TweetAnalysis.ipynb

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        "colab_type": "text"
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      "source": [
        "# Tweet Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9UBRuQ8cGeI",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "base_path = '/content/drive/My Drive/tweet/'"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RO2vmmg2sOX6",
        "colab_type": "text"
      },
      "source": [
        "## Import Library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L95YRDRlsOX6",
        "colab_type": "code",
        "outputId": "005943f6-e81f-46d9-fd77-8fc70966a585",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "import nltk\n",
        "import string\n",
        "import gensim\n",
        "import pickle\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import timeit\n",
        "from gensim import models, corpora\n",
        "from gensim.models import CoherenceModel\n",
        "import matplotlib.pyplot as plt\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "nltk.download('vader_lexicon')\n",
        "from tqdm.notebook import tnrange\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/nltk/twitter/__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.\n",
            "  warnings.warn(\"The twython library has not been installed. \"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6mWuW7BTsOX-",
        "colab_type": "text"
      },
      "source": [
        "## Sentiment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "SJluYaZCsOX_",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "df = pd.read_csv(base_path+\"dataClean.csv\")\n",
        "df = df.drop(['Unnamed: 0'], axis=1)\n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>Date</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>business we talked about investors reaction in...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>let s blow this joint anywhere this doesn t exist</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>the us has reported its first case of the dead...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>update on from china people have died and case...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>and here i was thinking the was how i was gonn...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553391</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>why in god s name are assemblyline employees p...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553392</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>us tests per day day average february march ma...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553393</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>today north central bronx hospital cut the rib...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553394</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>china s lately launched nationwide blockchain ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553395</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>text cinco to to make your donation to support...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>553396 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Date                                               Text\n",
              "0      2020-01-22  business we talked about investors reaction in...\n",
              "1      2020-01-22  let s blow this joint anywhere this doesn t exist\n",
              "2      2020-01-22  the us has reported its first case of the dead...\n",
              "3      2020-01-22  update on from china people have died and case...\n",
              "4      2020-01-22  and here i was thinking the was how i was gonn...\n",
              "...           ...                                                ...\n",
              "553391 2020-05-05  why in god s name are assemblyline employees p...\n",
              "553392 2020-05-05  us tests per day day average february march ma...\n",
              "553393 2020-05-05  today north central bronx hospital cut the rib...\n",
              "553394 2020-05-05  china s lately launched nationwide blockchain ...\n",
              "553395 2020-05-05  text cinco to to make your donation to support...\n",
              "\n",
              "[553396 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQI6Sxe1sOYD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1a095133-bb9c-42fd-fe21-cadc72118ddf"
      },
      "source": [
        "senti = SentimentIntensityAnalyzer()\n",
        "def get_sentiment_score(review):\n",
        "    try:\n",
        "        compound_score = senti.polarity_scores(review)['compound']\n",
        "        return compound_score\n",
        "    except:\n",
        "        return \"error\"\n",
        "    \n",
        "def get_sentiment(score):\n",
        "    if score > 0:\n",
        "        return 'Positive'\n",
        "    elif score < 0:\n",
        "        return 'Negative'\n",
        "    else:\n",
        "        return 'Neutral'\n",
        "start = timeit.default_timer()\n",
        "df[\"Sentiment_score\"] = df[\"Text\"].apply(get_sentiment_score)\n",
        "df[\"Polarity\"] = df[\"Sentiment_score\"].apply(get_sentiment)\n",
        "stop = timeit.default_timer()\n",
        "print('Time: ', stop - start)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time:  158.56596332400022\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-0IixvqsOYG",
        "colab_type": "code",
        "outputId": "8a84d72b-a1e6-439c-9cbf-55413e528c93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "print('Number of positive tweet:',len(df[df['Polarity']=='Positive']))\n",
        "print('Number of negative tweet:',len(df[df['Polarity']=='Negative']))\n",
