Data_Preprocessing.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)"
      ]
    },
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              "      <td>RT @CreakyJoints: Here’s what you need to know...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @writinguji: hello! just got tested today f...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RT @junhuihaee: #BeTSinMANILA — covid 19 posit...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @byuumie: #BeTSinMANILA — covid 19 positive...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>12995</th>\n",
              "      <td>@AlveyChain @Naur_ALV It's really amazing!\\nCa...</td>\n",
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              "    <tr>\n",
              "      <th>12999</th>\n",
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            "text/plain": [
              "                                                   tweet\n",
              "0      RT @CreakyJoints: Here’s what you need to know...\n",
              "1      RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...\n",
              "2      RT @writinguji: hello! just got tested today f...\n",
              "3      RT @junhuihaee: #BeTSinMANILA — covid 19 posit...\n",
              "4      RT @byuumie: #BeTSinMANILA — covid 19 positive...\n",
              "...                                                  ...\n",
              "12995  @AlveyChain @Naur_ALV It's really amazing!\\nCa...\n",
              "12996  RT @KatePri14608408: Helen has an autoimmune c...\n",
              "12997  With growing COVID-19 infections reported on t...\n",
              "12998  RT @hkfp: [Recap] 4 Hong Kong civil servants a...\n",
              "12999  @YouTube will forever be known as a media outl...\n",
              "\n",
              "[13000 rows x 1 columns]"
            ]
          },
          "execution_count": 58,
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      ],
      "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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      "execution_count": 59,
      "metadata": {
        "id": "bUeu_IUcQVRN"
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      "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",
        "  \n",
        "  return twt\n",
        "\n",
        "\n"
      ]
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              "      <td>RT @CreakyJoints: Here’s what you need to know...</td>\n",
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              "      <th>3</th>\n",
              "      <td>RT @junhuihaee: #BeTSinMANILA — covid 19 posit...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <th>95</th>\n",
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              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>RT @writinguji: hello! just got tested today f...</td>\n",
              "      <td>hello just got tested today for covid 19 and...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>@Taffinexile1 @toadmeister Oooh do enlighten m...</td>\n",
              "      <td>Oooh do enlighten me then. If \"severely rest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...</td>\n",
              "      <td>COVID-19 ALEBE THE SUN MANILAThread to be up...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>Help us develop an early warning system for di...</td>\n",
              "      <td>Help us develop an early warning system for di...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>100 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "                                                tweet  \\\n",
              "0   RT @CreakyJoints: Here’s what you need to know...   \n",
              "1   RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...   \n",
              "2   RT @writinguji: hello! just got tested today f...   \n",
              "3   RT @junhuihaee: #BeTSinMANILA — covid 19 posit...   \n",
              "4   RT @byuumie: #BeTSinMANILA — covid 19 positive...   \n",
              "..                                                ...   \n",
              "95  RT @writinguji: hello! just got tested today f...   \n",
              "96  RT @writinguji: hello! just got tested today f...   \n",
              "97  @Taffinexile1 @toadmeister Oooh do enlighten m...   \n",
              "98  RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...   \n",
              "99  Help us develop an early warning system for di...   \n",
              "\n",
              "                                       cleaned tweets  \n",
              "0     Heres what you need to know about getting th...  \n",
              "1     COVID-19 ALEBE THE SUN MANILAThread to be up...  \n",
              "2     hello just got tested today for covid 19 and...  \n",
              "3       covid 19 positive cases  (see thread if th...  \n",
              "4       covid 19 positive cases  (see thread if th...  \n",
              "..                                                ...  \n",
              "95    hello just got tested today for covid 19 and...  \n",
              "96    hello just got tested today for covid 19 and...  \n",
              "97    Oooh do enlighten me then. If \"severely rest...  \n",
