3-Sentiment-Analysis-On-Cleaned-TSLA-Tweets.ipynb

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      "source": [
        "### 1. Importing packages"
      ]
    },
    {
      "cell_type": "code",
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        "id": "XS4pX3vBs2ao"
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      "source": [
        "import pandas as pd\n",
        "from textblob import TextBlob\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "### 2. Loading dataset"
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      "source": [
        "tweets_df = pd.read_csv('cleaned-tsla-tweets.csv', lineterminator='\\n')\n",
        "tweets_df = tweets_df.iloc[:, [0, 1, 4]]\n",
        "tweets_df.head()"
      ],
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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            "text/plain": [
              "                    id  ...                                      cleaned_tweet\n",
              "0  1212450794705969152  ...  fun fact audi register e tron netherlands   su...\n",
              "1  1212450579634626560  ...       mf  stock watch shit go damn year miss curve\n",
              "2  1212450337543602177  ...  icannot enough june th within day  low reply c...\n",
              "3  1212450309131227141  ...          gain momentum taiwan ownerstwn     taiwan\n",
              "4  1212449703318753280  ...  schuldensuehner extrapolate number another two...\n",
              "\n",
              "[5 rows x 3 columns]"
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      "source": [
        "tweets_df.info()"
      ],
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 113741 entries, 0 to 113740\n",
            "Data columns (total 3 columns):\n",
            " #   Column         Non-Null Count   Dtype \n",
            "---  ------         --------------   ----- \n",
            " 0   id             113741 non-null  int64 \n",
            " 1   date           113741 non-null  object\n",
            " 2   cleaned_tweet  109112 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 2.6+ MB\n"
          ]
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      },
      "source": [
        "# converting date column into datetime type\n",
        "tweets_df.date = pd.to_datetime(tweets_df.date)\n",
        "tweets_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 113741 entries, 0 to 113740\n",
            "Data columns (total 3 columns):\n",
            " #   Column         Non-Null Count   Dtype         \n",
            "---  ------         --------------   -----         \n",
            " 0   id             113741 non-null  int64         \n",
            " 1   date           113741 non-null  datetime64[ns]\n",
            " 2   cleaned_tweet  109112 non-null  object        \n",
            "dtypes: datetime64[ns](1), int64(1), object(1)\n",
            "memory usage: 2.6+ MB\n"
          ]
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    {
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        "id": "I9-onEl_s2at",
        "outputId": "d8bec6a1-4126-466b-9c5a-9ffaaa886e26"
      },
      "source": [
        "# checking for null values\n",
        "tweets_df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                  0\n",
              "date                0\n",
              "cleaned_tweet    4629\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_HwPqUms2at"
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      "source": [
        "# dropping null values\n",
        "tweets_df = tweets_df.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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        "id": "TGrtwTTos2au"
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      "source": [
        "### 3. Calculating sentiment scores"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# using TextBlob to get sentiment scores\n",
        "tweets_df['sentiment_score'] = tweets_df.apply(lambda row: TextBlob(row.cleaned_tweet).sentiment[0], axis=1)\n",
        "tweets_df.head()"
      ],
      "execution_count": null,
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              "                    id  ... sentiment_score\n",
              "0  1212450794705969152  ...            0.15\n",
              "1  1212450579634626560  ...           -0.20\n",
              "2  1212450337543602177  ...            0.20\n",
              "3  1212450309131227141  ...            0.00\n",
              "4  1212449703318753280  ...            0.75\n",
              "\n",
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      "source": [
        "# previewing how sentiment scores differ in different tweets\n",
        "pd.options.display.max_colwidth = 300\n",
        "random_subset = tweets_df.sample(n=10)\n",
        "random_subset = random_subset.drop(columns=['id', 'date'])\n",
        "random_subset = random_subset.rename(columns={'tweet': 'Original Tweet', \n",
        "                                              'cleaned_tweet': 'Cleaned Tweet', \n",
        "                                              'sentiment_score':'Sentiment Score'})\n",
        "random_subset.head(10)"
      ],
      "execution_count": null,
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cleaned Tweet</th>\n",
