>\n",
              "      <td>2020-09-12 20:10:58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @MZHemingway: Very friendly telling of even...</td>\n",
              "      <td>2020-01-17 13:13:59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56566</th>\n",
              "      <td>RT @RandPaul: I don’t know why @JoeBiden think...</td>\n",
              "      <td>2020-10-23 03:46:25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56567</th>\n",
              "      <td>RT @EliseStefanik: President @realDonaldTrump ...</td>\n",
              "      <td>2020-10-23 03:42:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56568</th>\n",
              "      <td>RT @TeamTrump: LIVE: Presidential Debate #Deba...</td>\n",
              "      <td>2020-10-23 01:03:58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56569</th>\n",
              "      <td>Just signed an order to support the workers of...</td>\n",
              "      <td>2020-10-22 21:04:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56570</th>\n",
              "      <td>Suburban women want Safety &amp;amp; Security. Joe...</td>\n",
              "      <td>2020-10-22 18:31:46</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>56571 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    text                date\n",
              "0      Republicans and Democrats have both created ou... 2011-08-02 18:07:48\n",
              "1      I was thrilled to be back in the Great city of... 2020-03-03 01:34:50\n",
              "2      RT @CBS_Herridge: READ: Letter to surveillance... 2020-01-17 03:22:47\n",
              "3      The Unsolicited Mail In Ballot Scam is a major... 2020-09-12 20:10:58\n",
              "4      RT @MZHemingway: Very friendly telling of even... 2020-01-17 13:13:59\n",
              "...                                                  ...                 ...\n",
              "56566  RT @RandPaul: I don’t know why @JoeBiden think... 2020-10-23 03:46:25\n",
              "56567  RT @EliseStefanik: President @realDonaldTrump ... 2020-10-23 03:42:05\n",
              "56568  RT @TeamTrump: LIVE: Presidential Debate #Deba... 2020-10-23 01:03:58\n",
              "56569  Just signed an order to support the workers of... 2020-10-22 21:04:21\n",
              "56570  Suburban women want Safety &amp; Security. Joe... 2020-10-22 18:31:46\n",
              "\n",
              "[56571 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVD5rkYQjnCX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "26c68096-d9f0-417c-bee2-60c792fd8595"
      },
      "source": [
        "#Filtering records by date - we choose the time period we are interested in\n",
        "tweets = tweets[(tweets['date'] >= '2017-01-20') & (tweets['date'] <= '2020-12-31' )]\n",
        "tweets = tweets.reset_index(drop=True)\n",
        "tweets.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I was thrilled to be back in the Great city of...</td>\n",
              "      <td>2020-03-03 01:34:50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RT @CBS_Herridge: READ: Letter to surveillance...</td>\n",
              "      <td>2020-01-17 03:22:47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Unsolicited Mail In Ballot Scam is a major...</td>\n",
              "      <td>2020-09-12 20:10:58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RT @MZHemingway: Very friendly telling of even...</td>\n",
              "      <td>2020-01-17 13:13:59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @WhiteHouse: President @realDonaldTrump ann...</td>\n",
              "      <td>2020-01-17 00:11:56</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text                date\n",
              "0  I was thrilled to be back in the Great city of... 2020-03-03 01:34:50\n",
              "1  RT @CBS_Herridge: READ: Letter to surveillance... 2020-01-17 03:22:47\n",
              "2  The Unsolicited Mail In Ballot Scam is a major... 2020-09-12 20:10:58\n",
              "3  RT @MZHemingway: Very friendly telling of even... 2020-01-17 13:13:59\n",
              "4  RT @WhiteHouse: President @realDonaldTrump ann... 2020-01-17 00:11:56"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOoykTkUkR7u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b9c0458-e128-47f7-be8e-4030d1f382f9"
      },
      "source": [
        "tweets.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 26081 entries, 0 to 26080\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype         \n",
            "---  ------  --------------  -----         \n",
            " 0   text    26081 non-null  object        \n",
            " 1   date    26081 non-null  datetime64[ns]\n",
            "dtypes: datetime64[ns](1), object(1)\n",
            "memory usage: 407.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUoOsoluGcKl"
      },
      "source": [
        "def findRetweeted(tweet):\r\n",
        "    '''The function will extract people's twitter logins from the retweet'''\r\n",
        "    return re.findall(r'(?<=RT\\s)(@[A-Za-z]+[A-Za-z0-9-_]+)', tweet)\r\n",
        "\r\n",
        "def findMentioned(tweet):\r\n",
        "    '''The function will extract twitter logins of people mentioned in the tweet'''\r\n",
        "    return re.findall(r'(?<!RT\\s)(@[A-Za-z]+[A-Za-z0-9-_]+)', tweet)  \r\n",
        "\r\n",
        "def findHashtags(tweet):\r\n",
        "    '''The function will extract the hashtags used in the tweet'''\r\n",
        "    return re.findall(r'(#[A-Za-z]+[A-Za-z0-9-_]+)', tweet)   "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnbgsTzyG5_z"
      },
      "source": [
        "tweets['retweeted'] = tweets['text'].apply(findRetweeted)\r\n",
        "tweets['mentioned'] = tweets['text'].apply(findMentioned)\r\n",
        "tweets['hashtags'] = tweets['text'].apply(findHashtags)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOVr2MBcKXEj"
      },
      "source": [
        "#take the rows from the hashtag column where the hashtags are located\r\n",
        "hashtags_list_df = tweets.loc[tweets.hashtags.apply(lambda hashtags_list: hashtags_list !=[]),['hashtags']]\r\n",
        "\r\n",
        "#create a dataframe where each hashtag usage gets its own line\r\n",
        "flattened_hashtags_df = pd.DataFrame(\r\n",
        "    [hashtag for hashtags_list in hashtags_list_df.hashtags\r\n",
        "    for hashtag in hashtags_list],\r\n",
        "    columns=['hashtag'])\r\n",
        "\r\n",
        "#the number of occurrences of each hashtag\r\n",
        "popular_hashtags = flattened_hashtags_df.groupby('hashtag').size()\\\r\n",
        "                                        .reset_index(name='counts')\\\r\n",
        "                                        .sort_values('counts', ascending=False)\\\r\n",
        "                                        .reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "s26x9syQPPk3",
        "outputId": "78a6dc88-09b9-4c4b-f0db-0d760df2ec66"
      },
      "source": [
        "popular_hashtags = popular_hashtags.head(10)\r\n",
        "popular_hashtags.set_index(['hashtag'], inplace = True)\r\n",
        "popular_hashtags"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>counts</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hashtag</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>#MAGA</th>\n",
              "      <td>436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>#KAG2020</th>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>#COVID19</th>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>#USMCA</th>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>#USA</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>#Dorian</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>#coronavirus</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>#AmericaFirst</th>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>#PaycheckProtectionProgram</th>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>#MakeAmericaGreatAgain</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            counts\n",
              "hashtag                           \n",
              "#MAGA                          436\n",
              "#KAG2020                        74\n",
              "#COVID19                        66\n",
              "#USMCA                          42\n",
              "#USA                            33\n",
              "#Dorian                         33\n",
              "#coronavirus                    28\n",
              "#AmericaFirst                   27\n",
              "#PaycheckProtectionProgram      24\n",
              "#MakeAmericaGreatAgain          23"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "5SIhC09A9DoE",
        "outputId": "46246ff6-e6d2-4391-875f-2ba4c92327c0"
      },
      "source": [
        "#Popular hashtags\r\n",
        "fig, ax = plt.subplots(1,1, figsize=(16, 7))\r\n",
        "plt.title('Popular hashtags')\r\n",
        "ax.bar(popular_hashtags.index, popular_hashtags['counts'], width=0.6,\r\n",
        "       edgecolor='darkgray', color=sns.color_palette(\"crest\", 10),\r\n",
        "       linewidth=0.7)\r\n",
        "for i in popular_hashtags.index:\r\n",
        "  ax.annotate(f\"{popular_hashtags['counts'][i]}\", xy=(i, popular_hashtags['counts'][i] + 10), va = 'center', ha='center', fontsize=11, fontweight='light', color='black')\r\n",