        "print('Number of neutral tweet:',len(df[df['Polarity']=='Neutral']))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of positive tweet: 225753\n",
            "Number of negative tweet: 210681\n",
            "Number of neutral tweet: 116962\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfoNIfJDsOYI",
        "colab_type": "code",
        "outputId": "8c0775a3-9fbe-404e-813b-07e0123a2722",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "df_temp = df.groupby(['Date','Polarity']).count().unstack()\n",
        "df_temp = df_temp[\"Text\"].reset_index()\n",
        "df_temp['Positive_rate'] = df_temp['Positive']*100.0/(df_temp['Positive']+df_temp['Negative']+df_temp['Neutral'])\n",
        "df_temp['Neutral_rate'] = df_temp['Neutral']*100.0/(df_temp['Positive']+df_temp['Negative']+df_temp['Neutral'])\n",
        "df_temp['Negative_rate'] = df_temp['Negative']*100.0/(df_temp['Positive']+df_temp['Negative']+df_temp['Neutral'])\n",
        "df_temp['Count'] = df_temp['Positive']+df_temp['Negative']+df_temp['Neutral']\n",
        "df_temp"
      ],
      "execution_count": 6,
      "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>Polarity</th>\n",
              "      <th>Date</th>\n",
              "      <th>Negative</th>\n",
              "      <th>Neutral</th>\n",
              "      <th>Positive</th>\n",
              "      <th>Positive_rate</th>\n",
              "      <th>Neutral_rate</th>\n",
              "      <th>Negative_rate</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>46.875000</td>\n",
              "      <td>40.625000</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>14</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>32.432432</td>\n",
              "      <td>29.729730</td>\n",
              "      <td>37.837838</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>28</td>\n",
              "      <td>23</td>\n",
              "      <td>15</td>\n",
              "      <td>22.727273</td>\n",
              "      <td>34.848485</td>\n",
              "      <td>42.424242</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>21</td>\n",
              "      <td>16</td>\n",
              "      <td>24</td>\n",
              "      <td>39.344262</td>\n",
              "      <td>26.229508</td>\n",
              "      <td>34.426230</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>36</td>\n",
              "      <td>17</td>\n",
              "      <td>16</td>\n",
              "      <td>23.188406</td>\n",
              "      <td>24.637681</td>\n",
              "      <td>52.173913</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>5843</td>\n",
              "      <td>3283</td>\n",
              "      <td>6699</td>\n",
              "      <td>42.331754</td>\n",
              "      <td>20.745656</td>\n",
              "      <td>36.922591</td>\n",
              "      <td>15825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>2020-05-02</td>\n",
              "      <td>3988</td>\n",
              "      <td>2193</td>\n",
              "      <td>3939</td>\n",
              "      <td>38.922925</td>\n",
              "      <td>21.669960</td>\n",
              "      <td>39.407115</td>\n",
              "      <td>10120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>2020-05-03</td>\n",
              "      <td>3613</td>\n",
              "      <td>2012</td>\n",
              "      <td>3513</td>\n",
              "      <td>38.443861</td>\n",
              "      <td>22.017947</td>\n",
              "      <td>39.538192</td>\n",
              "      <td>9138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>2020-05-04</td>\n",
              "      <td>3952</td>\n",
              "      <td>2193</td>\n",
              "      <td>4658</td>\n",
              "      <td>43.117653</td>\n",
              "      <td>20.299917</td>\n",
              "      <td>36.582431</td>\n",
              "      <td>10803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>5537</td>\n",
              "      <td>2911</td>\n",
              "      <td>6127</td>\n",
              "      <td>42.037736</td>\n",
              "      <td>19.972556</td>\n",
              "      <td>37.989708</td>\n",
              "      <td>14575</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>104 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Polarity       Date  Negative  Neutral  ...  Neutral_rate  Negative_rate  Count\n",
              "0        2020-01-22        13       15  ...     46.875000      40.625000     32\n",
              "1        2020-01-23        14       11  ...     29.729730      37.837838     37\n",
              "2        2020-01-24        28       23  ...     34.848485      42.424242     66\n",
              "3        2020-01-25        21       16  ...     26.229508      34.426230     61\n",
              "4        2020-01-26        36       17  ...     24.637681      52.173913     69\n",
              "..              ...       ...      ...  ...           ...            ...    ...\n",
              "99       2020-05-01      5843     3283  ...     20.745656      36.922591  15825\n",
              "100      2020-05-02      3988     2193  ...     21.669960      39.407115  10120\n",
              "101      2020-05-03      3613     2012  ...     22.017947      39.538192   9138\n",
              "102      2020-05-04      3952     2193  ...     20.299917      36.582431  10803\n",
              "103      2020-05-05      5537     2911  ...     19.972556      37.989708  14575\n",
              "\n",
              "[104 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6CCXD2KjsOYL",
        "colab_type": "code",