              "      <th>Sentiment Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>69872</th>\n",
              "      <td>amzn goog aapl</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73952</th>\n",
              "      <td>know best analysis amp alert come trial service</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2581</th>\n",
              "      <td>break news  inc close new high market cap  record b        motors  nasda  ev  automotive</td>\n",
              "      <td>0.148182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16286</th>\n",
              "      <td>trumpery elon take money experience dude  week</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11145</th>\n",
              "      <td>bot  mar th put spread</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49880</th>\n",
              "      <td>spy gspc aapl nflx goog  microsoft stock hit new time high first since february</td>\n",
              "      <td>0.182121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5459</th>\n",
              "      <td>hotpockets ever wheel fortune hodlbaggins spartantrader like tendency hyperbole happen  find way eventually sell million ev w w annually half toyota really outlandish number</td>\n",
              "      <td>0.016667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11982</th>\n",
              "      <td>bullish  ly factory open</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91499</th>\n",
              "      <td>release  result destroys revenue profit expectation electrek</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86137</th>\n",
              "      <td>moon river know facetious consider lack little big barron fun mum picture would bet entire portfolio minus  share unless camera socially distant time</td>\n",
              "      <td>-0.014583</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-ad8c9361-85e3-402b-91d0-d085e40e2fe6');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "                                                                                                                                                                       Cleaned Tweet  Sentiment Score\n",
              "69872                                                                                                                                                                amzn goog aapl          0.000000\n",
              "73952                                                                                                                                know best analysis amp alert come trial service         1.000000\n",
              "2581                                                                                        break news  inc close new high market cap  record b        motors  nasda  ev  automotive         0.148182\n",
              "16286                                                                                                                                 trumpery elon take money experience dude  week         0.000000\n",
              "11145                                                                                                                                                         bot  mar th put spread         0.000000\n",
              "49880                                                                                                spy gspc aapl nflx goog  microsoft stock hit new time high first since february         0.182121\n",
              "5459   hotpockets ever wheel fortune hodlbaggins spartantrader like tendency hyperbole happen  find way eventually sell million ev w w annually half toyota really outlandish number         0.016667\n",
              "11982                                                                                                                                                       bullish  ly factory open         0.000000\n",
              "91499                                                                                                                   release  result destroys revenue profit expectation electrek         0.000000\n",
              "86137                          moon river know facetious consider lack little big barron fun mum picture would bet entire portfolio minus  share unless camera socially distant time        -0.014583"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfgzupV5s2av",
        "outputId": "78c70cd2-e23e-4b03-8af4-490483b4feb1"
      },
      "source": [
        "tweets_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(109112, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AN8S2CQos2aw"
      },
      "source": [
        "### 4. Frequency analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "vvtuGDcAs2aw",
        "outputId": "3820a7f8-e6f3-4128-c792-a73762ea6dbc"
      },
      "source": [
        "# plotting counts of tweets for binned sentiment scores\n",
        "bins = [-1.0, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1]\n",
        "tweets_df['bin'] = pd.cut(tweets_df['sentiment_score'], bins)\n",
        "plt.figure(figsize=(16,9))\n",
        "ax = sns.countplot(x=tweets_df['bin'])\n",
        "ax.set_ylabel('Number of Tweets', fontsize=12)\n",
        "ax.set_xlabel('Sentiment Scores', fontsize=12)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha='right', fontsize=14)\n",