        "for s in ['top', 'left', 'right']:\r\n",
        "    ax.spines[s].set_visible(False)\r\n",
        "ax.set_xticklabels(popular_hashtags.index, rotation=45)\r\n",
        "ax.grid(axis='y', linestyle='-', alpha=0.4)    \r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4RxrENbQlmo"
      },
      "source": [
        "#get the rows from the retweeted column that contain the retweets\r\n",
        "retweeted_list_df = tweets.loc[tweets.retweeted.apply(lambda retweeted_list: retweeted_list !=[]),['retweeted']]\r\n",
        "\r\n",
        "#create a dataframe where each use of the retweet gets its own line\r\n",
        "flattened_retweeted_df = pd.DataFrame(\r\n",
        "    [retweeted for retweeted_list in retweeted_list_df.retweeted\r\n",
        "    for retweeted in retweeted_list],\r\n",
        "    columns=['retweeted'])\r\n",
        "\r\n",
        "#the number of times each retweet has occurred\r\n",
        "popular_retweeted = flattened_retweeted_df.groupby('retweeted').size()\\\r\n",
        "                                        .reset_index(name='counts')\\\r\n",
        "                                        .sort_values('counts', ascending=False)\\\r\n",
        "                                        .reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "XSWHqKBVQ9fB",
        "outputId": "7bfc94e2-3b2a-4f42-84cc-ab9702c2a239"
      },
      "source": [
        "popular_retweeted = popular_retweeted.head(10)\r\n",
        "popular_retweeted.set_index(['retweeted'], inplace = True)\r\n",
        "popular_retweeted"
      ],
      "execution_count": null,
      "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>counts</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>retweeted</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>@realDonaldTrump</th>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@WhiteHouse</th>\n",
              "      <td>665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@TeamTrump</th>\n",
              "      <td>269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@GOPChairwoman</th>\n",
              "      <td>213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@Jim_Jordan</th>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@DanScavino</th>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@GOP</th>\n",
              "      <td>133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@Scavino45</th>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@GreggJarrett</th>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@DonaldJTrumpJr</th>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  counts\n",
              "retweeted               \n",
              "@realDonaldTrump     985\n",
              "@WhiteHouse          665\n",
              "@TeamTrump           269\n",
              "@GOPChairwoman       213\n",
              "@Jim_Jordan          143\n",
              "@DanScavino          141\n",
              "@GOP                 133\n",
              "@Scavino45           114\n",
              "@GreggJarrett        111\n",
              "@DonaldJTrumpJr      100"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "NAcqRDYq9f8y",
        "outputId": "43a6d17e-6a67-41f4-b4b0-b8a6689dc4fe"
      },
      "source": [
        "#Popular retweets\r\n",
        "fig, ax = plt.subplots(1,1, figsize=(16, 7))\r\n",
        "plt.title('Popular retweets')\r\n",
        "ax.bar(popular_retweeted.index, popular_retweeted['counts'], width=0.6, \r\n",
        "       edgecolor='darkgray', color=sns.color_palette(\"crest\", 10),\r\n",
        "       linewidth=0.7)\r\n",
        "for i in popular_retweeted.index:\r\n",
        "  ax.annotate(f\"{popular_retweeted['counts'][i]}\", xy=(i, popular_retweeted['counts'][i] + 20), va = 'center', ha='center', fontsize=11, fontweight='light', color='black')\r\n",
        "for s in ['top', 'left', 'right']:\r\n",
        "    ax.spines[s].set_visible(False)\r\n",
        "ax.set_xticklabels(popular_retweeted.index, rotation=45)\r\n",
        "ax.grid(axis='y', linestyle='-', alpha=0.4)    \r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5NQrZXXRXzI"
      },
      "source": [
        "#get the rows from the 'mentioned' column containing the '@' calls\r\n",
        "mentioned_list_df = tweets.loc[tweets.mentioned.apply(lambda mentioned_list: mentioned_list !=[]),['mentioned']]\r\n",
        "\r\n",
        "#create a dataframe where each use of the '@' call gets its own line\r\n",
        "flattened_mentioned_df = pd.DataFrame(\r\n",
        "    [mentioned for mentioned_list in mentioned_list_df.mentioned\r\n",
        "    for mentioned in mentioned_list],\r\n",
        "    columns=['mentioned'])\r\n",
        "\r\n",
        "#number of occurrences of each '@' call\r\n",
        "popular_mentioned = flattened_mentioned_df.groupby('mentioned').size()\\\r\n",
        "                                        .reset_index(name='counts')\\\r\n",
        "                                        .sort_values('counts', ascending=False)\\\r\n",
        "                                        .reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "uYTuMXfKR3YZ",
        "outputId": "03f44921-8771-4097-b3c6-e47afae0206d"
      },
      "source": [
        "popular_mentioned = popular_mentioned.head(10)\r\n",
        "popular_mentioned.set_index(['mentioned'], inplace = True)\r\n",
        "popular_mentioned"
      ],
      "execution_count": null,
      "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>counts</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mentioned</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>@realDonaldTrump</th>\n",
              "      <td>1567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@FoxNews</th>\n",
              "      <td>495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@foxandfriends</th>\n",
              "      <td>303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@POTUS</th>\n",
              "      <td>189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@WhiteHouse</th>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@CNN</th>\n",
              "      <td>133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@nytimes</th>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@FLOTUS</th>\n",
              "      <td>105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@BreitbartNews</th>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>@OANN</th>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  counts\n",
              "mentioned               \n",
              "@realDonaldTrump    1567\n",
              "@FoxNews             495\n",
              "@foxandfriends       303\n",
              "@POTUS               189\n",
              "@WhiteHouse          174\n",
              "@CNN                 133\n",
              "@nytimes             114\n",
              "@FLOTUS              105\n",
              "@BreitbartNews       104\n",
              "@OANN                 93"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "sDxdEN3C98GU",
        "outputId": "d9f3e8be-6097-40e0-f0c7-ea10c1d9a7cb"
      },
      "source": [
        "#Popular mentioned\r\n",
        "fig, ax = plt.subplots(1,1, figsize=(16, 7))\r\n",
        "plt.title('Popular mentions')\r\n",
        "ax.bar(popular_mentioned.index, popular_mentioned['counts'], width=0.6, \r\n",
        "       edgecolor='darkgray', color=sns.color_palette(\"crest\", 10),\r\n",
        "       linewidth=0.7)\r\n",
        "for i in popular_mentioned.index:\r\n",
        "  ax.annotate(f\"{popular_mentioned['counts'][i]}\", xy=(i, popular_mentioned['counts'][i] + 30), va = 'center', ha='center', fontsize=11, fontweight='light', color='black')\r\n",
        "for s in ['top', 'left', 'right']:\r\n",
        "    ax.spines[s].set_visible(False)\r\n",
        "ax.set_xticklabels(popular_mentioned.index, rotation=45)\r\n",
        "ax.grid(axis='y', linestyle='-', alpha=0.4)    \r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEXZCDmuEw09"
      },
      "source": [
        "def cleanTxt(text):\n",
        "  text = re.sub(r'@[A-Za-z0-9]+', '', text) #removing the '@' call\n",
        "  text = re.sub(r'#', '', text) #remove '#' sign\n",
        "  text = re.sub(r'RT[\\s]+', '', text) #removed 'RT' retweet tag\n",
        "  text = re.sub(r'https?:\\/\\/\\S+', '', text) #removal of links\n",
        "  text = re.sub(r'[.,!?:;-=\\'\\\"$%&()*+/<>\\[\\\\\\]^_{|}~]', '', text) #remove punctuation marks\n",
        "  text = re.sub(r'[0-9]+', '', text) #delete digits\n",
        "  text = re.sub(r'\\s+', ' ', text) #removing multiple spaces\n",
        "\n",
        "  return text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEtVhqFjGm9N"
      },
      "source": [
        "#Clear text\n",
        "tweets['text'] = tweets['text'].apply(cleanTxt)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2avCwq2A5eq"
      },
      "source": [
        "#Clean up text from emoji\r\n",
        "def emojiFreeText(text):\r\n",
        "    allchars = [str for str in text]\r\n",
        "    emoji_list = [c for c in allchars if c in emoji.UNICODE_EMOJI]\r\n",
        "    clean_text = ' '.join([str for str in text.split() if not any(i in str for i in emoji_list)])\r\n",