        "outputId": "a7c19432-8c5d-4526-8e86-3f28209f43f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        }
      },
      "source": [
        "plt.plot('Date', 'Positive_rate', data=df_temp, color='green')\n",
        "plt.plot('Date', 'Negative_rate', data=df_temp, color='red')\n",
        "plt.xticks(rotation=50)\n",
        "plt.ylabel('Percentage', fontsize=15)\n",
        "plt.xlabel('Date', fontsize=15)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FACQ5ddPsOYN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(base_path+\"sentiment_result.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDcFoIvtsOYP",
        "colab_type": "text"
      },
      "source": [
        "## Topic modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQXh8ddKsOYQ",
        "colab_type": "text"
      },
      "source": [
        "### Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WChYE9UsOYQ",
        "colab_type": "code",
        "outputId": "d2d9b27c-4cc6-46ae-d3e3-abec763f1d14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "nltk.download('wordnet')\n",
        "stop = gensim.parsing.preprocessing.STOPWORDS\n",
        "stop_extent = ['ok','let','hey']\n",
        "punc = set(string.punctuation)\n",
        "wn = WordNetLemmatizer()\n",
        "\n",
        "def clean(doc):\n",
        "    # remove punctuation\n",
        "    doc = [ch for ch in doc if ch not in punc]\n",
        "    doc = \"\".join(doc)\n",
        "    \n",
        "    doc = doc.split()\n",
        "    # remove stop words\n",
        "    doc = [word for word in doc if word not in stop]\n",
        "    doc = [word for word in doc if word not in stop_extent]\n",
        "    # bigram\n",
        "    doc = bigram_mod[doc]\n",
        "    # lemmatize\n",
        "    doc = [wn.lemmatize(word) for word in doc]\n",
        "    # remove single char\n",
        "    doc = [word for word in doc if len(word) >1]\n",
        "    return doc\n",
        "\n",
        "start = timeit.default_timer()\n",
        "doc_full = df['Text'].to_list()\n",
        "# bigram mode\n",
        "bigram = gensim.models.Phrases([i.split() for i in doc_full], min_count=5, threshold=100)\n",
        "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
        "# clean data\n",
        "doc_clean = [clean(doc) for doc in doc_full]\n",
        "stop = timeit.default_timer()\n",
        "print('Time: ', stop - start)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "Time:  129.354234938\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiTa1_-t0BQm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# save\n",
        "with open(base_path+\"doc_clean.pkl\", \"wb\") as fp: \n",
        "    pickle.dump(doc_clean, fp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlDGPgBUsOYS",
        "colab_type": "text"
      },
      "source": [
        "### Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWFul4pI3_Ji",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load\n",
        "doc_clean = []\n",
        "with open(base_path+\"doc_clean.pkl\", 'rb') as fp:\n",
        "    doc_clean = pickle.load(fp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zy9uA01GsOYT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600,
          "referenced_widgets": [
            "8c116e76c0384735a0b39d44ec995ec1",
            "c2770df0c3ed4eefb71e1d87ce91ed8c",
            "806a015340d642c88ecf493814a800f5",
            "a5abe64b737041af8fa9d517d9193733",
            "a2e7bf91cf1d4ca19bad5c76fea3d5ef",
            "d2a7dffaf6a94a76a89c194748f9b96e",
            "8db67f7cc3044b31838c5e1d2f0248a1",
            "5cb0c8e2f4fd4c31a84c5ae1d632f86b"
          ]
        },
        "outputId": "f3919e11-6ae8-4c49-bf56-c432e59f846c"
      },
      "source": [
        "doc_clean_small = doc_clean[:200000]\n",
        "dictionary = corpora.Dictionary(doc_clean_small)\n",
        "dictionary.filter_extremes(no_below=5)\n",
        "corpus = [dictionary.doc2bow(doc) for doc in doc_clean_small]\n",
        "tfidf = models.TfidfModel(corpus)\n",
        "corpus_tfidf = tfidf[corpus]\n",
        "lda_models = []\n",
        "for num_topics in tnrange(20,30):\n",
        "    print ('No. topics: {}'.format(num_topics))\n",
        "    ldamodel = models.ldamulticore.LdaMulticore(corpus=corpus_tfidf, \n",
        "                                                num_topics=num_topics, \n",
        "                                                id2word=dictionary,\n",
        "                                                workers=6,\n",
        "                                                passes=20)\n",
        "    perplexity = ldamodel.log_perplexity(corpus_tfidf)\n",
        "    print('Perplexity: {}'.format(perplexity))\n",
        "    coherence_model_lda = CoherenceModel(model=ldamodel, \n",
        "                                        texts=doc_clean_small, \n",
        "                                        dictionary=dictionary, \n",
        "                                        coherence='c_v')\n",
        "    score = coherence_model_lda.get_coherence()\n",
        "    print('Score: {}'.format(score))\n",
        "    lda_models.append(ldamodel)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8c116e76c0384735a0b39d44ec995ec1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "No. topics: 20\n",
            "Perplexity: -10.584885184541692\n",
            "Score: 0.44120446174403744\n",
            "No. topics: 21\n",
            "Perplexity: -10.652254924964044\n",
            "Score: 0.42743932731489387\n",
            "No. topics: 22\n",
            "Perplexity: -10.713743125734824\n",
            "Score: 0.42608636491470336\n",
            "No. topics: 23\n",
            "Perplexity: -10.749066393660355\n",
            "Score: 0.40945042225355244\n",
