Big_Data_Textblob.ipynb

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      "source": [
        "import csv,re\n",
        "from textblob import TextBlob\n",
        "import tweepy\n",
        "import re\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "consumer_key= \"Consumer_key\"\n",
        "consumer_secret= \"Consumer_Secret\"\n",
        "access_token= \"Access_Token\"\n",
        "access_token_secret= \"Access_token_Secret\"\n",
        "auth= tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
        "auth.set_access_token= (access_token, access_token_secret)\n",
        "api= tweepy.API(auth, wait_on_rate_limit=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def func():\n",
        "    tweets = list(tweepy.Cursor(\n",
        "        api.search, \"{0}\".format(\"COVID-19\"), lang=\"en\",tweet_mode='extended', count=13000).items(13000))\n",
        "    Tweet_outpt = []\n",
        "    for i in tweets:\n",
        "        text = i.full_text\n",
        "        place = \"\"\n",
        "        if i.place:\n",
        "            place = i.place.full_name\n",
        "        json_output = {\n",
        "            \"tweet\": text,\n",
        "        }\n",
        "        Tweet_outpt.append(json_output)\n",
        "    return Tweet_outpt\n",
        "final_arr=func()\n",
        "df = pd.DataFrame(final_arr)\n",
        "df"
      ],
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              "                                                   tweet\n",
              "0      RT @LvivTyler: Putin should have been awarded ...\n",
              "1      RT @thebradfordfile: The FL Surgeon General re...\n",
              "2      RT @thebradfordfile: The FL Surgeon General re...\n",
              "3      RT @ikwilson: BREAKING:  From Florida Departme...\n",
              "4      RT @thebradfordfile: The FL Surgeon General re...\n",
              "...                                                  ...\n",
              "12995  RT @yaneerbaryam: \"Researchers believe that CO...\n",
              "12996  RT @Covid19Critical: As many as 50-80% of indi...\n",
              "12997  RT @elisaperego78: Italy's cardiologist and sp...\n",
              "12998  RT @MoriartyLab: The Canadian Thanksgiving COV...\n",
              "12999  RT @ikwilson: BREAKING:  From Florida Departme...\n",
              "\n",
              "[13000 rows x 1 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet</th>\n",
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              "      <th>0</th>\n",
              "      <td>RT @LvivTyler: Putin should have been awarded ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RT @thebradfordfile: The FL Surgeon General re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @thebradfordfile: The FL Surgeon General re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RT @ikwilson: BREAKING:  From Florida Departme...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @thebradfordfile: The FL Surgeon General re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12995</th>\n",
              "      <td>RT @yaneerbaryam: \"Researchers believe that CO...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12996</th>\n",
              "      <td>RT @Covid19Critical: As many as 50-80% of indi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12997</th>\n",
              "      <td>RT @elisaperego78: Italy's cardiologist and sp...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12998</th>\n",
              "      <td>RT @MoriartyLab: The Canadian Thanksgiving COV...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12999</th>\n",
              "      <td>RT @ikwilson: BREAKING:  From Florida Departme...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13000 rows × 1 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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      "cell_type": "code",
      "source": [
        "\n",
        "def cleanTwt(twt):\n",
        "  twt = re.sub('COVID-19','COVID-19',twt)\n",
        "  twt = re.sub('COVID19','COVID19',twt)\n",
        "  twt = re.sub('#[A-Za-z0-9]+', '', twt)\n",
        "  twt = re.sub('\\\\n', '', twt)\n",
        "  twt = re.sub('https?:\\/\\/\\S+', '', twt)\n",
        "  twt = re.sub('@[^\\s]+','',twt)\n",
        "  twt = re.sub('RT','',twt)\n",
        "  twt = re.sub('!','',twt)\n",
        "  \n",
        "  twt = ''.join([i if ord(i) < 128 else '' for i in twt])\n",
        "  return twt\n",
        "\n",
        "\n"
      ],
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        "id": "NlrSBLMi7uMz"
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    {
      "cell_type": "code",
      "source": [
        "#cleaned tweets\n",
        "df['cleaned tweets'] = df['tweet'].apply(cleanTwt)\n",
        "\n",
        "df.head(100)"
      ],
      "metadata": {
        "id": "cmyv6nc_77N5",
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              "                                                tweet  \\\n",
              "0   RT @LvivTyler: Putin should have been awarded ...   \n",
              "1   RT @thebradfordfile: The FL Surgeon General re...   \n",
              "2   RT @thebradfordfile: The FL Surgeon General re...   \n",
              "3   RT @ikwilson: BREAKING:  From Florida Departme...   \n",
              "4   RT @thebradfordfile: The FL Surgeon General re...   \n",
              "..                                                ...   \n",
              "95  RT @thebradfordfile: The FL Surgeon General re...   \n",
              "96  The UKCB’s report, Bereavement is Everybody’s ...   \n",
              "97  RT @thebradfordfile: The FL Surgeon General re...   \n",
              "98  RT @ID_ethics: “This article argues that exten...   \n",
              "99  RT @BrianOSheaSPI: CCP has opened police opera...   \n",
              "\n",
              "                                       cleaned tweets  \n",
              "0     Putin should have been awarded the Nobel Pri...  \n",
              "1     The FL Surgeon General releasing a warning a...  \n",
              "2     The FL Surgeon General releasing a warning a...  \n",
              "3     BREAKING:  From Florida Department of Health...  \n",