        "    return clean_text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXFTfwydBtmT"
      },
      "source": [
        "tweets['text'] = tweets['text'].apply(emojiFreeText)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fssLkNZDzy5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "7f68d0a8-1cd8-463b-cf89-2e73404e12bd"
      },
      "source": [
        "#We exclude blank tweets after cleaning\r\n",
        "tweets = tweets[(tweets['text'] != '')]\r\n",
        "tweets = tweets.reset_index(drop=True)\r\n",
        "tweets"
      ],
      "execution_count": null,
      "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>text</th>\n",
              "      <th>date</th>\n",
              "      <th>retweeted</th>\n",
              "      <th>mentioned</th>\n",
              "      <th>hashtags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I was thrilled to be back in the Great city of...</td>\n",
              "      <td>2020-03-03 01:34:50</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[#KAG2020]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Herridge READ Letter to surveillance court obt...</td>\n",
              "      <td>2020-01-17 03:22:47</td>\n",
              "      <td>[@CBS_Herridge]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Unsolicited Mail In Ballot Scam is a major...</td>\n",
              "      <td>2020-09-12 20:10:58</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Very friendly telling of events here about Com...</td>\n",
              "      <td>2020-01-17 13:13:59</td>\n",
              "      <td>[@MZHemingway]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>President announced historic steps to protect ...</td>\n",
              "      <td>2020-01-17 00:11:56</td>\n",
              "      <td>[@WhiteHouse]</td>\n",
              "      <td>[@realDonaldTrump]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24541</th>\n",
              "      <td>I don’t know why thinks he can continue to lie...</td>\n",
              "      <td>2020-10-23 03:46:25</td>\n",
              "      <td>[@RandPaul]</td>\n",
              "      <td>[@JoeBiden]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24542</th>\n",
              "      <td>President excels at communicating directly to ...</td>\n",
              "      <td>2020-10-23 03:42:05</td>\n",
              "      <td>[@EliseStefanik]</td>\n",
              "      <td>[@realDonaldTrump]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24543</th>\n",
              "      <td>LIVE Presidential Debate Debates Text VOTE to</td>\n",
              "      <td>2020-10-23 01:03:58</td>\n",
              "      <td>[@TeamTrump]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[#Debates2020]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24544</th>\n",
              "      <td>Just signed an order to support the workers of...</td>\n",
              "      <td>2020-10-22 21:04:21</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24545</th>\n",
              "      <td>Suburban women want Safety amp Security Joe Bi...</td>\n",
              "      <td>2020-10-22 18:31:46</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24546 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    text  ...        hashtags\n",
              "0      I was thrilled to be back in the Great city of...  ...      [#KAG2020]\n",
              "1      Herridge READ Letter to surveillance court obt...  ...              []\n",
              "2      The Unsolicited Mail In Ballot Scam is a major...  ...              []\n",
              "3      Very friendly telling of events here about Com...  ...              []\n",
              "4      President announced historic steps to protect ...  ...              []\n",
              "...                                                  ...  ...             ...\n",
              "24541  I don’t know why thinks he can continue to lie...  ...              []\n",
              "24542  President excels at communicating directly to ...  ...              []\n",
              "24543      LIVE Presidential Debate Debates Text VOTE to  ...  [#Debates2020]\n",
              "24544  Just signed an order to support the workers of...  ...              []\n",
              "24545  Suburban women want Safety amp Security Joe Bi...  ...              []\n",
              "\n",
              "[24546 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "meQUMpuNlChE",
        "outputId": "c8f05ad7-0090-43ed-f7e4-d6151b3b5420"
      },
      "source": [
        "#Identification of languages\n",
        "predicted_languages = [langid.classify(tweet) for tweet in tweets['text']]\n",
        "\n",
        "lang_df = pd.DataFrame(predicted_languages, columns=['language','value'])\n",
        "\n",
        "#Show the top 10 languages & the number of tweets in these languages\n",
        "print(lang_df['language'].value_counts().head(10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "en    23809\n",
            "zh      124\n",
            "fr       67\n",
            "de       64\n",
            "es       57\n",
            "la       52\n",
            "pl       29\n",
            "mt       29\n",
            "nl       27\n",
            "it       27\n",
            "Name: language, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2XHsLmx1Qif"
      },
      "source": [
        "#We add the column 'language' to the previous table\n",
        "tweets = pd.concat([tweets, lang_df['language']], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "sjqvjk9D4Pyg",
        "outputId": "20775029-71b2-45da-de64-2cc8ebb75e34"
      },
      "source": [
        "#We choose tweets written in English\n",
        "tweets = tweets[(tweets['language'] == 'en')]\n",
        "tweets = tweets.reset_index(drop=True)\n",
        "tweets.head()"
      ],
      "execution_count": null,
      "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>text</th>\n",
              "      <th>date</th>\n",
              "      <th>retweeted</th>\n",
              "      <th>mentioned</th>\n",
              "      <th>hashtags</th>\n",
              "      <th>language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I was thrilled to be back in the Great city of...</td>\n",
              "      <td>2020-03-03 01:34:50</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[#KAG2020]</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Herridge READ Letter to surveillance court obt...</td>\n",
              "      <td>2020-01-17 03:22:47</td>\n",
              "      <td>[@CBS_Herridge]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Unsolicited Mail In Ballot Scam is a major...</td>\n",
              "      <td>2020-09-12 20:10:58</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Very friendly telling of events here about Com...</td>\n",
              "      <td>2020-01-17 13:13:59</td>\n",
              "      <td>[@MZHemingway]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>President announced historic steps to protect ...</td>\n",
              "      <td>2020-01-17 00:11:56</td>\n",
              "      <td>[@WhiteHouse]</td>\n",
              "      <td>[@realDonaldTrump]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text  ... language\n",
              "0  I was thrilled to be back in the Great city of...  ...       en\n",
              "1  Herridge READ Letter to surveillance court obt...  ...       en\n",
              "2  The Unsolicited Mail In Ballot Scam is a major...  ...       en\n",
              "3  Very friendly telling of events here about Com...  ...       en\n",
              "4  President announced historic steps to protect ...  ...       en\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8cpwAhQ2sMn"
      },
      "source": [
        "#tokenization, lemmatization, stopwords removal\r\n",
        "def clean_tweet(tweet):\r\n",
        "    tweet = tweet.lower() # lower case\r\n",
        "    tweet_tokens = word_tokenize(tweet) #tokenization\r\n",
        "    filtered_words = [w for w in tweet_tokens if not w in stop_words] #stopwords removal\r\n",
        "\r\n",
        "    lemmatizer = WordNetLemmatizer()\r\n",
        "    lemma_words = [lemmatizer.lemmatize(w, pos='a') for w in filtered_words] #lemmatization\r\n",
        "\r\n",
        "    return ' '.join(filtered_words)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vX-zVWgqKbHX"
      },
      "source": [
        "tweets['clean_tweet'] = tweets.text.apply(clean_tweet)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_Rup8CbPc0_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "02615939-b0c8-4a77-b0c8-ad8cacb56ca8"
      },
      "source": [
        "column_names = ['text','clean_tweet','date','retweeted','mentioned','hashtags','language']\r\n",
        "tweets = tweets.reindex(columns=column_names)\r\n",
        "tweets.head()"
      ],
      "execution_count": null,
      "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>text</th>\n",
              "      <th>clean_tweet</th>\n",
              "      <th>date</th>\n",
              "      <th>retweeted</th>\n",
              "      <th>mentioned</th>\n",
              "      <th>hashtags</th>\n",