              "98    COVID-19 ALEBE THE SUN MANILAThread to be up...  \n",
              "99  Help us develop an early warning system for di...  \n",
              "\n",
              "[100 rows x 2 columns]"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#cleaned tweets\n",
        "df['cleaned tweets'] = df['tweet'].apply(cleanTwt)\n",
        "\n",
        "df.head(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "a6BgyTZ9bpxp"
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      "outputs": [],
      "source": [
        "\n",
        "df.to_csv('tweets.csv')"
      ]
    },
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              "    .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>tweet</th>\n",
              "      <th>cleaned tweets</th>\n",
              "      <th>Subjectivity</th>\n",
              "      <th>Polarity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RT @CreakyJoints: Here’s what you need to know...</td>\n",
              "      <td>Heres what you need to know about getting th...</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...</td>\n",
              "      <td>COVID-19 ALEBE THE SUN MANILAThread to be up...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @writinguji: hello! just got tested today f...</td>\n",
              "      <td>hello just got tested today for covid 19 and...</td>\n",
              "      <td>0.311189</td>\n",
              "      <td>0.113636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RT @junhuihaee: #BeTSinMANILA — covid 19 posit...</td>\n",
              "      <td>covid 19 positive cases  (see thread if th...</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.227273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @byuumie: #BeTSinMANILA — covid 19 positive...</td>\n",
              "      <td>covid 19 positive cases  (see thread if th...</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.227273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>RT @writinguji: hello! just got tested today f...</td>\n",
              "      <td>hello just got tested today for covid 19 and...</td>\n",
              "      <td>0.311189</td>\n",
              "      <td>0.113636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>RT @writinguji: hello! just got tested today f...</td>\n",
              "      <td>hello just got tested today for covid 19 and...</td>\n",
              "      <td>0.311189</td>\n",
              "      <td>0.113636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>@Taffinexile1 @toadmeister Oooh do enlighten m...</td>\n",
              "      <td>Oooh do enlighten me then. If \"severely rest...</td>\n",
              "      <td>0.601389</td>\n",
              "      <td>0.187500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...</td>\n",
              "      <td>COVID-19 ALEBE THE SUN MANILAThread to be up...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>Help us develop an early warning system for di...</td>\n",
              "      <td>Help us develop an early warning system for di...</td>\n",
              "      <td>0.418750</td>\n",
              "      <td>0.009375</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22a9a141-6674-44ed-80c0-f01a1e93bd47')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-22a9a141-6674-44ed-80c0-f01a1e93bd47');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                tweet  \\\n",
              "0   RT @CreakyJoints: Here’s what you need to know...   \n",
              "1   RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...   \n",
              "2   RT @writinguji: hello! just got tested today f...   \n",
              "3   RT @junhuihaee: #BeTSinMANILA — covid 19 posit...   \n",
              "4   RT @byuumie: #BeTSinMANILA — covid 19 positive...   \n",
              "..                                                ...   \n",
              "95  RT @writinguji: hello! just got tested today f...   \n",
              "96  RT @writinguji: hello! just got tested today f...   \n",
              "97  @Taffinexile1 @toadmeister Oooh do enlighten m...   \n",
              "98  RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...   \n",
              "99  Help us develop an early warning system for di...   \n",
              "\n",
              "                                       cleaned tweets  Subjectivity  Polarity  \n",
              "0     Heres what you need to know about getting th...      0.900000  0.500000  \n",
              "1     COVID-19 ALEBE THE SUN MANILAThread to be up...      0.000000  0.000000  \n",
              "2     hello just got tested today for covid 19 and...      0.311189  0.113636  \n",
              "3       covid 19 positive cases  (see thread if th...      0.545455  0.227273  \n",
              "4       covid 19 positive cases  (see thread if th...      0.545455  0.227273  \n",
              "..                                                ...           ...       ...  \n",
              "95    hello just got tested today for covid 19 and...      0.311189  0.113636  \n",
              "96    hello just got tested today for covid 19 and...      0.311189  0.113636  \n",
