NEG_Word.ipynb

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      "source": [
        "Positive Synonym Generator. "
      ],
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        "id": "uMVdtQXR6a4X"
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      "execution_count": 69,
      "metadata": {
        "id": "GF8fymyL8kT-"
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      "source": [
        "import pandas as pd"
      ]
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      "cell_type": "code",
      "source": [
        "url=\"https://raw.githubusercontent.com/ShethNidhi06/Twitter_Data/main/Tweets.csv\"\n",
        "df1=pd.read_csv(url)\n",
        "df1=df1.dropna()\n",
        "df1.head()"
      ],
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              "       textID                                               text  \\\n",
              "0  cb774db0d1                I`d have responded, if I were going   \n",
              "1  549e992a42      Sooo SAD I will miss you here in San Diego!!!   \n",
              "2  088c60f138                          my boss is bullying me...   \n",
              "3  9642c003ef                     what interview! leave me alone   \n",
              "4  358bd9e861   Sons of ****, why couldn`t they put them on t...   \n",
              "\n",
              "                         selected_text sentiment  \n",
              "0  I`d have responded, if I were going   neutral  \n",
              "1                             Sooo SAD  negative  \n",
              "2                          bullying me  negative  \n",
              "3                       leave me alone  negative  \n",
              "4                        Sons of ****,  negative  "
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              "      <th>1</th>\n",
              "      <td>549e992a42</td>\n",
              "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
              "      <td>Sooo SAD</td>\n",
              "      <td>negative</td>\n",
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              "      <td>what interview! leave me alone</td>\n",
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      "cell_type": "markdown",
      "source": [
        "Creating a DataFrame (df_filtered) containing only negative tweets.\n"
      ],
      "metadata": {
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      "source": [
        "df_filtered = df1[df1['sentiment'] =='negative']\n",
        "df_filtered=df_filtered.reset_index()\n",
        "  \n",
        "# Print the new dataframe\n",
        "print(df_filtered.head())\n",
        "print('Length of dataframe:', len(df_filtered))"
      ],
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          "text": [
            "   index      textID                                               text  \\\n",
            "0      1  549e992a42      Sooo SAD I will miss you here in San Diego!!!   \n",
            "1      2  088c60f138                          my boss is bullying me...   \n",
            "2      3  9642c003ef                     what interview! leave me alone   \n",
            "3      4  358bd9e861   Sons of ****, why couldn`t they put them on t...   \n",
            "4     12  74a76f6e0a       My Sharpie is running DANGERously low on ink   \n",
            "\n",
            "    selected_text sentiment  \n",
            "0        Sooo SAD  negative  \n",
            "1     bullying me  negative  \n",
            "2  leave me alone  negative  \n",
            "3   Sons of ****,  negative  \n",
            "4     DANGERously  negative  \n",
            "Length of dataframe: 7781\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating individual sentiment scores using VADER"
      ],
      "metadata": {
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      "cell_type": "code",
      "source": [
        "!pip install vaderSentiment\n"
      ],
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      },
      "execution_count": 72,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: vaderSentiment in /usr/local/lib/python3.7/dist-packages (3.3.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from vaderSentiment) (2.23.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (3.0.4)\n"
          ]
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    },
    {
      "cell_type": "code",
      "source": [
        "from vaderSentiment.vaderSentiment import *\n",
        "new_words = {\n",
        "    '****':-2.5,\n",
        "}\n",
        "\n",
        "sid_obj = SentimentIntensityAnalyzer()\n",
        "sid_obj.lexicon.update(new_words)\n",
        "\n",
        "def sentiment_scores(text):\n",
        "    sentiments=[]\n",
        "    sentitext= SentiText(text)\n",
        "    words_and_emoticons = sentitext.words_and_emoticons\n",
        "   \n",
        "    for item in words_and_emoticons:\n",
        "            valence = 0\n",
        "            i = words_and_emoticons.index(item)\n",
        "    \n",
        "    \n",
        "\n",
        "            sentiment_dict = sid_obj.sentiment_valence(valence, sentitext, item, i, sentiments)\n",
        "            \n",
        "\n",
        "    return sentiment_dict\n"
      ],
      "metadata": {
        "id": "a3s6tvMfyqrJ"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentence=df_filtered.text[1]\n",
        "print(sentence)\n",