              "      <th>language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I was thrilled to be back in the Great city of...</td>\n",
              "      <td>thrilled back great city charlotte north carol...</td>\n",
              "      <td>2020-03-03 01:34:50</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[#KAG2020]</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Herridge READ Letter to surveillance court obt...</td>\n",
              "      <td>herridge read letter surveillance court obtain...</td>\n",
              "      <td>2020-01-17 03:22:47</td>\n",
              "      <td>[@CBS_Herridge]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Unsolicited Mail In Ballot Scam is a major...</td>\n",
              "      <td>unsolicited mail ballot scam major threat demo...</td>\n",
              "      <td>2020-09-12 20:10:58</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Very friendly telling of events here about Com...</td>\n",
              "      <td>friendly telling events comeys apparent leakin...</td>\n",
              "      <td>2020-01-17 13:13:59</td>\n",
              "      <td>[@MZHemingway]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>President announced historic steps to protect ...</td>\n",
              "      <td>president announced historic steps protect con...</td>\n",
              "      <td>2020-01-17 00:11:56</td>\n",
              "      <td>[@WhiteHouse]</td>\n",
              "      <td>[@realDonaldTrump]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text  ... language\n",
              "0  I was thrilled to be back in the Great city of...  ...       en\n",
              "1  Herridge READ Letter to surveillance court obt...  ...       en\n",
              "2  The Unsolicited Mail In Ballot Scam is a major...  ...       en\n",
              "3  Very friendly telling of events here about Com...  ...       en\n",
              "4  President announced historic steps to protect ...  ...       en\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEQPUjHN_CBw"
      },
      "source": [
        "Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5k4iPsp-CPz"
      },
      "source": [
        "#subjectivity\n",
        "def getSubjectivity(text):\n",
        "  return TextBlob(text).sentiment.subjectivity"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZbQN7nyC-EPY"
      },
      "source": [
        "#polarity\n",
        "def getPolarity(text):\n",
        "  return TextBlob(text).sentiment.polarity"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VTsAMYZAbfq"
      },
      "source": [
        "#we are adding two new columns\n",
        "tweets['subjectivity'] = tweets['clean_tweet'].apply(getSubjectivity)\n",
        "tweets['polarity'] = tweets['clean_tweet'].apply(getPolarity)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JtOilXQ0CnQa"
      },
      "source": [
        "#Function to determine if tweet is positive, neutral or negative.\n",
        "def getAnalysis(score):\n",
        "  if score < 0:\n",
        "    return \"Negative\"\n",
        "  elif score == 0:\n",
        "    return \"Neutral\"\n",
        "  else:\n",
        "    return \"Positive\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "61TjdX0DDGcW",
        "outputId": "68814611-49a2-45fe-a070-87b2544d20c7"
      },
      "source": [
        "tweets['sentiment'] = tweets['polarity'].apply(getAnalysis)\n",
        "tweets.head()"
      ],
      "execution_count": null,
      "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>text</th>\n",
              "      <th>clean_tweet</th>\n",
              "      <th>date</th>\n",
              "      <th>retweeted</th>\n",
              "      <th>mentioned</th>\n",
              "      <th>hashtags</th>\n",
              "      <th>language</th>\n",
              "      <th>subjectivity</th>\n",
              "      <th>polarity</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I was thrilled to be back in the Great city of...</td>\n",
              "      <td>thrilled back great city charlotte north carol...</td>\n",
              "      <td>2020-03-03 01:34:50</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[#KAG2020]</td>\n",
              "      <td>en</td>\n",
              "      <td>0.483333</td>\n",
              "      <td>0.450000</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Herridge READ Letter to surveillance court obt...</td>\n",
              "      <td>herridge read letter surveillance court obtain...</td>\n",
              "      <td>2020-01-17 03:22:47</td>\n",
              "      <td>[@CBS_Herridge]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Unsolicited Mail In Ballot Scam is a major...</td>\n",
              "      <td>unsolicited mail ballot scam major threat demo...</td>\n",
              "      <td>2020-09-12 20:10:58</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "      <td>0.454762</td>\n",
              "      <td>0.029464</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Very friendly telling of events here about Com...</td>\n",
              "      <td>friendly telling events comeys apparent leakin...</td>\n",
              "      <td>2020-01-17 13:13:59</td>\n",
              "      <td>[@MZHemingway]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "      <td>0.425000</td>\n",
              "      <td>0.212500</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>President announced historic steps to protect ...</td>\n",
              "      <td>president announced historic steps protect con...</td>\n",
              "      <td>2020-01-17 00:11:56</td>\n",
              "      <td>[@WhiteHouse]</td>\n",
              "      <td>[@realDonaldTrump]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "      <td>0.200794</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text  ... sentiment\n",
              "0  I was thrilled to be back in the Great city of...  ...  Positive\n",
              "1  Herridge READ Letter to surveillance court obt...  ...  Positive\n",
              "2  The Unsolicited Mail In Ballot Scam is a major...  ...  Positive\n",
              "3  Very friendly telling of events here about Com...  ...  Positive\n",
              "4  President announced historic steps to protect ...  ...  Positive\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaWnvFAoSUlB"
      },
      "source": [
        "column_names = ['text','clean_tweet','date','subjectivity','polarity','sentiment','retweeted','mentioned','hashtags','language']\r\n",
        "tweets = tweets.reindex(columns=column_names)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "mfuveDKIa-P2",
        "outputId": "b8fca273-3119-468f-e85f-b0e064b4bb07"
      },
      "source": [
        "tweets['month'] = (tweets['date']).dt.date\n",
        "tweets['month'] = pd.to_datetime(tweets['month'], format='%Y-%m-%d')\n",
        "tweets.head()"
      ],
      "execution_count": null,
      "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>text</th>\n",
              "      <th>clean_tweet</th>\n",
              "      <th>date</th>\n",
              "      <th>subjectivity</th>\n",
              "      <th>polarity</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>retweeted</th>\n",
              "      <th>mentioned</th>\n",
              "      <th>hashtags</th>\n",
              "      <th>language</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I was thrilled to be back in the Great city of...</td>\n",
              "      <td>thrilled back great city charlotte north carol...</td>\n",
              "      <td>2020-03-03 01:34:50</td>\n",
              "      <td>0.483333</td>\n",
              "      <td>0.450000</td>\n",
              "      <td>Positive</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[#KAG2020]</td>\n",
              "      <td>en</td>\n",
              "      <td>2020-03-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Herridge READ Letter to surveillance court obt...</td>\n",
              "      <td>herridge read letter surveillance court obtain...</td>\n",
              "      <td>2020-01-17 03:22:47</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>Positive</td>\n",
              "      <td>[@CBS_Herridge]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "      <td>2020-01-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Unsolicited Mail In Ballot Scam is a major...</td>\n",
              "      <td>unsolicited mail ballot scam major threat demo...</td>\n",
              "      <td>2020-09-12 20:10:58</td>\n",
              "      <td>0.454762</td>\n",
              "      <td>0.029464</td>\n",
              "      <td>Positive</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "      <td>2020-09-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Very friendly telling of events here about Com...</td>\n",
              "      <td>friendly telling events comeys apparent leakin...</td>\n",
              "      <td>2020-01-17 13:13:59</td>\n",
              "      <td>0.425000</td>\n",
              "      <td>0.212500</td>\n",
              "      <td>Positive</td>\n",
              "      <td>[@MZHemingway]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "      <td>2020-01-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>President announced historic steps to protect ...</td>\n",