              "97    Oooh do enlighten me then. If \"severely rest...      0.601389  0.187500  \n",
              "98    COVID-19 ALEBE THE SUN MANILAThread to be up...      0.000000  0.000000  \n",
              "99  Help us develop an early warning system for di...      0.418750  0.009375  \n",
              "\n",
              "[100 rows x 4 columns]"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "2TVfqtiWYaLy"
      },
      "outputs": [],
      "source": [
        "#creating a function for sentiment text\n",
        "\n",
        "def getSentiment(score):\n",
        "  if score < 0:\n",
        "    return 0\n",
        "\n",
        "  elif score == 0:\n",
        "    return 2\n",
        "  elif score > 0:\n",
        "    return 1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "C13vwRhqYekA",
        "outputId": "b901f7fa-71fb-4e66-8fe5-c6ab9536a903"
      },
      "outputs": [
        {
          "data": {
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              "      <th>0</th>\n",
              "      <td>RT @CreakyJoints: Here’s what you need to know...</td>\n",
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              "      <th>3</th>\n",
              "      <td>RT @junhuihaee: #BeTSinMANILA — covid 19 posit...</td>\n",
              "      <td>covid 19 positive cases  (see thread if th...</td>\n",
              "      <td>0.545455</td>\n",
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              "      <td>RT @byuumie: #BeTSinMANILA — covid 19 positive...</td>\n",
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              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>For example, the stresses induced by the COVID...</td>\n",
              "      <td>For example, the stresses induced by the COVID...</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>-0.066667</td>\n",
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              "      <th>9996</th>\n",
              "      <td>RT @FLSurgeonGen: Today, we released an analys...</td>\n",
              "      <td>Today, we released an analysis on COVID-19 m...</td>\n",
              "      <td>0.158333</td>\n",
              "      <td>0.125000</td>\n",
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              "      <th>9997</th>\n",
              "      <td>RT @FLSurgeonGen: Today, we released an analys...</td>\n",
              "      <td>Today, we released an analysis on COVID-19 m...</td>\n",
              "      <td>0.158333</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>RT @cbs_ke: The Nairobi Securities Exchange (N...</td>\n",
              "      <td>The Nairobi Securities Exchange (NSE) has re...</td>\n",
              "      <td>0.095833</td>\n",
              "      <td>-0.062500</td>\n",
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              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>RT @TRyanGregory: Since 2020, COVID-19 has res...</td>\n",
              "      <td>Since 2020, COVID-19 has resulted in 1.06 mi...</td>\n",
              "      <td>0.166667</td>\n",
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              "      <td>1</td>\n",
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            "text/plain": [
              "                                                  tweet  \\\n",
              "0     RT @CreakyJoints: Here’s what you need to know...   \n",
              "1     RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...   \n",
              "2     RT @writinguji: hello! just got tested today f...   \n",
              "3     RT @junhuihaee: #BeTSinMANILA — covid 19 posit...   \n",
              "4     RT @byuumie: #BeTSinMANILA — covid 19 positive...   \n",
              "...                                                 ...   \n",
              "9995  For example, the stresses induced by the COVID...   \n",
              "9996  RT @FLSurgeonGen: Today, we released an analys...   \n",
              "9997  RT @FLSurgeonGen: Today, we released an analys...   \n",
              "9998  RT @cbs_ke: The Nairobi Securities Exchange (N...   \n",
              "9999  RT @TRyanGregory: Since 2020, COVID-19 has res...   \n",
              "\n",
              "                                         cleaned tweets  Subjectivity  \\\n",
              "0       Heres what you need to know about getting th...      0.900000   \n",
              "1       COVID-19 ALEBE THE SUN MANILAThread to be up...      0.000000   \n",
              "2       hello just got tested today for covid 19 and...      0.311189   \n",
              "3         covid 19 positive cases  (see thread if th...      0.545455   \n",
              "4         covid 19 positive cases  (see thread if th...      0.545455   \n",
              "...                                                 ...           ...   \n",
              "9995  For example, the stresses induced by the COVID...      0.133333   \n",
              "9996    Today, we released an analysis on COVID-19 m...      0.158333   \n",
              "9997    Today, we released an analysis on COVID-19 m...      0.158333   \n",
              "9998    The Nairobi Securities Exchange (NSE) has re...      0.095833   \n",