            "No. topics: 24\n",
            "Perplexity: -10.79381737000039\n",
            "Score: 0.4095813894058131\n",
            "No. topics: 25\n",
            "Perplexity: -10.845090928579033\n",
            "Score: 0.42669408460735697\n",
            "No. topics: 26\n",
            "Perplexity: -10.921729828461192\n",
            "Score: 0.4126999913562725\n",
            "No. topics: 27\n",
            "Perplexity: -10.933590998591002\n",
            "Score: 0.4560466548507642\n",
            "No. topics: 28\n",
            "Perplexity: -10.9712591598644\n",
            "Score: 0.4115784950762584\n",
            "No. topics: 29\n",
            "Perplexity: -11.024811258383178\n",
            "Score: 0.4498601926311814\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSDU7E1gE-ih",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ldamodel = lda_models[7]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5EosqtRAFQpw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# save ldamodel\n",
        "with open(base_path+\"lda_model.pkl\", \"wb\") as fp: \n",
        "    pickle.dump(ldamodel, fp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZmeeTEBOt3-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load ldamodel\n",
        "ldamodel = None\n",
        "with open(base_path+\"lda_model.pkl\", \"rb\") as fp: \n",
        "    ldamodel = pickle.load(fp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lG88QsjUfDTt",
        "colab_type": "code",
        "outputId": "1ce220be-1a44-4b6b-effc-4f7a837ba5e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 996
        }
      },
      "source": [
        "for idx, topic in ldamodel.print_topics(-1):\n",
        "    print(\"Topic: {} \\nWords: {}\".format(idx, topic ))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Topic: 0 \n",
            "Words: 0.009*\"hand\" + 0.007*\"wash\" + 0.005*\"tw\" + 0.004*\"prisoner\" + 0.004*\"clinical_trial\" + 0.004*\"officer\" + 0.004*\"writes\" + 0.003*\"water\" + 0.003*\"changing\" + 0.003*\"soap\"\n",
            "Topic: 1 \n",
            "Words: 0.009*\"india\" + 0.006*\"warned\" + 0.006*\"trump\" + 0.005*\"hart_island\" + 0.005*\"oil\" + 0.005*\"loved_one\" + 0.005*\"latino\" + 0.005*\"nyt\" + 0.005*\"forever\" + 0.004*\"changed\"\n",
            "Topic: 2 \n",
            "Words: 0.008*\"resource\" + 0.007*\"join\" + 0.007*\"pandemic\" + 0.006*\"crisis\" + 0.006*\"student\" + 0.006*\"help\" + 0.005*\"free\" + 0.005*\"curve\" + 0.005*\"webinar\" + 0.005*\"health\"\n",
            "Topic: 3 \n",
            "Words: 0.009*\"like\" + 0.008*\"people\" + 0.007*\"know\" + 0.007*\"shit\" + 0.006*\"got\" + 0.006*\"thing\" + 0.006*\"going\" + 0.005*\"gonna\" + 0.005*\"day\" + 0.005*\"think\"\n",
            "Topic: 4 \n",
            "Words: 0.008*\"dr_fauci\" + 0.007*\"fauci\" + 0.007*\"florida\" + 0.006*\"texas\" + 0.005*\"plasma\" + 0.005*\"poverty\" + 0.005*\"trump\" + 0.004*\"saved\" + 0.004*\"pollution\" + 0.004*\"laid\"\n",
            "Topic: 5 \n",
            "Words: 0.012*\"stay\" + 0.012*\"stayhome\" + 0.011*\"home\" + 0.009*\"nyc\" + 0.008*\"safe\" + 0.008*\"igshid\" + 0.007*\"quarantine\" + 0.006*\"time\" + 0.006*\"socialdistancing\" + 0.006*\"new\"\n",
            "Topic: 6 \n",
            "Words: 0.013*\"relief\" + 0.011*\"worker\" + 0.010*\"fund\" + 0.009*\"help\" + 0.008*\"pandemic\" + 0.008*\"support\" + 0.008*\"health\" + 0.007*\"healthcare\" + 0.007*\"donate\" + 0.006*\"need\"\n",
            "Topic: 7 \n",
            "Words: 0.010*\"tiger\" + 0.007*\"homeless\" + 0.007*\"worker\" + 0.006*\"essential\" + 0.006*\"bronx_zoo\" + 0.006*\"positive\" + 0.006*\"test\" + 0.006*\"mta\" + 0.005*\"shelter\" + 0.005*\"new\"\n",
            "Topic: 8 \n",
            "Words: 0.008*\"spain\" + 0.007*\"grocery\" + 0.005*\"initiative\" + 0.005*\"threatens\" + 0.005*\"hardest_hit\" + 0.005*\"brief\" + 0.005*\"pause\" + 0.004*\"singapore\" + 0.004*\"weekly\" + 0.004*\"pledge\"\n",
            "Topic: 9 \n",
            "Words: 0.011*\"sign\" + 0.010*\"petition\" + 0.006*\"pandemic\" + 0.005*\"trump\" + 0.005*\"intelligence\" + 0.004*\"month\" + 0.004*\"spread\" + 0.004*\"crisis\" + 0.004*\"american_moneyforthepeople\" + 0.003*\"projection\"\n",
            "Topic: 10 \n",
            "Words: 0.010*\"contact_tracing\" + 0.009*\"mom\" + 0.009*\"reopen\" + 0.008*\"survivor\" + 0.006*\"utm_source\" + 0.006*\"minority\" + 0.006*\"campaign\" + 0.006*\"lost\" + 0.005*\"project\" + 0.005*\"shutdown\"\n",
            "Topic: 11 \n",
            "Words: 0.020*\"mask\" + 0.008*\"face\" + 0.007*\"wearing\" + 0.007*\"wear\" + 0.005*\"people\" + 0.005*\"igshid\" + 0.005*\"glove\" + 0.004*\"like\" + 0.004*\"articleshare\" + 0.004*\"html_referringsource\"\n",
            "Topic: 12 \n",
            "Words: 0.010*\"testing\" + 0.009*\"vaccine\" + 0.008*\"trial\" + 0.007*\"voting\" + 0.007*\"site\" + 0.007*\"launch\" + 0.006*\"mail\" + 0.005*\"vote\" + 0.005*\"record\" + 0.005*\"south_korea\"\n",
            "Topic: 13 \n",
            "Words: 0.018*\"black\" + 0.010*\"people\" + 0.008*\"rate\" + 0.008*\"data\" + 0.007*\"death\" + 0.007*\"american\" + 0.007*\"dying\" + 0.006*\"higher\" + 0.006*\"african\" + 0.005*\"killing\"\n",
            "Topic: 14 \n",
            "Words: 0.032*\"case\" + 0.029*\"death\" + 0.028*\"new\" + 0.023*\"york\" + 0.013*\"death_toll\" + 0.013*\"number\" + 0.012*\"state\" + 0.012*\"confirmed\" + 0.009*\"city\" + 0.009*\"total\"\n",
            "Topic: 15 \n",
            "Words: 0.021*\"update\" + 0.016*\"live\" + 0.013*\"briefing\" + 0.011*\"mayor\" + 0.011*\"blasio\" + 0.010*\"latest\" + 0.009*\"watch\" + 0.009*\"news\" + 0.008*\"new\" + 0.008*\"task_force\"\n",
            "Topic: 16 \n",