        "ax.set_title('Binned Sentiment Scores Tweet Counts', fontsize=20)\n",
        "ax.set_ylim(0, 120000)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bZUwyE2gs2ax"
      },
      "source": [
        "### 5. Calculating daily average sentiment scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UXfRvdlKs2ax",
        "outputId": "a85bf636-b888-4f8e-f493-a005df169f78"
      },
      "source": [
        "daily_sentiment_scores_df = tweets_df[['date', 'sentiment_score']]\n",
        "daily_sentiment_scores_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>sentiment_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>-0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2a31626-dfda-4d08-98c1-fd4dd8ca2d54')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f2a31626-dfda-4d08-98c1-fd4dd8ca2d54 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f2a31626-dfda-4d08-98c1-fd4dd8ca2d54');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        date  sentiment_score\n",
              "0 2020-01-02             0.15\n",
              "1 2020-01-02            -0.20\n",
              "2 2020-01-02             0.20\n",
              "3 2020-01-02             0.00\n",
              "4 2020-01-02             0.75"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "tI6gAX1os2ay",
        "outputId": "05684dfa-8e1f-4a19-f46a-e45d721114db"
      },
      "source": [
        "# daily average sentiment scores\n",
        "daily_sentiment_scores_df = daily_sentiment_scores_df.resample('D', on='date').mean()\n",
        "daily_sentiment_scores_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3e23c8a3-6598-49e2-81a0-98524571b75c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>sentiment_score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>0.099938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>0.111398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>0.073760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-04</th>\n",
              "      <td>0.083606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-05</th>\n",
              "      <td>0.086658</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e23c8a3-6598-49e2-81a0-98524571b75c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3e23c8a3-6598-49e2-81a0-98524571b75c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3e23c8a3-6598-49e2-81a0-98524571b75c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            sentiment_score\n",
              "date                       \n",
              "2020-01-01         0.099938\n",
              "2020-01-02         0.111398\n",
              "2020-01-03         0.073760\n",
              "2020-01-04         0.083606\n",
              "2020-01-05         0.086658"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "-U3uoSfls2ay",
        "outputId": "60a8be34-e962-459c-848f-432bf1ea53ea"
      },
      "source": [
        "# plotting top 10 daily average sentiment scores\n",
        "top_avg_df = daily_sentiment_scores_df.nlargest(columns='sentiment_score', n=10)\n",
        "plt.figure(figsize=(16,5))\n",
        "ax = sns.barplot(data=top_avg_df, x=top_avg_df.index.date, y=top_avg_df['sentiment_score'])\n",
        "ax.set_ylabel('Average Sentiment Score', fontsize=12)\n",
        "ax.set_xlabel('Date', fontsize=12)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha='right', fontsize=12)\n",
        "ax.set_title('Top 10 Daily Average Sentiment Scores', fontsize=22)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "ea91ELrAs2az",
        "outputId": "901eefbe-3052-4de4-96f2-e98c1a4beafa"
      },
      "source": [
        "# plotting bottom 10 daily average sentiment scores\n",
        "bottom_avg_df = daily_sentiment_scores_df.nsmallest(columns='sentiment_score', n=10)\n",
        "plt.figure(figsize=(16,5))\n",
        "ax = sns.barplot(data=bottom_avg_df, x=bottom_avg_df.index.date, y=bottom_avg_df['sentiment_score'])\n",
        "ax.set_ylabel('Average Sentiment Score', fontsize=12)\n",
        "ax.set_xlabel('Date', fontsize=12)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha='right', fontsize=12)\n",
        "ax.set_title('Bottom 10 Daily Average Sentiment Scores', fontsize=22)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdPt-wKzs2az"
      },
      "source": [
        "### 6. Removing tweets with zero sentiment scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "tRqcrLQCs2a0",
        "outputId": "6a990a3b-6268-410f-9b41-763c264faf2c"
      },
      "source": [
        "tweets_df1 = tweets_df.copy()\n",
        "tweets_df1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>date</th>\n",
              "      <th>cleaned_tweet</th>\n",
              "      <th>sentiment_score</th>\n",
              "      <th>bin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1212450794705969152</td>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>fun fact audi register e tron netherlands   sux combine  obviously order magnitude</td>\n",
              "      <td>0.15</td>\n",
              "      <td>(0.0, 0.2]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1212450579634626560</td>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>mf  stock watch shit go damn year miss curve</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>(-0.4, -0.2]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1212450337543602177</td>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>icannot enough june th within day  low reply charts never short lol</td>\n",