              "9999    Since 2020, COVID-19 has resulted in 1.06 mi...      0.166667   \n",
              "\n",
              "      Polarity  Sentiment  \n",
              "0     0.500000          1  \n",
              "1     0.000000          2  \n",
              "2     0.113636          1  \n",
              "3     0.227273          1  \n",
              "4     0.227273          1  \n",
              "...        ...        ...  \n",
              "9995 -0.066667          0  \n",
              "9996  0.125000          1  \n",
              "9997  0.125000          1  \n",
              "9998 -0.062500          0  \n",
              "9999  0.166667          1  \n",
              "\n",
              "[10000 rows x 5 columns]"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Sentiment'] = df['Polarity'].apply(getSentiment)\n",
        "\n",
        "df.head(10000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "DNzkHJbtamaw"
      },
      "outputs": [],
      "source": [
        "tweets=pd.read_csv(\"tweets.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
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      "outputs": [
        {
          "name": "stderr",
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          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
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              "   Unnamed: 0                                              tweet  \\\n",
              "0           0  RT @CreakyJoints: Here’s what you need to know...   \n",
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              "4           4  RT @byuumie: #BeTSinMANILA — covid 19 positive...   \n",
              "\n",
              "                                      cleaned tweets  Positive  Negative  \\\n",
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              "\n",
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              "4    0.820    0.6351  "
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Data_Labeling\n",
        "import nltk\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "nltk.download(\"vader_lexicon\")\n",
        "sentiments = SentimentIntensityAnalyzer()\n",
        "tweets[\"Positive\"] = [sentiments.polarity_scores(i)[\"pos\"] for i in tweets[\"tweet\"]]\n",
        "tweets[\"Negative\"] = [sentiments.polarity_scores(i)[\"neg\"] for i in tweets[\"tweet\"]]\n",
        "tweets[\"Neutral\"] = [sentiments.polarity_scores(i)[\"neu\"] for i in tweets[\"tweet\"]]\n",
        "tweets['Compound'] = [sentiments.polarity_scores(i)[\"compound\"] for i in tweets[\"tweet\"]]\n",
        "tweets.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
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        "id": "SgunLnOGdQQT",
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      "outputs": [
        {
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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",
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              "  "
            ],
            "text/plain": [
              "    Unnamed: 0                                              tweet  \\\n",
              "0            0  RT @CreakyJoints: Here’s what you need to know...   \n",
              "1            1  RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...   \n",
              "2            2  RT @writinguji: hello! just got tested today f...   \n",
              "3            3  RT @junhuihaee: #BeTSinMANILA — covid 19 posit...   \n",
              "4            4  RT @byuumie: #BeTSinMANILA — covid 19 positive...   \n",
              "..         ...                                                ...   \n",
              "95          95  RT @writinguji: hello! just got tested today f...   \n",
              "96          96  RT @writinguji: hello! just got tested today f...   \n",
              "97          97  @Taffinexile1 @toadmeister Oooh do enlighten m...   \n",
              "98          98  RT @hongjeons: COVID-19 ALERT\\nBE THE SUN MANI...   \n",
              "99          99  Help us develop an early warning system for di...   \n",
              "\n",
              "                                       cleaned tweets  Positive  Negative  \\\n",
              "0     Heres what you need to know about getting th...     0.000     0.120   \n",
              "1     COVID-19 ALEBE THE SUN MANILAThread to be up...     0.155     0.000   \n",
              "2     hello just got tested today for covid 19 and...     0.135     0.000   \n",
              "3       covid 19 positive cases  (see thread if th...     0.189     0.000   \n",
              "4       covid 19 positive cases  (see thread if th...     0.180     0.000   \n",
              "..                                                ...       ...       ...   \n",
              "95    hello just got tested today for covid 19 and...     0.135     0.000   \n",
              "96    hello just got tested today for covid 19 and...     0.135     0.000   \n",
              "97    Oooh do enlighten me then. If \"severely rest...     0.162     0.197   \n",
              "98    COVID-19 ALEBE THE SUN MANILAThread to be up...     0.155     0.000   \n",
              "99  Help us develop an early warning system for di...     0.188     0.077   \n",