            "Words: 0.009*\"prison\" + 0.008*\"jail\" + 0.008*\"inmate\" + 0.007*\"passed_away\" + 0.005*\"pandemic\" + 0.005*\"release\" + 0.005*\"charity\" + 0.005*\"housing\" + 0.005*\"rent\" + 0.004*\"crisis\"\n",
            "Topic: 17 \n",
            "Words: 0.015*\"patient\" + 0.010*\"drug\" + 0.010*\"hydroxychloroquine\" + 0.009*\"treatment\" + 0.009*\"hospital\" + 0.006*\"treat\" + 0.006*\"doctor\" + 0.006*\"supply\" + 0.006*\"survey\" + 0.005*\"study\"\n",
            "Topic: 18 \n",
            "Words: 0.008*\"tracking\" + 0.006*\"privacy\" + 0.006*\"apple_google\" + 0.006*\"equipment\" + 0.005*\"scam\" + 0.005*\"google\" + 0.005*\"technology\" + 0.005*\"track\" + 0.004*\"misinformation\" + 0.004*\"news\"\n",
            "Topic: 19 \n",
            "Words: 0.008*\"cat\" + 0.007*\"wisconsin\" + 0.007*\"pet\" + 0.006*\"dog\" + 0.006*\"russia\" + 0.006*\"easter_sunday\" + 0.005*\"election\" + 0.005*\"real_estate\" + 0.005*\"animal\" + 0.005*\"warns\"\n",
            "Topic: 20 \n",
            "Words: 0.024*\"trump\" + 0.007*\"china\" + 0.007*\"president\" + 0.007*\"american\" + 0.005*\"hoax\" + 0.005*\"response\" + 0.005*\"people\" + 0.005*\"donald\" + 0.005*\"biden\" + 0.005*\"democrat\"\n",
            "Topic: 21 \n",
            "Words: 0.007*\"map\" + 0.007*\"icu\" + 0.005*\"amazon\" + 0.005*\"feature\" + 0.005*\"defeat\" + 0.004*\"ea\" + 0.004*\"challenging\" + 0.004*\"bailout\" + 0.004*\"democracy\" + 0.004*\"cc\"\n",
            "Topic: 22 \n",
            "Words: 0.011*\"ventilator\" + 0.010*\"frontline\" + 0.006*\"fuck\" + 0.006*\"sport\" + 0.006*\"hero\" + 0.006*\"peak\" + 0.005*\"season\" + 0.005*\"game\" + 0.004*\"celebrity\" + 0.004*\"fan\"\n",
            "Topic: 23 \n",
            "Words: 0.007*\"gate\" + 0.006*\"inequality\" + 0.006*\"nursing_home\" + 0.005*\"pandemic\" + 0.005*\"reveals\" + 0.005*\"recovering\" + 0.004*\"startup\" + 0.004*\"crisis\" + 0.004*\"utm_medium\" + 0.004*\"success\"\n",
            "Topic: 24 \n",
            "Words: 0.008*\"passover\" + 0.006*\"flight\" + 0.006*\"airline\" + 0.005*\"refund\" + 0.005*\"tracker\" + 0.004*\"cousin\" + 0.004*\"payment\" + 0.004*\"cancel\" + 0.004*\"ease\" + 0.004*\"pastor\"\n",
            "Topic: 25 \n",
            "Words: 0.010*\"test\" + 0.010*\"recovered\" + 0.009*\"flu\" + 0.008*\"tested\" + 0.008*\"antibody\" + 0.008*\"people\" + 0.007*\"symptom\" + 0.005*\"immigrant\" + 0.005*\"testing\" + 0.005*\"know\"\n",
            "Topic: 26 \n",
            "Words: 0.024*\"easter\" + 0.012*\"boris_johnson\" + 0.005*\"honor\" + 0.005*\"celebrate\" + 0.005*\"navigate\" + 0.005*\"veteran\" + 0.005*\"church\" + 0.005*\"chloroquine\" + 0.004*\"tribute\" + 0.004*\"magazine\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcg--pKmHxA5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "outputId": "d728c540-351f-4abd-e3fb-d6a06ef6a466"
      },
      "source": [
        "topics = []\n",
        "for idx, topic in ldamodel.print_topics(-1):\n",
        "    each_topic = [i.split('*\"')[1][:-1] for i in topic.split(' + ')]\n",
        "\n",
        "    topics.append(', '.join(each_topic))\n",
        "df_topics = pd.DataFrame(topics,columns=['Topics'])\n",
        "df_topics.to_csv(base_path+'model_topics.csv')\n",
        "df_topics"
      ],
      "execution_count": 42,
      "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>Topics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hand, wash, tw, prisoner, clinical_trial, offi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>india, warned, trump, hart_island, oil, loved_...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>resource, join, pandemic, crisis, student, hel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>like, people, know, shit, got, thing, going, g...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>dr_fauci, fauci, florida, texas, plasma, pover...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>stay, stayhome, home, nyc, safe, igshid, quara...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>relief, worker, fund, help, pandemic, support,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>tiger, homeless, worker, essential, bronx_zoo,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>spain, grocery, initiative, threatens, hardest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>sign, petition, pandemic, trump, intelligence,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>contact_tracing, mom, reopen, survivor, utm_so...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>mask, face, wearing, wear, people, igshid, glo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>testing, vaccine, trial, voting, site, launch,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>black, people, rate, data, death, american, dy...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>case, death, new, york, death_toll, number, st...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>update, live, briefing, mayor, blasio, latest,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>prison, jail, inmate, passed_away, pandemic, r...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>patient, drug, hydroxychloroquine, treatment, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>tracking, privacy, apple_google, equipment, sc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>cat, wisconsin, pet, dog, russia, easter_sunda...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>trump, china, president, american, hoax, respo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>map, icu, amazon, feature, defeat, ea, challen...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>ventilator, frontline, fuck, sport, hero, peak...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>gate, inequality, nursing_home, pandemic, reve...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>passover, flight, airline, refund, tracker, co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>test, recovered, flu, tested, antibody, people...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>easter, boris_johnson, honor, celebrate, navig...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               Topics\n",