        "print(sentiment_scores(sentence))\n",
        "sentiment_scores(\"****\")"
      ],
      "metadata": {
        "id": "5bVWtnXZWpz9",
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          "base_uri": "https://localhost:8080/"
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      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my boss is bullying me...\n",
            "[0, 0, 0, -2.9, 0]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-2.5]"
            ]
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          "execution_count": 74
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    },
    {
      "cell_type": "code",
      "source": [
        "df_filtered['scores']=df_filtered['text'].map(lambda x:list(sentiment_scores(x)))\n",
        "df_filtered.reset_index()\n",
        "df_filtered.head()\n",
        "#df_filtered.insert(loc=0, column='scores', value=sentiment_scores(df_filtered['text']))\n",
        "\n"
      ],
      "metadata": {
        "id": "08I1iPyA-Kl7",
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          "height": 206
        },
        "outputId": "5b0b19a7-07cf-4a85-a065-b6524093f0f4"
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      "execution_count": 75,
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            "text/plain": [
              "   index      textID                                               text  \\\n",
              "0      1  549e992a42      Sooo SAD I will miss you here in San Diego!!!   \n",
              "1      2  088c60f138                          my boss is bullying me...   \n",
              "2      3  9642c003ef                     what interview! leave me alone   \n",
              "3      4  358bd9e861   Sons of ****, why couldn`t they put them on t...   \n",
              "4     12  74a76f6e0a       My Sharpie is running DANGERously low on ink   \n",
              "\n",
              "    selected_text sentiment                                      scores  \n",
              "0        Sooo SAD  negative      [0, -2.833, 0, 0, -0.6, 0, 0, 0, 0, 0]  \n",
              "1     bullying me  negative                          [0, 0, 0, -2.9, 0]  \n",
              "2  leave me alone  negative                       [0, 0, -0.2, 0, -1.0]  \n",
              "3   Sons of ****,  negative  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  \n",
              "4     DANGERously  negative              [0, 0, 0, 0, -2.0, -1.1, 0, 0]  "
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              "      <td>DANGERously</td>\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-1d4fd424-7691-49b6-b059-655369b8e279 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-1d4fd424-7691-49b6-b059-655369b8e279');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#converting the list of scores to a dictionary={index:score}\n",
        "def dict(list):\n",
        "  dict={}\n",
        "  for i in range(len(list)):\n",
        "    dict[i]=list[i]\n",
        "  return dict\n",
        "df_filtered['dictionary']=df_filtered['scores'].apply(lambda x: dict(x))\n",
        "df_filtered.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "16QqhFvLxsTJ",
        "outputId": "daccd6ac-be46-4ded-a078-b01f4d683e74"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index      textID                                               text  \\\n",
              "0      1  549e992a42      Sooo SAD I will miss you here in San Diego!!!   \n",
              "1      2  088c60f138                          my boss is bullying me...   \n",
              "2      3  9642c003ef                     what interview! leave me alone   \n",
              "3      4  358bd9e861   Sons of ****, why couldn`t they put them on t...   \n",
              "4     12  74a76f6e0a       My Sharpie is running DANGERously low on ink   \n",
              "\n",
              "    selected_text sentiment                                      scores  \\\n",
              "0        Sooo SAD  negative      [0, -2.833, 0, 0, -0.6, 0, 0, 0, 0, 0]   \n",
              "1     bullying me  negative                          [0, 0, 0, -2.9, 0]   \n",
              "2  leave me alone  negative                       [0, 0, -0.2, 0, -1.0]   \n",
              "3   Sons of ****,  negative  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "4     DANGERously  negative              [0, 0, 0, 0, -2.0, -1.1, 0, 0]   \n",
              "\n",
              "                                          dictionary  \n",
              "0  {0: 0, 1: -2.833, 2: 0, 3: 0, 4: -0.6, 5: 0, 6...  \n",
              "1                  {0: 0, 1: 0, 2: 0, 3: -2.9, 4: 0}  \n",
              "2               {0: 0, 1: 0, 2: -0.2, 3: 0, 4: -1.0}  \n",
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              "4  {0: 0, 1: 0, 2: 0, 3: 0, 4: -2.0, 5: -1.1, 6: ...  "
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              "      <td>leave me alone</td>\n",
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              "                                                     [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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#extracting the most negative word in the sentence by sorting the dictionary and returning its index \n",
        "import operator\n",
        "from collections import OrderedDict\n",
        "def sort_dict(dict):\n",
        "  dict2=sorted(dict.items(), key=operator.itemgetter(1))\n",