              "\n",
              "    Neutral  Compound Sentiment  \n",
              "0     0.880   -0.3818  Negative  \n",
              "1     0.845    0.4466  Positive  \n",
              "2     0.865    0.5983  Positive  \n",
              "3     0.811    0.6351  Positive  \n",
              "4     0.820    0.6351  Positive  \n",
              "..      ...       ...       ...  \n",
              "95    0.865    0.5983  Positive  \n",
              "96    0.865    0.5983  Positive  \n",
              "97    0.641   -0.2406  Negative  \n",
              "98    0.845    0.4466  Positive  \n",
              "99    0.735    0.5423  Positive  \n",
              "\n",
              "[100 rows x 8 columns]"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "score = tweets[\"Compound\"].values\n",
        "sentiment = []\n",
        "for i in score:\n",
        "    if i > 0 :\n",
        "        sentiment.append('Positive')\n",
        "    elif i < 0 :\n",
        "        sentiment.append('Negative')\n",
        "    else:\n",
        "        sentiment.append('Neutral')\n",
        "tweets[\"Sentiment\"] = sentiment\n",
        "tweets.head(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1z7zS00d6vP",
        "outputId": "4527d3fb-af51-4561-a83a-8243a431ba03"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Positive    6421\n",
            "Neutral     3632\n",
            "Negative    2947\n",
            "Name: Sentiment, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(tweets[\"Sentiment\"].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "6K_05quSd-Vb"
      },
      "outputs": [],
      "source": [
        "#Output_Labeling\n",
        "tweets.to_csv(\"Labeled_Data.csv\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4uVRY4Qne8aF",
        "outputId": "ee25213b-23d8-4db8-8b27-8d100aaa100a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(13000, 8)"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tweets.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMby9ZCje_ao",
        "outputId": "3e74cc39-68c3-467e-c8d7-7505357e5b23"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LIbG2wRefLPd",
        "outputId": "98161bf9-ae84-4bbb-b1fe-baf895e3bec0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        [, , Heres, what, you, need, to, know, about, ...\n",
              "1        [, , COVID-19, ALEBE, THE, SUN, MANILAThread, ...\n",
              "2        [, , hello, just, got, tested, today, for, cov...\n",
              "3        [, , , , covid, 19, positive, cases, , (see, t...\n",
              "4        [, , , , covid, 19, positive, cases, , (see, t...\n",
              "                               ...                        \n",
              "12995    [, , It's, really, amazingCause, of, COVID-19,...\n",
              "12996    [, , Helen, has, an, autoimmune, condition, bu...\n",
              "12997    [With, growing, COVID-19, infections, reported...\n",
              "12998    [, , [Recap], 4, Hong, Kong, civil, servants, ...\n",
              "12999    [, will, forever, be, known, as, a, media, out...\n",
              "Name: cleaned tweets, Length: 13000, dtype: object"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Splitting Data \n",
        "df=pd.DataFrame(tweets['cleaned tweets'])\n",
        "split_data = df[\"cleaned tweets\"].str.split(\" \")\n",
        "split_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkDRiCIQfO7x",
        "outputId": "b9334529-5970-4073-e256-9b8206d0c82b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[], [], ['hello'], ['just'], ['got'], ['tested'], ['today'], ['for'], ['covid'], ['19'], ['and'], ['it'], ['came'], ['out'], ['positive.'], ['I'], ['attended'], ['Be'], ['the'], ['Sun'], ['in'], ['Manila'], ['Day'], ['1'], ['at'], ['LBA'], ['Regular,'], ['2']]\n"
          ]
        }
      ],
      "source": [
        "##Tokenization\n",
        "\n",
        "data=split_data\n",
        "s_new = []\n",
        "def tokenization_s(data): \n",
        "    for sent in (data[:][2]): \n",
        "        s_token = sent_tokenize(sent)\n",
        "        if s_token != '':\n",
        "            s_new.append(s_token)\n",
        "    return s_new\n",
        "\n",
        "print(tokenization_s(data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "b_pODanMfjYJ"
      },
      "outputs": [],
      "source": [
        "#Tokenization\n",
        "\n",
        "df1=pd.DataFrame(tweets['cleaned tweets'])\n",
        "df1[\"Sentiment\"]=tweets[\"Sentiment\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4fdWvz5Cf7dB",
        "outputId": "808584cb-47e3-4e19-8d34-f43578d4b9ba"
      },
      "outputs": [
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              "<p>11000 rows × 2 columns</p>\n",
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            "text/plain": [
              "                                          cleaned tweets Sentiment\n",
              "0        Heres what you need to know about getting th...  Negative\n",
              "1        COVID-19 ALEBE THE SUN MANILAThread to be up...  Positive\n",
              "2        hello just got tested today for covid 19 and...  Positive\n",