              "      <td>president announced historic steps protect con...</td>\n",
              "      <td>2020-01-17 00:11:56</td>\n",
              "      <td>0.200794</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>Positive</td>\n",
              "      <td>[@WhiteHouse]</td>\n",
              "      <td>[@realDonaldTrump]</td>\n",
              "      <td>[]</td>\n",
              "      <td>en</td>\n",
              "      <td>2020-01-17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text  ...      month\n",
              "0  I was thrilled to be back in the Great city of...  ... 2020-03-03\n",
              "1  Herridge READ Letter to surveillance court obt...  ... 2020-01-17\n",
              "2  The Unsolicited Mail In Ballot Scam is a major...  ... 2020-09-12\n",
              "3  Very friendly telling of events here about Com...  ... 2020-01-17\n",
              "4  President announced historic steps to protect ...  ... 2020-01-17\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cbYhKl9Flr9C",
        "outputId": "440725f3-012b-451e-9175-73493915b502"
      },
      "source": [
        "print('Number of positive tweets:',len(tweets[(tweets['sentiment'] == 'Positive')]),'\\n')\n",
        "print('Number of neutral tweets:',len(tweets[(tweets['sentiment'] == 'Neutral')]),'\\n')\n",
        "print('Number of negative tweets:',len(tweets[(tweets['sentiment'] == 'Negative')]),'\\n')\n",
        "print('The sum of all tweets:',len(tweets[(tweets['sentiment'] == 'Positive')])+len(tweets[(tweets['sentiment'] == 'Neutral')])+len(tweets[(tweets['sentiment'] == 'Negative')]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of positive tweets: 11996 \n",
            "\n",
            "Number of neutral tweets: 6674 \n",
            "\n",
            "Number of negative tweets: 5139 \n",
            "\n",
            "The sum of all tweets: 23809\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "I9jC4FFyHa26",
        "outputId": "876a1ed6-2aa3-4a28-ac13-efd70d772c3e"
      },
      "source": [
        "viz = tweets.groupby(tweets['month'].dt.year)[['text']].count()\r\n",
        "viz"
      ],
      "execution_count": null,
      "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>text</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>2386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>3432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>7286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>10705</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        text\n",
              "month       \n",
              "2017    2386\n",
              "2018    3432\n",
              "2019    7286\n",
              "2020   10705"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "yCxrlLC05a2k",
        "outputId": "b1342f26-c1ee-492c-e6ca-f5f01dddf709"
      },
      "source": [
        "#Visualization of the number of tweets in individual years\r\n",
        "fig, ax = plt.subplots(figsize=(16,7))\r\n",
        "viz.plot(ax=ax, color=['dodgerblue'], legend=False)\r\n",
        "plt.title('Amount of tweets in individual years')\r\n",
        "plt.style.use('fast')\r\n",
        "ax.set_xlabel('Year')\r\n",
        "ax.set_ylabel('Amount of tweets') \r\n",
        "ax.annotate('2417', (2017, 2417+200), ha='center')\r\n",
        "ax.annotate('3446', (2018, 3446+300), ha='center')\r\n",
        "ax.annotate('7333', (2019, 7333+300), ha='center')\r\n",
        "ax.annotate('10778', (2020, 10778+200), ha='center')\r\n",
        "for s in ['top', 'left', 'right']:\r\n",
        "    ax.spines[s].set_visible(False)\r\n",
        "ax.set_xticklabels(['','2017','','2018','','2019','','2020'])\r\n",
        "ax.grid(axis='y', linestyle='-', alpha=0.4)    \r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "doYNpOaIuBQx"
      },
      "source": [
        "Tweets split by years:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J26ypHL4uAOW"
      },
      "source": [
        "year2017 = tweets[(tweets['date'] >= '2017-01-20') & (tweets['date'] <= '2017-12-31' )]\n",
        "year2017 = year2017.reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPVPctB-uN5K"
      },
      "source": [
        "year2018 = tweets[(tweets['date'] >= '2018-01-01') & (tweets['date'] <= '2018-12-31' )]\n",
        "year2018 = year2018.reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55rXQBibu04a"
      },
      "source": [
        "year2019 = tweets[(tweets['date'] >= '2019-01-01') & (tweets['date'] <= '2019-12-31' )]\n",
        "year2019 = year2019.reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzaYbemouPYx"
      },
      "source": [
        "year2020 = tweets[(tweets['date'] >= '2020-01-01') & (tweets['date'] <= '2020-12-31' )]\n",
        "year2020 = year2020.reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VdPovvHHV-d"
      },
      "source": [
        "**Year 2017**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xYJqggVFxikZ",
        "outputId": "53d9dd1f-ca79-40b3-8727-23427068da21"
      },
      "source": [
        "group2017 = year2017.groupby([year2017['month'].dt.month, 'sentiment'])[['sentiment']].count()\n",
        "group2017.columns = ['count']\n",
        "group2017"
      ],
      "execution_count": null,
      "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></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <th>sentiment</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
              "      <th>Negative</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
              "      <th>Negative</th>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
              "      <th>Negative</th>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
              "      <th>Negative</th>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">5</th>\n",
              "      <th>Negative</th>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
              "      <th>Negative</th>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">7</th>\n",
              "      <th>Negative</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
              "      <th>Negative</th>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">9</th>\n",
              "      <th>Negative</th>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
              "      <th>Negative</th>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">11</th>\n",
              "      <th>Negative</th>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
              "      <th>Negative</th>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 count\n",
              "month sentiment       \n",
              "1     Negative      18\n",
              "      Neutral       22\n",
              "      Positive      32\n",
              "2     Negative      45\n",
              "      Neutral       28\n",
              "      Positive      80\n",
              "3     Negative      27\n",
              "      Neutral       41\n",
              "      Positive      73\n",
              "4     Negative      31\n",
              "      Neutral       36\n",
              "      Positive      80\n",
              "5     Negative      34\n",
              "      Neutral       46\n",
              "      Positive      70\n",
              "6     Negative      41\n",
              "      Neutral       61\n",
              "      Positive     103\n",
              "7     Negative      48\n",
              "      Neutral       60\n",
              "      Positive     127\n",
              "8     Negative      53\n",
              "      Neutral       64\n",
              "      Positive     143\n",
              "9     Negative      43\n",
              "      Neutral       76\n",
              "      Positive     173\n",
              "10    Negative      58\n",
              "      Neutral       75\n",
              "      Positive     150\n",
              "11    Negative      56\n",
              "      Neutral       48\n",
              "      Positive     148\n",
              "12    Negative      43\n",
              "      Neutral       36\n",
              "      Positive     108"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "-x5n3bKK6EDb",
        "outputId": "145139ce-c0e7-46fb-e9ba-1fcff1721959"
      },
      "source": [
        "#Stacked column chart\r\n",
        "fig, ax = plt.subplots(figsize=(16,7))\r\n",
        "group2017.groupby(level=0).apply(lambda x: 100 * x / x.sum()).unstack().plot(kind='bar',stacked=True, color=['orangered','darkgray','limegreen'], width=0.6, ax=ax)\r\n",
        "plt.title('Summary of tweets in 2017')\r\n",
        "ax.set_xlabel('Month')\r\n",
        "ax.set_ylabel('Amount of tweets')\r\n",
        "plt.xticks(rotation=45)\r\n",
        "for s in ['top', 'right']:\r\n",
        "    ax.spines[s].set_visible(False)\r\n",
        "#Add this loop to add the annotations\r\n",
        "for p in ax.patches:\r\n",
        "    width = p.get_width()\r\n",
        "    height = p.get_height()\r\n",
        "    x, y = p.get_xy() \r\n",
        "    ax.annotate(f'{height:.3}%', (x + width/2, y + height*0.5), ha='center', fontsize=12, fontweight='light')\r\n",
        "ax.set_xticklabels(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']) \r\n",
        "plt.legend(['Negative','Neutral','Positive'], bbox_to_anchor=(1.015, 0.95), loc='upper left', borderaxespad=0., frameon=False)\r\n",