        "\n",
        "  sorted_dict = OrderedDict()\n",
        "  for k, v in dict2:\n",
        "    sorted_dict[k] = v\n",
        "  first_pair = next(iter((sorted_dict.items())) )\n",
        "  for i in range(len(df_filtered)):\n",
        "    ele=first_pair[i]\n",
        "    return int(ele)\n",
        "\n",
        "\n",
        "df_filtered['neg_word']=df_filtered['dictionary'].apply(lambda x:sort_dict(x))\n",
        "df_filtered=df_filtered.reset_index()\n",
        "df_filtered.head()\n",
        "\n",
        "\n",
        "# def minimum(senti):\n",
        "#   senti.keys.sort()\n",
        "  \n",
        "#   return senti[0]"
      ],
      "metadata": {
        "id": "cnpdVNR49lBY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2c2d4849-a60d-4832-caf5-9cc49121b241"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   level_0  index      textID  \\\n",
              "0        0      1  549e992a42   \n",
              "1        1      2  088c60f138   \n",
              "2        2      3  9642c003ef   \n",
              "3        3      4  358bd9e861   \n",
              "4        4     12  74a76f6e0a   \n",
              "\n",
              "                                                text   selected_text  \\\n",
              "0      Sooo SAD I will miss you here in San Diego!!!        Sooo SAD   \n",
              "1                          my boss is bullying me...     bullying me   \n",
              "2                     what interview! leave me alone  leave me alone   \n",
              "3   Sons of ****, why couldn`t they put them on t...   Sons of ****,   \n",
              "4       My Sharpie is running DANGERously low on ink     DANGERously   \n",
              "\n",
              "  sentiment                                      scores  \\\n",
              "0  negative      [0, -2.833, 0, 0, -0.6, 0, 0, 0, 0, 0]   \n",
              "1  negative                          [0, 0, 0, -2.9, 0]   \n",
              "2  negative                       [0, 0, -0.2, 0, -1.0]   \n",
              "3  negative  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "4  negative              [0, 0, 0, 0, -2.0, -1.1, 0, 0]   \n",
              "\n",
              "                                          dictionary  neg_word  \n",
              "0  {0: 0, 1: -2.833, 2: 0, 3: 0, 4: -0.6, 5: 0, 6...         1  \n",
              "1                  {0: 0, 1: 0, 2: 0, 3: -2.9, 4: 0}         3  \n",
              "2               {0: 0, 1: 0, 2: -0.2, 3: 0, 4: -1.0}         4  \n",
              "3  {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: ...         0  \n",
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              "      <td>negative</td>\n",
              "      <td>[0, 0, -0.2, 0, -1.0]</td>\n",
              "      <td>{0: 0, 1: 0, 2: -0.2, 3: 0, 4: -1.0}</td>\n",
              "      <td>4</td>\n",
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              "      <th>3</th>\n",
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              "      <td>DANGERously</td>\n",
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              "      <td>{0: 0, 1: 0, 2: 0, 3: 0, 4: -2.0, 5: -1.1, 6: ...</td>\n",
              "      <td>4</td>\n",
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              "\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-16a82cc9-f275-431d-818d-12a20e7aba9b 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-16a82cc9-f275-431d-818d-12a20e7aba9b');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#neg=df_filtered['scores'].sort_values()\n",
        "#print(neg[1][0])\n",
        "\n",
        "# type(df_filtered['neg_word'])\n",
        "# df_filtered.neg_word[0][0]"
      ],
      "metadata": {
        "id": "ea3ogrdcIbuy"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# def find_word(series):\n",
        "#   for i in range(len(df_filtered)):\n",
        "#     ele=series[i]\n",
        "#     return int(ele)\n",
        "# df_filtered['ind']=df_filtered['neg_word'].apply(lambda x: find_word(x))\n",
        "# df_filtered.head()\n",
        "index=df_filtered['neg_word'][2]  \n",
        "print(index)\n",
        "print(df_filtered.text[2].split()[index])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9U-JMd8dky6",
        "outputId": "219a64a7-119b-427d-e166-41e7af5aa586"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n",
            "alone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Most negative word in sentence "
      ],
      "metadata": {
        "id": "mPt2G8kV47Bc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " \n",
        "#   for i in range(len(df_filtered)):\n",
        "#     lst=text.split()\n",
        "#     index=df_filtered['neg_word'][i]\n",
        "#     word=lst[index-1]\n",
        "#     return word\n",
        "\n",
        "# for i in range(len(df_filtered)):\n",
        "#   df_filtered['word'][i]=df_filtered['text'][i].split()[df_filtered['neg_word'][i]]\n",
        "\n",
        "def ext_word():\n",
        "  list=[]\n",
        "  for i in range(len(df_filtered)):\n",
        "    lst=df_filtered['text'][i].split()\n",
        "    index=df_filtered['neg_word'][i]\n",
        "    list.append(lst[index])\n",
        "  return list\n",
        "  \n",
        "#store the words in a list and assign it to a new column in the dataframe.\n",
        "word_list=ext_word()\n",