              "3          covid 19 positive cases  (see thread if th...  Positive\n",
              "4          covid 19 positive cases  (see thread if th...  Positive\n",
              "...                                                  ...       ...\n",
              "10995    Yankee Candle is warning consumers that if y...  Negative\n",
              "10996      Why isnt she urgently messaging to mask to...  Positive\n",
              "10997   Tissue-specific pathway activities: A retrosp...   Neutral\n",
              "10998    More and more articles coming out every day ...  Positive\n",
              "10999    41-year-old Louisiana congressman-elect Luke...  Negative\n",
              "\n",
              "[11000 rows x 2 columns]"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1['cleaned tweets'].dropna(inplace=True)\n",
        "df1['cleaned tweets'] = df1['cleaned tweets'].astype(str)\n",
        "df1.head(11000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
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        {
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              "      <td>[covid, positive, cases, see, thread, if, ther...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12995</th>\n",
              "      <td>[It, really, amazingCause, of, I, wo, go]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12996</th>\n",
              "      <td>[Helen, has, an, autoimmune, condition, but, t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12997</th>\n",
              "      <td>[With, growing, infections, reported, on, the,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12998</th>\n",
              "      <td>[Recap, Hong, Kong, civil, servants, arrested,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12999</th>\n",
              "      <td>[will, forever, be, known, as, a, media, outle...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13000 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b111c9ce-9177-4d39-9f38-8bfd13d366d7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b111c9ce-9177-4d39-9f38-8bfd13d366d7');\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",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                            tokenization\n",
              "0      [Heres, what, you, need, to, know, about, gett...\n",
              "1      [ALEBE, THE, SUN, MANILAThread, to, be, update...\n",
              "2      [hello, just, got, tested, today, for, covid, ...\n",
              "3      [covid, positive, cases, see, thread, if, ther...\n",
              "4      [covid, positive, cases, see, thread, if, ther...\n",
              "...                                                  ...\n",
              "12995          [It, really, amazingCause, of, I, wo, go]\n",
              "12996  [Helen, has, an, autoimmune, condition, but, t...\n",
              "12997  [With, growing, infections, reported, on, the,...\n",
              "12998  [Recap, Hong, Kong, civil, servants, arrested,...\n",
              "12999  [will, forever, be, known, as, a, media, outle...\n",
              "\n",
              "[13000 rows x 1 columns]"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def tokenize(column):\n",
        "    tokens = nltk.word_tokenize(column)\n",
        "    return [w for w in tokens if w.isalpha()] \n",
        "df1['tokenization'] = df1.apply(lambda x: tokenize(x['cleaned tweets']), axis=1)\n",
        "df1[['tokenization']].head(13000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "WYDlAQD5giqP"
      },
      "outputs": [],
      "source": [
        "#Stemming\n",
        "from nltk.stem.snowball import SnowballStemmer \n",
        "stemmer = SnowballStemmer(\"english\")\n",
        "df1['stemming'] = df1['tokenization'].apply(lambda x: [stemmer.stem(y) for y in x])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "-HSj31aNg-xg",
        "outputId": "7d7eb085-d608-4ab8-98a6-09ab67381e8c"
      },
      "outputs": [
        {
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              "      <th>12998</th>\n",
              "      <td>[Recap] 4 Hong Kong civil servants arrested ...</td>\n",
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              "      <td>[will, forev, be, known, as, a, media, outlet,...</td>\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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            "text/plain": [
              "                                          cleaned tweets Sentiment  \\\n",
              "0        Heres what you need to know about getting th...  Negative   \n",
              "1        COVID-19 ALEBE THE SUN MANILAThread to be up...  Positive   \n",
              "2        hello just got tested today for covid 19 and...  Positive   \n",
              "3          covid 19 positive cases  (see thread if th...  Positive   \n",
              "4          covid 19 positive cases  (see thread if th...  Positive   \n",
              "...                                                  ...       ...   \n",
              "12995    It's really amazingCause of COVID-19,I won't...  Positive   \n",
              "12996    Helen has an autoimmune condition but the co...   Neutral   \n",