              "0   hand, wash, tw, prisoner, clinical_trial, offi...\n",
              "1   india, warned, trump, hart_island, oil, loved_...\n",
              "2   resource, join, pandemic, crisis, student, hel...\n",
              "3   like, people, know, shit, got, thing, going, g...\n",
              "4   dr_fauci, fauci, florida, texas, plasma, pover...\n",
              "5   stay, stayhome, home, nyc, safe, igshid, quara...\n",
              "6   relief, worker, fund, help, pandemic, support,...\n",
              "7   tiger, homeless, worker, essential, bronx_zoo,...\n",
              "8   spain, grocery, initiative, threatens, hardest...\n",
              "9   sign, petition, pandemic, trump, intelligence,...\n",
              "10  contact_tracing, mom, reopen, survivor, utm_so...\n",
              "11  mask, face, wearing, wear, people, igshid, glo...\n",
              "12  testing, vaccine, trial, voting, site, launch,...\n",
              "13  black, people, rate, data, death, american, dy...\n",
              "14  case, death, new, york, death_toll, number, st...\n",
              "15  update, live, briefing, mayor, blasio, latest,...\n",
              "16  prison, jail, inmate, passed_away, pandemic, r...\n",
              "17  patient, drug, hydroxychloroquine, treatment, ...\n",
              "18  tracking, privacy, apple_google, equipment, sc...\n",
              "19  cat, wisconsin, pet, dog, russia, easter_sunda...\n",
              "20  trump, china, president, american, hoax, respo...\n",
              "21  map, icu, amazon, feature, defeat, ea, challen...\n",
              "22  ventilator, frontline, fuck, sport, hero, peak...\n",
              "23  gate, inequality, nursing_home, pandemic, reve...\n",
              "24  passover, flight, airline, refund, tracker, co...\n",
              "25  test, recovered, flu, tested, antibody, people...\n",
              "26  easter, boris_johnson, honor, celebrate, navig..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6PfN15ERj7p",
        "colab_type": "code",
        "outputId": "9baa4570-611c-49ea-df18-7a58b6cf355f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456,
          "referenced_widgets": [
            "241bc134d2694c5899a0867d4296a9ad",
            "0f5f077867de4375881c90031dc0c04b",
            "3dcc386844f94676bf7747fcfe026c4d",
            "0ee3a3757e9b42adb7d78174cff6d599",
            "0aea741f31904946a4c6ec398a77a164",
            "dc57aeea67244fa6bd04ac1fc3f246bc",
            "f04b6956322b445bbee00e6302572301",
            "cd181c84884e4fa4858a8aca48a3dab5"
          ]
        }
      },
      "source": [
        "topic_list = [sorted(ldamodel[dictionary.doc2bow(doc_clean[i])], \n",
        "                     key=lambda tup: -1*tup[1])[0][0] for i in tnrange(len(doc_clean))]\n",
        "df['Topics'] = pd.DataFrame(topic_list,columns=['Topics'])\n",
        "df"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "241bc134d2694c5899a0867d4296a9ad",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=553396), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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>Date</th>\n",
              "      <th>Text</th>\n",
              "      <th>Sentiment_score</th>\n",
              "      <th>Polarity</th>\n",
              "      <th>Topics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>business we talked about investors reaction in...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>let s blow this joint anywhere this doesn t exist</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>the us has reported its first case of the dead...</td>\n",
              "      <td>-0.0258</td>\n",
              "      <td>Negative</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>update on from china people have died and case...</td>\n",
              "      <td>-0.8442</td>\n",
              "      <td>Negative</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>and here i was thinking the was how i was gonn...</td>\n",
              "      <td>-0.6560</td>\n",
              "      <td>Negative</td>\n",
              "      <td>3</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>553391</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>why in god s name are assemblyline employees p...</td>\n",
              "      <td>0.2732</td>\n",
              "      <td>Positive</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553392</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>us tests per day day average february march ma...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553393</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>today north central bronx hospital cut the rib...</td>\n",
              "      <td>-0.0258</td>\n",
              "      <td>Negative</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553394</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>china s lately launched nationwide blockchain ...</td>\n",
              "      <td>-0.2732</td>\n",
              "      <td>Negative</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553395</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>text cinco to to make your donation to support...</td>\n",
              "      <td>0.4019</td>\n",