              "      <td>0.20</td>\n",
              "      <td>(0.0, 0.2]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1212450309131227141</td>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>gain momentum taiwan ownerstwn     taiwan</td>\n",
              "      <td>0.00</td>\n",
              "      <td>(-0.2, 0.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1212449703318753280</td>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>schuldensuehner extrapolate number another two year consider amp occur ice company scramble survive look like  successful</td>\n",
              "      <td>0.75</td>\n",
              "      <td>(0.6, 0.8]</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-23212244-c1c2-4d21-b5d7-c8ea6cc8b00f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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            "text/plain": [
              "                    id       date  ... sentiment_score           bin\n",
              "0  1212450794705969152 2020-01-02  ...            0.15    (0.0, 0.2]\n",
              "1  1212450579634626560 2020-01-02  ...           -0.20  (-0.4, -0.2]\n",
              "2  1212450337543602177 2020-01-02  ...            0.20    (0.0, 0.2]\n",
              "3  1212450309131227141 2020-01-02  ...            0.00   (-0.2, 0.0]\n",
              "4  1212449703318753280 2020-01-02  ...            0.75    (0.6, 0.8]\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Owy8H_3s2a0",
        "outputId": "46871b59-a128-44a8-bb3f-63ef70bcdad3"
      },
      "source": [
        "# checking tweets with zero sentiment scores\n",
        "(tweets_df1.sentiment_score == 0).sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48917"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wVXKO1mds2a0",
        "outputId": "58fa0df2-6695-4511-9106-48c2904fe59c"
      },
      "source": [
        "# removing tweets with zero sentiment scores\n",
        "tweets_df1 = tweets_df1[tweets_df1.sentiment_score !=0 ]\n",
        "(tweets_df1.sentiment_score == 0).sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nii7LGrTs2a1"
      },
      "source": [
        "# daily average sentiment scores without zero sentiment scores\n",
        "tweets_df1 = tweets_df1.groupby(['date'], as_index=False).mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYVgg_T3s2a1",
        "outputId": "85eaa59c-68c0-4eed-dcc9-eb68473e0b40"
      },
      "source": [
        "# checking for null values\n",
        "tweets_df1.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date               0\n",
              "id                 0\n",
              "sentiment_score    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "ewqMA-ADs2a2",
        "outputId": "29805427-97d4-40f2-c998-5c8df985a4b4"
      },
      "source": [
        "# adding final average sentiment scores\n",
        "daily_sentiment_scores_df['sentiment_score_final'] = tweets_df1.sentiment_score.values\n",
        "daily_sentiment_scores_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-04110092-39b5-48c4-bcaf-46e78c2c3b43\">\n",
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              "    }\n",
              "\n",
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              "        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>sentiment_score</th>\n",
              "      <th>sentiment_score_final</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>0.099938</td>\n",
              "      <td>0.154961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>0.111398</td>\n",
              "      <td>0.161528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>0.073760</td>\n",
              "      <td>0.128297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-04</th>\n",
              "      <td>0.083606</td>\n",
              "      <td>0.144796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-05</th>\n",
              "      <td>0.086658</td>\n",
              "      <td>0.132267</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-04110092-39b5-48c4-bcaf-46e78c2c3b43 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-04110092-39b5-48c4-bcaf-46e78c2c3b43');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            sentiment_score  sentiment_score_final\n",
              "date                                              \n",
              "2020-01-01         0.099938               0.154961\n",
              "2020-01-02         0.111398               0.161528\n",
              "2020-01-03         0.073760               0.128297\n",
              "2020-01-04         0.083606               0.144796\n",
              "2020-01-05         0.086658               0.132267"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIe46jV3s2a2"
      },
      "source": [
        "### 7. Saving sentiment analysis results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTVmp6mqs2a2"
      },
      "source": [
        "daily_sentiment_scores_df.to_csv('daily-avg-sentiment-scores.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "AfZPaMwgGTL4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}