              "12997  With growing COVID-19 infections reported on t...  Positive   \n",
              "12998    [Recap] 4 Hong Kong civil servants arrested ...  Negative   \n",
              "12999   will forever be known as a media outlet that ...  Positive   \n",
              "\n",
              "                                            tokenization  \\\n",
              "0      [Heres, what, you, need, to, know, about, gett...   \n",
              "1      [ALEBE, THE, SUN, MANILAThread, to, be, update...   \n",
              "2      [hello, just, got, tested, today, for, covid, ...   \n",
              "3      [covid, positive, cases, see, thread, if, ther...   \n",
              "4      [covid, positive, cases, see, thread, if, ther...   \n",
              "...                                                  ...   \n",
              "12995          [It, really, amazingCause, of, I, wo, go]   \n",
              "12996  [Helen, has, an, autoimmune, condition, but, t...   \n",
              "12997  [With, growing, infections, reported, on, the,...   \n",
              "12998  [Recap, Hong, Kong, civil, servants, arrested,...   \n",
              "12999  [will, forever, be, known, as, a, media, outle...   \n",
              "\n",
              "                                                stemming  \n",
              "0      [here, what, you, need, to, know, about, get, ...  \n",
              "1      [aleb, the, sun, manilathread, to, be, updat, ...  \n",
              "2      [hello, just, got, test, today, for, covid, an...  \n",
              "3      [covid, posit, case, see, thread, if, there, a...  \n",
              "4      [covid, posit, case, see, thread, if, there, a...  \n",
              "...                                                  ...  \n",
              "12995           [it, realli, amazingcaus, of, i, wo, go]  \n",
              "12996  [helen, has, an, autoimmun, condit, but, the, ...  \n",
              "12997  [with, grow, infect, report, on, the, first, w...  \n",
              "12998  [recap, hong, kong, civil, servant, arrest, fo...  \n",
              "12999  [will, forev, be, known, as, a, media, outlet,...  \n",
              "\n",
              "[13000 rows x 4 columns]"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHQ8Lg9vU0J0",
        "outputId": "f0dae466-e80c-4725-b4d6-22cbba0a3f27"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "#Lemmatization\n",
        "import nltk\n",
        "nltk.download('omw-1.4')\n",
        "w_tokenizer = nltk.tokenize.WhitespaceTokenizer()\n",
        "lemmatizer = nltk.stem.WordNetLemmatizer()\n",
        "def lemmatize_text(text):\n",
        "    return [lemmatizer.lemmatize(w) for w in w_tokenizer.tokenize(text)]\n",
        "df1['text_lemmatized'] = df1[\"cleaned tweets\"].apply(lemmatize_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "DzYokctUAzPa",
        "outputId": "520fba27-0fa6-4444-c942-f64d6518b778"
      },
      "outputs": [
        {
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              "      <td>Heres what you need to know about getting th...</td>\n",
              "      <td>Negative</td>\n",
              "      <td>[Heres, what, you, need, to, know, about, gett...</td>\n",
              "      <td>[here, what, you, need, to, know, about, get, ...</td>\n",
              "      <td>[Heres, what, you, need, to, know, about, gett...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>COVID-19 ALEBE THE SUN MANILAThread to be up...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>[ALEBE, THE, SUN, MANILAThread, to, be, update...</td>\n",
              "      <td>[aleb, the, sun, manilathread, to, be, updat, ...</td>\n",
              "      <td>[COVID-19, ALEBE, THE, SUN, MANILAThread, to, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>hello just got tested today for covid 19 and...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>[hello, just, got, tested, today, for, covid, ...</td>\n",
              "      <td>[hello, just, got, test, today, for, covid, an...</td>\n",
              "      <td>[hello, just, got, tested, today, for, covid, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>covid 19 positive cases  (see thread if th...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>[covid, positive, cases, see, thread, if, ther...</td>\n",
              "      <td>[covid, posit, case, see, thread, if, there, a...</td>\n",
              "      <td>[covid, 19, positive, case, (see, thread, if, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>covid 19 positive cases  (see thread if th...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>[covid, positive, cases, see, thread, if, ther...</td>\n",
              "      <td>[covid, posit, case, see, thread, if, there, a...</td>\n",
              "      <td>[covid, 19, positive, case, (see, thread, if, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "                                      cleaned tweets Sentiment  \\\n",
              "0    Heres what you need to know about getting th...  Negative   \n",
              "1    COVID-19 ALEBE THE SUN MANILAThread to be up...  Positive   \n",
              "2    hello just got tested today for covid 19 and...  Positive   \n",