              "      <td>Positive</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>553396 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Date  ... Topics\n",
              "0      2020-01-22  ...      2\n",
              "1      2020-01-22  ...      3\n",
              "2      2020-01-22  ...     14\n",
              "3      2020-01-22  ...     14\n",
              "4      2020-01-22  ...      3\n",
              "...           ...  ...    ...\n",
              "553391 2020-05-05  ...      7\n",
              "553392 2020-05-05  ...     14\n",
              "553393 2020-05-05  ...     17\n",
              "553394 2020-05-05  ...     15\n",
              "553395 2020-05-05  ...     15\n",
              "\n",
              "[553396 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jcc5BKRi_zGY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "120f9cb5-64ba-4652-cf52-cf7b8c7f73fe"
      },
      "source": [
        "df_topic = df.groupby(['Topics','Polarity'])['Text'].count().unstack()\n",
        "df_topic[\"Positive_rate\"] = df_topic[\"Positive\"]/(df_topic[\"Positive\"]+df_topic[\"Negative\"]+df_topic[\"Neutral\"])\n",
        "df_topic[\"Negative_rate\"] = df_topic[\"Negative\"]/(df_topic[\"Positive\"]+df_topic[\"Negative\"]+df_topic[\"Neutral\"])\n",
        "df_topic[\"Neutral_rate\"] = df_topic[\"Neutral\"]/(df_topic[\"Positive\"]+df_topic[\"Negative\"]+df_topic[\"Neutral\"])\n",
        "df_topic[[\"Neutral\",\"Negative\",\"Positive\"]].plot(kind='bar',stacked=True)\n",
        "df_topic"
      ],
      "execution_count": 41,
      "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>Polarity</th>\n",
              "      <th>Negative</th>\n",
              "      <th>Neutral</th>\n",
              "      <th>Positive</th>\n",
              "      <th>Positive_rate</th>\n",
              "      <th>Negative_rate</th>\n",
              "      <th>Neutral_rate</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topics</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3891</td>\n",
              "      <td>3174</td>\n",
              "      <td>3582</td>\n",
              "      <td>0.336433</td>\n",
              "      <td>0.365455</td>\n",
              "      <td>0.298112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2483</td>\n",
              "      <td>1604</td>\n",
              "      <td>1662</td>\n",
              "      <td>0.289094</td>\n",
              "      <td>0.431901</td>\n",
              "      <td>0.279005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14547</td>\n",
              "      <td>11822</td>\n",
              "      <td>32160</td>\n",
              "      <td>0.549471</td>\n",
              "      <td>0.248543</td>\n",
              "      <td>0.201985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>25508</td>\n",
              "      <td>7443</td>\n",
              "      <td>26594</td>\n",
              "      <td>0.446620</td>\n",
              "      <td>0.428382</td>\n",
              "      <td>0.124998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2814</td>\n",
              "      <td>1545</td>\n",
              "      <td>1626</td>\n",
              "      <td>0.271679</td>\n",
              "      <td>0.470175</td>\n",
              "      <td>0.258145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>9186</td>\n",
              "      <td>6425</td>\n",
              "      <td>21535</td>\n",
              "      <td>0.579739</td>\n",
              "      <td>0.247294</td>\n",
              "      <td>0.172966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>14347</td>\n",
              "      <td>5248</td>\n",
              "      <td>24934</td>\n",
              "      <td>0.559950</td>\n",
              "      <td>0.322195</td>\n",
              "      <td>0.117856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5163</td>\n",
              "      <td>2822</td>\n",
              "      <td>6211</td>\n",
              "      <td>0.437518</td>\n",
              "      <td>0.363694</td>\n",
              "      <td>0.198788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1361</td>\n",
              "      <td>1120</td>\n",
              "      <td>1230</td>\n",
              "      <td>0.331447</td>\n",
              "      <td>0.366748</td>\n",
              "      <td>0.301805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>6321</td>\n",
              "      <td>3544</td>\n",
              "      <td>5976</td>\n",
              "      <td>0.377249</td>\n",
              "      <td>0.399028</td>\n",
              "      <td>0.223723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1435</td>\n",
              "      <td>1284</td>\n",
              "      <td>1938</td>\n",
              "      <td>0.416148</td>\n",
              "      <td>0.308138</td>\n",
              "      <td>0.275714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5225</td>\n",
              "      <td>4276</td>\n",
              "      <td>6894</td>\n",
              "      <td>0.420494</td>\n",
              "      <td>0.318695</td>\n",
              "      <td>0.260811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3535</td>\n",
              "      <td>3419</td>\n",
              "      <td>3501</td>\n",
              "      <td>0.334864</td>\n",
              "      <td>0.338116</td>\n",
              "      <td>0.327021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>10987</td>\n",
              "      <td>3535</td>\n",
              "      <td>4503</td>\n",
              "      <td>0.236689</td>\n",
              "      <td>0.577503</td>\n",
              "      <td>0.185808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>24494</td>\n",
              "      <td>16422</td>\n",
              "      <td>14836</td>\n",