              "4     The FL Surgeon General releasing a warning a...  \n",
              "..                                                ...  \n",
              "95    The FL Surgeon General releasing a warning a...  \n",
              "96  The UKCBs report, Bereavement is Everybodys Bu...  \n",
              "97    The FL Surgeon General releasing a warning a...  \n",
              "98    This article argues that extended school clo...  \n",
              "99    CCP has opened police operations in NYC Mean...  \n",
              "\n",
              "[100 rows x 2 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet</th>\n",
              "      <th>cleaned tweets</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>RT @LvivTyler: Putin should have been awarded ...</td>\n",
              "      <td>Putin should have been awarded the Nobel Pri...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RT @thebradfordfile: The FL Surgeon General re...</td>\n",
              "      <td>The FL Surgeon General releasing a warning a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @thebradfordfile: The FL Surgeon General re...</td>\n",
              "      <td>The FL Surgeon General releasing a warning a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RT @ikwilson: BREAKING:  From Florida Departme...</td>\n",
              "      <td>BREAKING:  From Florida Department of Health...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @thebradfordfile: The FL Surgeon General re...</td>\n",
              "      <td>The FL Surgeon General releasing a warning a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>RT @thebradfordfile: The FL Surgeon General re...</td>\n",
              "      <td>The FL Surgeon General releasing a warning a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>The UKCB’s report, Bereavement is Everybody’s ...</td>\n",
              "      <td>The UKCBs report, Bereavement is Everybodys Bu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>RT @thebradfordfile: The FL Surgeon General re...</td>\n",
              "      <td>The FL Surgeon General releasing a warning a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>RT @ID_ethics: “This article argues that exten...</td>\n",
              "      <td>This article argues that extended school clo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>RT @BrianOSheaSPI: CCP has opened police opera...</td>\n",
              "      <td>CCP has opened police operations in NYC Mean...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 2 columns</p>\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",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Text Blob (Data Labeling)\n",
        "\n",
        "def getSubjectivity(twt):\n",
        "  return TextBlob(twt).sentiment.subjectivity\n",
        "\n",
        "def getPolarity(twt):\n",
        "  return TextBlob(twt).sentiment.polarity\n",
        "\n",
        "#creating two columns subjectivity and polarity\n",
        "\n",
        "df['Subjectivity'] = df['cleaned tweets'].apply(getSubjectivity)\n",
        "df['Polarity'] = df['cleaned tweets'].apply(getPolarity)\n",
        "\n",
        "df.head(100)"
      ],
      "metadata": {
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          "height": 424
        },
        "id": "IlJzyCvc8BaC",
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              "                                                tweet  \\\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating a function for sentiment text\n",
        "\n",
        "def getSentiment(score):\n",
        "  if score < 0:\n",
        "    return 'Negative'\n",
        "\n",
        "  elif score == 0:\n",
        "    return 'Netural'\n",
        "\n",
        "  elif score > 0:\n",
        "    return 'Positive'\n"
      ],
      "metadata": {
        "id": "BrwNzltu_e-l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Sentiment'] = df['Polarity'].apply(getSentiment)\n",
        "\n",
        "df.head(10000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ezZI6CdxAUqi",
        "outputId": "a51b473c-b8dc-4440-fd10-3fcf19bd956b"
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-67ed9584-8fde-4d65-8d84-58604d0fe414');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating a scatter plot showing subjectivity and polarity\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "for i in range(0, df.shape[0]):\n",
        "\n",
        "  plt.scatter(df['Polarity'][i], df['Subjectivity'][i], color='Purple')\n",
        "plt.title('Sentimental Analysis Scatter Plot')\n",
        "plt.xlabel('Polarity')\n",
        "plt.ylabel('Subjectivity')\n",
        "plt.show()\n",
        "  "
      ],
      "metadata": {
        "id": "Pd239rVUAWzV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Sentiment'].value_counts().plot(kind= 'bar')\n",
        "plt.title('Sentimental Analysis Scatter Plot')\n",
        "plt.xlabel('Sentiments')\n",
        "plt.ylabel('Number of Tweets')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "oX-gCcLIAeRR",
        "outputId": "fe903c67-7057-4e01-9d8e-701388e8bd71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Sentiment'].value_counts().plot(kind= 'pie')\n",
        "plt.title('Sentimental Analysis')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "vdZWo2PqBT9O",
        "outputId": "9537c102-4311-49f8-b653-3cb03d42ba9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Preprocessing \n",
        "df.head(100)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "psIJXoGxB0eP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#important libraries for preprocessing using NLTK\n",
        "import nltk\n",
        "from nltk import word_tokenize, FreqDist\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "nltk.download\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "from nltk.tokenize import TweetTokenizer\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "from nltk import sent_tokenize, word_tokenize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvTY0BI8DqxE",
        "outputId": "a6365bcb-fb2a-4d43-df7d-d30a4c8e77c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9qtBoEM6LVP9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}