        "df_filtered['word']=word_list\n",
        "df_filtered.head()"
      ],
      "metadata": {
        "id": "WbN2c6__i6CQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "2e8258f2-b51a-49a8-b269-f2a77fee4aeb"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   level_0  index      textID  \\\n",
              "0        0      1  549e992a42   \n",
              "1        1      2  088c60f138   \n",
              "2        2      3  9642c003ef   \n",
              "3        3      4  358bd9e861   \n",
              "4        4     12  74a76f6e0a   \n",
              "\n",
              "                                                text   selected_text  \\\n",
              "0      Sooo SAD I will miss you here in San Diego!!!        Sooo SAD   \n",
              "1                          my boss is bullying me...     bullying me   \n",
              "2                     what interview! leave me alone  leave me alone   \n",
              "3   Sons of ****, why couldn`t they put them on t...   Sons of ****,   \n",
              "4       My Sharpie is running DANGERously low on ink     DANGERously   \n",
              "\n",
              "  sentiment                                      scores  \\\n",
              "0  negative      [0, -2.833, 0, 0, -0.6, 0, 0, 0, 0, 0]   \n",
              "1  negative                          [0, 0, 0, -2.9, 0]   \n",
              "2  negative                       [0, 0, -0.2, 0, -1.0]   \n",
              "3  negative  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "4  negative              [0, 0, 0, 0, -2.0, -1.1, 0, 0]   \n",
              "\n",
              "                                          dictionary  neg_word         word  \n",
              "0  {0: 0, 1: -2.833, 2: 0, 3: 0, 4: -0.6, 5: 0, 6...         1          SAD  \n",
              "1                  {0: 0, 1: 0, 2: 0, 3: -2.9, 4: 0}         3     bullying  \n",
              "2               {0: 0, 1: 0, 2: -0.2, 3: 0, 4: -1.0}         4        alone  \n",
              "3  {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: ...         0         Sons  \n",
              "4  {0: 0, 1: 0, 2: 0, 3: 0, 4: -2.0, 5: -1.1, 6: ...         4  DANGERously  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a0ae65aa-11f1-4d68-b9ac-2f5a4980f11d\">\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>level_0</th>\n",
              "      <th>index</th>\n",
              "      <th>textID</th>\n",
              "      <th>text</th>\n",
              "      <th>selected_text</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>scores</th>\n",
              "      <th>dictionary</th>\n",
              "      <th>neg_word</th>\n",
              "      <th>word</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>549e992a42</td>\n",
              "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
              "      <td>Sooo SAD</td>\n",
              "      <td>negative</td>\n",
              "      <td>[0, -2.833, 0, 0, -0.6, 0, 0, 0, 0, 0]</td>\n",
              "      <td>{0: 0, 1: -2.833, 2: 0, 3: 0, 4: -0.6, 5: 0, 6...</td>\n",
              "      <td>1</td>\n",
              "      <td>SAD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>088c60f138</td>\n",
              "      <td>my boss is bullying me...</td>\n",
              "      <td>bullying me</td>\n",
              "      <td>negative</td>\n",
              "      <td>[0, 0, 0, -2.9, 0]</td>\n",
              "      <td>{0: 0, 1: 0, 2: 0, 3: -2.9, 4: 0}</td>\n",
              "      <td>3</td>\n",
              "      <td>bullying</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9642c003ef</td>\n",
              "      <td>what interview! leave me alone</td>\n",
              "      <td>leave me alone</td>\n",
              "      <td>negative</td>\n",
              "      <td>[0, 0, -0.2, 0, -1.0]</td>\n",
              "      <td>{0: 0, 1: 0, 2: -0.2, 3: 0, 4: -1.0}</td>\n",
              "      <td>4</td>\n",
              "      <td>alone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>358bd9e861</td>\n",
              "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
              "      <td>Sons of ****,</td>\n",
              "      <td>negative</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>{0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: ...</td>\n",
              "      <td>0</td>\n",
              "      <td>Sons</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>74a76f6e0a</td>\n",
              "      <td>My Sharpie is running DANGERously low on ink</td>\n",
              "      <td>DANGERously</td>\n",
              "      <td>negative</td>\n",
              "      <td>[0, 0, 0, 0, -2.0, -1.1, 0, 0]</td>\n",
              "      <td>{0: 0, 1: 0, 2: 0, 3: 0, 4: -2.0, 5: -1.1, 6: ...</td>\n",
              "      <td>4</td>\n",
              "      <td>DANGERously</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0ae65aa-11f1-4d68-b9ac-2f5a4980f11d')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-a0ae65aa-11f1-4d68-b9ac-2f5a4980f11d 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-a0ae65aa-11f1-4d68-b9ac-2f5a4980f11d');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_filtered.to_csv('file1.csv')"
      ],
      "metadata": {
        "id": "Cpj1qsGOHG77"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "uq68oeislAmU"
      },
      "execution_count": 83,
      "outputs": []
    }
  ]
}