        "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YyIhMzMCNeKZ"
      },
      "source": [
        "**Year 2018**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7_qJpZ-Mvxx4",
        "outputId": "4dc14f26-adb3-4dfc-a1b6-beaa29518a3a"
      },
      "source": [
        "group2018 = year2018.groupby([year2018['month'].dt.month, 'sentiment'])[['sentiment']].count()\n",
        "group2018.columns = ['count']\n",
        "group2018"
      ],
      "execution_count": null,
      "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></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <th>sentiment</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
              "      <th>Negative</th>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
              "      <th>Negative</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
              "      <th>Negative</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
              "      <th>Negative</th>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">5</th>\n",
              "      <th>Negative</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
              "      <th>Negative</th>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">7</th>\n",
              "      <th>Negative</th>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
              "      <th>Negative</th>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">9</th>\n",
              "      <th>Negative</th>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
              "      <th>Negative</th>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">11</th>\n",
              "      <th>Negative</th>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
              "      <th>Negative</th>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>176</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 count\n",
              "month sentiment       \n",
              "1     Negative      54\n",
              "      Neutral       16\n",
              "      Positive     133\n",
              "2     Negative      40\n",
              "      Neutral       27\n",
              "      Positive     104\n",
              "3     Negative      30\n",
              "      Neutral       33\n",
              "      Positive     106\n",
              "4     Negative      67\n",
              "      Neutral       28\n",
              "      Positive     156\n",
              "5     Negative      68\n",
              "      Neutral       35\n",
              "      Positive     153\n",
              "6     Negative      91\n",
              "      Neutral       69\n",
              "      Positive     216\n",
              "7     Negative      73\n",
              "      Neutral       69\n",
              "      Positive     188\n",
              "8     Negative     101\n",
              "      Neutral       51\n",
              "      Positive     205\n",
              "9     Negative      62\n",
              "      Neutral       83\n",
              "      Positive     210\n",
              "10    Negative      71\n",
              "      Neutral       75\n",
              "      Positive     197\n",
              "11    Negative      61\n",
              "      Neutral       57\n",
              "      Positive     214\n",
              "12    Negative      63\n",
              "      Neutral       39\n",
              "      Positive     176"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "qRBni5YC7Nxo",
        "outputId": "039ef2dc-1a11-452c-e72c-c2a022469a52"
      },
      "source": [
        "#Stacked column chart\r\n",
        "fig, ax = plt.subplots(figsize=(16,7))\r\n",
        "group2018.groupby(level=0).apply(lambda x: 100 * x / x.sum()).unstack().plot(kind='bar',stacked=True, color=['orangered','darkgray','limegreen'], width=0.6, ax=ax)\r\n",
        "plt.title('Summary of tweets in 2018')\r\n",
        "ax.set_xlabel('Month')\r\n",
        "ax.set_ylabel('Amount of tweets')\r\n",
        "plt.xticks(rotation=45)\r\n",
        "for s in ['top', 'right']:\r\n",
        "    ax.spines[s].set_visible(False)\r\n",
        "#Add this loop to add the annotations\r\n",
        "for p in ax.patches:\r\n",
        "    width = p.get_width()\r\n",
        "    height = p.get_height()\r\n",
        "    x, y = p.get_xy() \r\n",
        "    ax.annotate(f'{height:.3}%', (x + width/2, y + height*0.5), ha='center', fontsize=12, fontweight='light')\r\n",
        "ax.set_xticklabels(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']) \r\n",
        "plt.legend(['Negative','Neutral','Positive'], bbox_to_anchor=(1.015, 0.95), loc='upper left', borderaxespad=0., frameon=False)\r\n",
        "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cPq1iRLTN_yz"
      },
      "source": [
        "**Year 2019**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "77dp1_wWv6n4",
        "outputId": "9c51523d-b323-41d7-ce62-585042f1812b"
      },
      "source": [
        "group2019 = year2019.groupby([year2019['month'].dt.month, 'sentiment'])[['sentiment']].count()\n",
        "group2019.columns = ['count']\n",
        "group2019"
      ],
      "execution_count": null,
      "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></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <th>sentiment</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
              "      <th>Negative</th>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
              "      <th>Negative</th>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
              "      <th>Negative</th>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
              "      <th>Negative</th>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">5</th>\n",
              "      <th>Negative</th>\n",
              "      <td>146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
              "      <th>Negative</th>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">7</th>\n",
              "      <th>Negative</th>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
              "      <th>Negative</th>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">9</th>\n",
              "      <th>Negative</th>\n",
              "      <td>173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
              "      <th>Negative</th>\n",
              "      <td>219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">11</th>\n",
              "      <th>Negative</th>\n",
              "      <td>179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
              "      <th>Negative</th>\n",
              "      <td>234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>424</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 count\n",
              "month sentiment       \n",
              "1     Negative      76\n",
              "      Neutral       71\n",
              "      Positive     207\n",
              "2     Negative      53\n",
              "      Neutral       63\n",
              "      Positive     116\n",
              "3     Negative      94\n",
              "      Neutral       86\n",
              "      Positive     185\n",
              "4     Negative     106\n",
              "      Neutral       98\n",
              "      Positive     212\n",
              "5     Negative     146\n",
              "      Neutral      185\n",
              "      Positive     333\n",
              "6     Negative      96\n",
              "      Neutral       86\n",
              "      Positive     267\n",
              "7     Negative     136\n",
              "      Neutral      136\n",
              "      Positive     375\n",
              "8     Negative     150\n",
              "      Neutral      143\n",
              "      Positive     364\n",
              "9     Negative     173\n",
              "      Neutral      213\n",
              "      Positive     339\n",
              "10    Negative     219\n",
              "      Neutral      304\n",
              "      Positive     412\n",
              "11    Negative     179\n",
              "      Neutral      279\n",
              "      Positive     329\n",
              "12    Negative     234\n",
              "      Neutral      363\n",
              "      Positive     424"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "vpFDO3m97V-C",
        "outputId": "6a0b9b63-c3d3-48cd-9f48-0f559bf8b226"
      },
      "source": [
        "#Stacked column chart\r\n",
        "fig, ax = plt.subplots(figsize=(16,7))\r\n",
        "group2019.groupby(level=0).apply(lambda x: 100 * x / x.sum()).unstack().plot(kind='bar',stacked=True, color=['orangered','darkgray','limegreen'], width=0.6, ax=ax)\r\n",
        "plt.title('Summary of tweets in 2019')\r\n",
        "ax.set_xlabel('Month')\r\n",
        "ax.set_ylabel('Amount of tweets')\r\n",
        "plt.xticks(rotation=45)\r\n",
        "for s in ['top', 'right']:\r\n",
        "    ax.spines[s].set_visible(False)\r\n",
        "#Add this loop to add the annotations\r\n",
        "for p in ax.patches:\r\n",
        "    width = p.get_width()\r\n",
        "    height = p.get_height()\r\n",
        "    x, y = p.get_xy() \r\n",
        "    ax.annotate(f'{height:.3}%', (x + width/2, y + height*0.5), ha='center', fontsize=12, fontweight='light')\r\n",