              "      <td>0.266107</td>\n",
              "      <td>0.439338</td>\n",
              "      <td>0.294554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5335</td>\n",
              "      <td>9285</td>\n",
              "      <td>6757</td>\n",
              "      <td>0.316087</td>\n",
              "      <td>0.249567</td>\n",
              "      <td>0.434345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>4695</td>\n",
              "      <td>2449</td>\n",
              "      <td>3637</td>\n",
              "      <td>0.337353</td>\n",
              "      <td>0.435488</td>\n",
              "      <td>0.227159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>9544</td>\n",
              "      <td>5586</td>\n",
              "      <td>10475</td>\n",
              "      <td>0.409100</td>\n",
              "      <td>0.372740</td>\n",
              "      <td>0.218161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>4474</td>\n",
              "      <td>2998</td>\n",
              "      <td>4663</td>\n",
              "      <td>0.384260</td>\n",
              "      <td>0.368686</td>\n",
              "      <td>0.247054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2274</td>\n",
              "      <td>1868</td>\n",
              "      <td>2299</td>\n",
              "      <td>0.356932</td>\n",
              "      <td>0.353051</td>\n",
              "      <td>0.290017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>26820</td>\n",
              "      <td>7301</td>\n",
              "      <td>15317</td>\n",
              "      <td>0.309822</td>\n",
              "      <td>0.542498</td>\n",
              "      <td>0.147680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2108</td>\n",
              "      <td>1411</td>\n",
              "      <td>2531</td>\n",
              "      <td>0.418347</td>\n",
              "      <td>0.348430</td>\n",
              "      <td>0.233223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>3009</td>\n",
              "      <td>2214</td>\n",
              "      <td>4282</td>\n",
              "      <td>0.450500</td>\n",
              "      <td>0.316570</td>\n",
              "      <td>0.232930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>4037</td>\n",
              "      <td>2247</td>\n",
              "      <td>4217</td>\n",
              "      <td>0.401581</td>\n",
              "      <td>0.384440</td>\n",
              "      <td>0.213980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2203</td>\n",
              "      <td>1628</td>\n",
              "      <td>2057</td>\n",
              "      <td>0.349355</td>\n",
              "      <td>0.374151</td>\n",
              "      <td>0.276495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>13232</td>\n",
              "      <td>4934</td>\n",
              "      <td>10474</td>\n",
              "      <td>0.365712</td>\n",
              "      <td>0.462011</td>\n",
              "      <td>0.172277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1653</td>\n",
              "      <td>1358</td>\n",
              "      <td>1862</td>\n",
              "      <td>0.382105</td>\n",
              "      <td>0.339216</td>\n",
              "      <td>0.278678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Polarity  Negative  Neutral  ...  Negative_rate  Neutral_rate\n",
              "Topics                       ...                             \n",
              "0             3891     3174  ...       0.365455      0.298112\n",
              "1             2483     1604  ...       0.431901      0.279005\n",
              "2            14547    11822  ...       0.248543      0.201985\n",
              "3            25508     7443  ...       0.428382      0.124998\n",
              "4             2814     1545  ...       0.470175      0.258145\n",
              "5             9186     6425  ...       0.247294      0.172966\n",
              "6            14347     5248  ...       0.322195      0.117856\n",
              "7             5163     2822  ...       0.363694      0.198788\n",
              "8             1361     1120  ...       0.366748      0.301805\n",
              "9             6321     3544  ...       0.399028      0.223723\n",
              "10            1435     1284  ...       0.308138      0.275714\n",
              "11            5225     4276  ...       0.318695      0.260811\n",
              "12            3535     3419  ...       0.338116      0.327021\n",
              "13           10987     3535  ...       0.577503      0.185808\n",
              "14           24494    16422  ...       0.439338      0.294554\n",
              "15            5335     9285  ...       0.249567      0.434345\n",
              "16            4695     2449  ...       0.435488      0.227159\n",
              "17            9544     5586  ...       0.372740      0.218161\n",
              "18            4474     2998  ...       0.368686      0.247054\n",
              "19            2274     1868  ...       0.353051      0.290017\n",
              "20           26820     7301  ...       0.542498      0.147680\n",
              "21            2108     1411  ...       0.348430      0.233223\n",
              "22            3009     2214  ...       0.316570      0.232930\n",
              "23            4037     2247  ...       0.384440      0.213980\n",
              "24            2203     1628  ...       0.374151      0.276495\n",
              "25           13232     4934  ...       0.462011      0.172277\n",
              "26            1653     1358  ...       0.339216      0.278678\n",
              "\n",
              "[27 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}