        "ax.set_xticklabels(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']) \r\n",
        "plt.legend(['Negative','Neutral','Positive'], bbox_to_anchor=(1.015, 0.95), loc='upper left', borderaxespad=0., frameon=False)\r\n",
        "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7IkD96K5PMex"
      },
      "source": [
        "**Year 2020**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eLDak5YEwDY4",
        "outputId": "e26ac105-5880-4763-b8c4-f7c22623f373"
      },
      "source": [
        "group2020 = year2020.groupby([year2020['month'].dt.month, 'sentiment'])[['sentiment']].count()\n",
        "group2020.columns = ['count']\n",
        "group2020"
      ],
      "execution_count": null,
      "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></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <th>sentiment</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
              "      <th>Negative</th>\n",
              "      <td>182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
              "      <th>Negative</th>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
              "      <th>Negative</th>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
              "      <th>Negative</th>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">5</th>\n",
              "      <th>Negative</th>\n",
              "      <td>213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
              "      <th>Negative</th>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">7</th>\n",
              "      <th>Negative</th>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
              "      <th>Negative</th>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">9</th>\n",
              "      <th>Negative</th>\n",
              "      <td>218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
              "      <th>Negative</th>\n",
              "      <td>242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">11</th>\n",
              "      <th>Negative</th>\n",
              "      <td>153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
              "      <th>Negative</th>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>228</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 count\n",
              "month sentiment       \n",
              "1     Negative     182\n",
              "      Neutral      292\n",
              "      Positive     402\n",
              "2     Negative     190\n",
              "      Neutral      237\n",
              "      Positive     383\n",
              "3     Negative     165\n",
              "      Neutral      259\n",
              "      Positive     473\n",
              "4     Negative     163\n",
              "      Neutral      231\n",
              "      Positive     364\n",
              "5     Negative     213\n",
              "      Neutral      330\n",
              "      Positive     468\n",
              "6     Negative     196\n",
              "      Neutral      317\n",
              "      Positive     490\n",
              "7     Negative     174\n",
              "      Neutral      244\n",
              "      Positive     380\n",
              "8     Negative     183\n",
              "      Neutral      299\n",
              "      Positive     425\n",
              "9     Negative     218\n",
              "      Neutral      404\n",
              "      Positive     549\n",
              "10    Negative     242\n",
              "      Neutral      408\n",
              "      Positive     546\n",
              "11    Negative     153\n",
              "      Neutral      253\n",
              "      Positive     345\n",
              "12    Negative     111\n",
              "      Neutral      188\n",
              "      Positive     228"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "S5VmPiyQ7e0j",
        "outputId": "fd20e8ad-7c42-4abf-fc0e-158a31a7608d"
      },
      "source": [
        "#Stacked column chart\r\n",
        "fig, ax = plt.subplots(figsize=(16,7))\r\n",
        "group2020.groupby(level=0).apply(lambda x: 100 * x / x.sum()).unstack().plot(kind='bar',stacked=True, color=['orangered','darkgray','limegreen'], width=0.6, ax=ax)\r\n",
        "plt.title('Summary of tweets in 2020')\r\n",
        "ax.set_xlabel('Month')\r\n",
        "ax.set_ylabel('Amount of tweets')\r\n",
        "plt.xticks(rotation=45)\r\n",
        "for s in ['top', 'right']:\r\n",
        "    ax.spines[s].set_visible(False)\r\n",
        "#Add this loop to add the annotations\r\n",
        "for p in ax.patches:\r\n",
        "    width = p.get_width()\r\n",
        "    height = p.get_height()\r\n",
        "    x, y = p.get_xy() \r\n",
        "    ax.annotate(f'{height:.3}%', (x + width/2, y + height*0.5), ha='center', fontsize=12, fontweight='light')\r\n",
        "ax.set_xticklabels(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']) \r\n",
        "plt.legend(['Negative','Neutral','Positive'], bbox_to_anchor=(1.015, 0.95), loc='upper left', borderaxespad=0., frameon=False)\r\n",
        "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AhXv_BLmrboq"
      },
      "source": [
        "Thematic modeling with LDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Om7ynFLr5ZD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c819dbf-2768-4a72-baa8-5a42d52dfb18"
      },
      "source": [
        "vectorizer = CountVectorizer(\r\n",
        "analyzer='word',       \r\n",
        "min_df=3, #the minimum required word occurrences\r\n",
        "stop_words='english', #remove stop words\r\n",
        "lowercase=True, #convert all words to lowercase\r\n",
        "token_pattern='[a-zA-Z0-9]{3,}', #number of characters > 3\r\n",
        "max_features=5000, #maximum number of unique words\r\n",
        "                            )\r\n",
        "data_matrix = vectorizer.fit_transform(tweets['text'])\r\n",
        "data_matrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<23809x5000 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 251052 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T4MoYCJLKqWf"
      },
      "source": [
        "lda_model = LatentDirichletAllocation(\r\n",
        "n_components=10, # Number of topics\r\n",
        "learning_method='online',\r\n",
        "random_state=20,       \r\n",
        "n_jobs = -1  # Use all available CPUs\r\n",
        "                                     )\r\n",
        "lda_output = lda_model.fit_transform(data_matrix)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 861
        },
        "id": "07kRnc-ULrq5",
        "outputId": "5fdfa8b0-19dd-4b39-c197-d3fddff7da27"
      },
      "source": [
        "pyLDAvis.enable_notebook()\r\n",
        "pyLDAvis.sklearn.prepare(lda_model, data_matrix, vectorizer, mds='tsne')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el62139673736265184197118185\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el62139673736265184197118185_data = {\"mdsDat\": {\"x\": [32.42095184326172, 0.648955225944519, 71.01968383789062, 3.7859814167022705, -42.666500091552734, -3.6216893196105957, -3.632401704788208, -36.58440017700195, 44.391380310058594, 40.235809326171875], \"y\": [8.115229606628418, 71.70709991455078, 9.971006393432617, 33.18513488769531, -9.22206974029541, -43.52651596069336, -4.171707630157471, 35.804412841796875, 50.61538314819336, -31.591665267944336], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.5304667837333, 13.09922425291951, 10.523039586228196, 9.894516589122093, 9.722341159155508, 9.237644339977633, 9.060177003234449, 9.056134219859574, 8.764088597952872, 7.112367467816869]}, \"tinfo\": {\"Term\": [\"great\", \"trump\", \"president\", \"new\", \"fake\", \"news\", \"democrats\", \"amp\", \"thank\", \"biden\", \"border\", \"want\", \"media\", \"house\", \"joe\", \"states\", \"big\", \"united\", \"good\", \"america\", \"dems\", \"state\", \"military\", \"republican\", \"china\", \"american\", \"did\", \"strong\", \"know\", \"economy\", \"state\", \"work\", \"honor\", \"coming\", \"governor\", \"come\", \"john\", \"book\", \"florida\", \"called\", \"making\", \"happy\", \"rally\", \"small\", \"future\", \"secretary\", \"really\", \"highly\", \"used\", \"men\", \"winning\", \"attack\", \"victory\", \"lowest\", \"leaving\", \"agree\", \"crowd\", \"stronger\", \"respected\", \"businesses\", \"great\", \"america\", \"big\", \"make\", \"job\", \"doing\", \"better\", \"fantastic\", \"people\", \"win\", \"numbers\", \"thank\", \"help\", \"way\", \"today\", \"administration\", \"day\", \"amp\", \"like\", \"just\", \"country\", \"vote\", \"going\", \"want\", \"did\", \"usa\", \"pelosi\", \"say\", \"radical\", \"totally\", \"stop\", \"important\", \"political\", \"nancy\", \"presidential\", \"protect\", \"fight\", \"continue\", \"borders\", \"jordan\", \"police\", 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0.24102080491606206, 0.23142263126896226, 0.2890116731515612, 0.9951887699929711, 0.9984065262231494, 0.9957187655096558], \"Term\": [\"able\", \"act\", \"action\", \"adam\", \"administration\", \"administration\", \"ago\", \"agree\", \"allow\", \"amazing\", \"amendment\", \"america\", \"america\", \"america\", \"american\", \"american\", \"american\", \"americans\", \"americans\", \"amp\", \"amp\", \"amp\", \"amp\", \"amp\", \"angry\", \"approval\", \"asked\", \"attack\", \"attorney\", \"away\", \"bad\", \"bad\", \"bad\", \"bad\", \"bad\", \"bad\", \"based\", \"believe\", \"best\", \"best\", \"better\", \"better\", \"biden\", \"big\", \"big\", \"big\", \"biggest\", \"black\", \"book\", \"border\", \"borders\", \"breaking\", \"bring\", \"building\", \"businesses\", \"california\", \"called\", \"campaign\", \"carolina\", \"case\", \"case\", \"china\", \"china\", \"choice\", \"chuck\", \"city\", \"clinton\", \"close\", \"cnn\", \"collusion\", \"come\", \"comey\", \"coming\", \"committee\", \"complete\", \"conference\", \"congratulations\", \"congress\", \"congress\", \"congressman\", \"continue\", \"continues\", \"coronavirus\", \"coronavirus\", \"coronavirus\", \"correct\", \"corrupt\", \"corrupt\", \"countries\", \"country\", \"country\", \"country\", \"country\", \"country\", \"country\", \"court\", \"covid\", \"crazy\", \"crime\", \"crime\", \"crooked\", \"crowd\", \"cuts\", \"day\", \"day\", \"day\", \"deal\", \"deal\", \"deal\", \"deals\", \"decades\", \"decision\", \"democrat\", \"democrats\", \"democrats\", \"dems\", \"department\", \"did\", \"disaster\", \"dishonest\", \"doesn\", \"doing\", \"doing\", \"doing\", \"dollars\", \"don\", \"don\", \"donald\", \"dont\", \"drug\", \"drugs\", \"economic\", \"economy\", \"elected\", \"election\", \"election\", \"election\", \"election\", \"election\", \"election\", \"end\", \"end\", \"end\", \"endorsement\", \"enemy\", \"enforcement\", \"enjoy\", \"evidence\", \"fact\", \"fake\", \"family\", \"fantastic\", 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\"job\", \"job\", \"jobs\", \"jobs\", \"jobs\", \"joe\", \"john\", \"join\", \"jordan\", \"just\", \"just\", \"just\", \"just\", \"just\", \"justice\", \"justice\", \"kim\", \"know\", \"korea\", \"large\", \"law\", \"law\", \"laws\", \"leader\", \"leadership\", \"leaving\", \"left\", \"left\", \"legal\", \"let\", \"let\", \"lies\", \"life\", \"like\", \"like\", \"like\", \"like\", \"live\", \"lives\", \"long\", \"long\", \"long\", \"long\", \"longer\", \"look\", \"looking\", \"looking\", \"lost\", \"love\", \"love\", \"loves\", \"low\", \"low\", \"low\", \"lowest\", \"maga\", \"maga\", \"major\", \"make\", \"make\", \"making\", \"man\", \"mark\", \"market\", \"matter\", \"media\", \"meeting\", \"melania\", \"men\", \"mexico\", \"mexico\", \"michael\", \"mike\", \"military\", \"million\", \"million\", \"millions\", \"minister\", \"money\", \"money\", \"money\", \"months\", \"morning\", \"mueller\", \"mueller\", \"nancy\", \"nation\", \"national\", \"national\", \"nations\", \"needs\", 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\"voter\", \"voters\", \"voting\", \"wall\", \"wall\", \"want\", \"washington\", \"watch\", \"watch\", \"way\", \"way\", \"way\", \"weak\", \"welcome\", \"went\", \"whistleblower\", \"white\", \"win\", \"win\", \"win\", \"winning\", \"witch\", \"women\", \"women\", \"won\", \"won\", \"wonderful\", \"work\", \"workers\", \"workers\", \"working\", \"working\", \"working\", \"working\", \"world\", \"worse\", \"worst\", \"wrong\", \"year\", \"year\", \"years\", \"years\", \"years\", \"years\", \"yesterday\", \"york\", \"zero\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [7, 6, 5, 10, 9, 8, 4, 1, 3, 2]};\n",
              "\n",
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              "   // already loaded: just create the visualization\n",
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              "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
              "   require([\"d3\"], function(d3){\n",
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              "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
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              "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
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            "text/plain": [
              "PreparedData(topic_coordinates=               x          y  topics  cluster       Freq\n",
              "topic                                                  \n",
              "6      32.420952   8.115230       1        1  13.530467\n",
              "5       0.648955  71.707100       2        1  13.099224\n",
              "4      71.019684   9.971006       3        1  10.523040\n",
              "9       3.785981  33.185135       4        1   9.894517\n",
              "8     -42.666500  -9.222070       5        1   9.722341\n",
              "7      -3.621689 -43.526516       6        1   9.237644\n",
              "3      -3.632402  -4.171708       7        1   9.060177\n",
              "0     -36.584400  35.804413       8        1   9.056134\n",
              "2      44.391380  50.615383       9        1   8.764089\n",
              "1      40.235809 -31.591665      10        1   7.112367, topic_info=           Term         Freq        Total Category  logprob  loglift\n",
              "1918      great  3703.000000  3703.000000  Default  30.0000  30.0000\n",
              "4590      trump  2069.000000  2069.000000  Default  29.0000  29.0000\n",
              "3411  president  2774.000000  2774.000000  Default  28.0000  28.0000\n",
              "2988        new  1306.000000  1306.000000  Default  27.0000  27.0000\n",
              "1618       fake  1232.000000  1232.000000  Default  26.0000  26.0000\n",
              "...         ...          ...          ...      ...      ...      ...\n",
              "4472      thing   139.486102   261.673530  Topic10  -4.8970   2.0142\n",
              "1434   election   171.796548   813.205259  Topic10  -4.6886   1.0887\n",
              "2991       news   186.915711  1492.331798  Topic10  -4.6043   0.5659\n",
              "4157   southern   131.967841   219.230165  Topic10  -4.9524   2.1358\n",
              "2664       long   131.465312   419.342288  Topic10  -4.9562   1.4834\n",
              "\n",
              "[466 rows x 6 columns], token_table=      Topic      Freq            Term\n",
              "term                                 \n",
              "7         4  0.993308            able\n",
              "54        6  0.997978             act\n",
              "57        3  0.990926          action\n",
              "67        6  0.997544            adam\n",
              "80        1  0.631121  administration\n",
              "...     ...       ...             ...\n",
              "4982      9  0.231423           years\n",
              "4982     10  0.289012           years\n",
              "4985      9  0.995189       yesterday\n",
              "4986      7  0.998407            york\n",
              "4997      6  0.995719            zero\n",
              "\n",
              "[541 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[7, 6, 5, 10, 9, 8, 4, 1, 3, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JKCsOn2XMcwY",
        "outputId": "1bd00593-9a3d-47a7-8d4f-6d308a63d8dc"
      },
      "source": [
        "#Let's list the 10 most used words from each topic found by the LDA:\r\n",
        "for i, topic in enumerate(lda_model.components_):\r\n",
        "  print(f'Top 10 words for topic #{i}:')\r\n",
        "  print([vectorizer.get_feature_names()[i] for i in topic.argsort()[-10:]])\r\n",
        "  print('\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Top 10 words for topic #0:\n",
            "['obama', 'americans', 'north', 'hunt', 'witch', 'people', 'look', 'american', 'amp', 'thank']\n",
            "\n",
            "\n",
            "Top 10 words for topic #1:\n",
            "['live', 'years', 'wrong', 'soon', 'including', 'today', 'white', 'good', 'president', 'house']\n",
            "\n",
            "\n",
            "Top 10 words for topic #2:\n",
            "['meeting', 'national', 'safety', 'federal', 'jobs', 'security', 'report', 'republican', 'president', 'trump']\n",
            "\n",
            "\n",
            "Top 10 words for topic #3:\n",
            "['doesn', 'times', 'washington', 'best', 'york', 'things', 'amp', 'campaign', 'economy', 'new']\n",
            "\n",
            "\n",
            "Top 10 words for topic #4:\n",
            "['right', 'trade', 'just', 'world', 'china', 'united', 'states', 'media', 'fake', 'news']\n",
            "\n",
            "\n",
            "Top 10 words for topic #5:\n",
            "['republicans', 'left', 'election', 'just', 'don', 'vote', 'country', 'did', 'want', 'democrats']\n",
            "\n",
            "\n",
            "Top 10 words for topic #6:\n",
            "['doing', 'thank', 'job', 'make', 'amp', 'state', 'people', 'america', 'big', 'great']\n",
            "\n",
            "\n",
            "Top 10 words for topic #7:\n",
            "['hard', 'clinton', 'hillary', 'dollars', 'year', 'schiff', 'time', 'collusion', 'fbi', 'dems']\n",
            "\n",
            "\n",
            "Top 10 words for topic #8:\n",
            "['law', 'party', 'wall', 'total', 'russia', 'strong', 'military', 'great', 'amp', 'border']\n",
            "\n",
            "\n",
            "Top 10 words for topic #9:\n",
            "['said', 'good', 'democrats', 'impeachment', 'senate', 'democrat', 'know', 'joe', 'biden', 'amp']\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}