              "3      covid 19 positive cases  (see thread if th...  Positive   \n",
              "4      covid 19 positive cases  (see thread if th...  Positive   \n",
              "\n",
              "                                        tokenization  \\\n",
              "0  [Heres, what, you, need, to, know, about, gett...   \n",
              "1  [ALEBE, THE, SUN, MANILAThread, to, be, update...   \n",
              "2  [hello, just, got, tested, today, for, covid, ...   \n",
              "3  [covid, positive, cases, see, thread, if, ther...   \n",
              "4  [covid, positive, cases, see, thread, if, ther...   \n",
              "\n",
              "                                            stemming  \\\n",
              "0  [here, what, you, need, to, know, about, get, ...   \n",
              "1  [aleb, the, sun, manilathread, to, be, updat, ...   \n",
              "2  [hello, just, got, test, today, for, covid, an...   \n",
              "3  [covid, posit, case, see, thread, if, there, a...   \n",
              "4  [covid, posit, case, see, thread, if, there, a...   \n",
              "\n",
              "                                     text_lemmatized  \n",
              "0  [Heres, what, you, need, to, know, about, gett...  \n",
              "1  [COVID-19, ALEBE, THE, SUN, MANILAThread, to, ...  \n",
              "2  [hello, just, got, tested, today, for, covid, ...  \n",
              "3  [covid, 19, positive, case, (see, thread, if, ...  \n",
              "4  [covid, 19, positive, case, (see, thread, if, ...  "
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSEGRggzhMUS",
        "outputId": "07156f15-ae92-43f9-fd98-6f284411a838"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "#stop words\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "from nltk.tokenize import word_tokenize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "aGKwkFONhxz4"
      },
      "outputs": [],
      "source": [
        "filtered_word_list =tweets['cleaned tweets'] #make a copy of the word_list\n",
        "for tweet in tweets: # iterate over word_list\n",
        "  if tweet in stopwords.words('english'): \n",
        "    filtered_word_list.remove(word)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaaIAHDGic_D",
        "outputId": "38a494ed-da0f-443a-e241-ddbfc379dbf6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0          Heres what you need to know about getting th...\n",
              "1          COVID-19 ALEBE THE SUN MANILAThread to be up...\n",
              "2          hello just got tested today for covid 19 and...\n",
              "3            covid 19 positive cases  (see thread if th...\n",
              "4            covid 19 positive cases  (see thread if th...\n",
              "                               ...                        \n",
              "12995      It's really amazingCause of COVID-19,I won't...\n",
              "12996      Helen has an autoimmune condition but the co...\n",
              "12997    With growing COVID-19 infections reported on t...\n",
              "12998      [Recap] 4 Hong Kong civil servants arrested ...\n",
              "12999     will forever be known as a media outlet that ...\n",
              "Name: cleaned tweets, Length: 13000, dtype: object"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "filtered_word_list\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "Ey3u3F_CkDlp"
      },
      "outputs": [],
      "source": [
        "#CountVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "count_vectorize = CountVectorizer() \n",
        "vectorized = count_vectorize.fit_transform(df1[\"cleaned tweets\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d10yClzlEAZN",
        "outputId": "4f855adc-7cc0-4cba-8b34-189d2368cb35"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  (0, 4307)\t1\n",
            "  (0, 9759)\t1\n",
            "  (0, 9991)\t1\n",
            "  (0, 6059)\t1\n",
            "  (0, 9032)\t1\n",
            "  (0, 5103)\t1\n",
            "  (0, 544)\t1\n",
            "  (0, 3999)\t1\n",
            "  (0, 8901)\t1\n",
            "  (0, 5194)\t1\n",
            "  (0, 2418)\t1\n",
            "  (0, 135)\t1\n",
            "  (0, 1503)\t1\n",
            "  (0, 882)\t1\n",
            "  (0, 3774)\t1\n",
            "  (0, 8156)\t1\n",
            "  (0, 8956)\t1\n",
            "  (0, 3586)\t1\n",
            "['00' '000' '0000' ... 'zubair' 'zulu' 'zweli']\n",
            "youre 0\n"
          ]
        }
      ],
      "source": [
        "print(vectorized[0,:])\n",
        "\n",
        "#print(vc.vocabulary_)\n",
        "print(count_vectorize.get_feature_names_out())\n",
        "print(count_vectorize.get_feature_names_out()[10000], vectorized[0,10000])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "xd9hP8tEEIuX"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(df1,df1[\"Sentiment\"],test_size = 0.2, random_state =26105111)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jyb033PMI6k6",
        "outputId": "163d1f3b-fd9a-4d38-a347-9079703d3fe3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "execution_count": 97,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "clf = MultinomialNB()\n",
        "vectorized = count_vectorize.fit_transform(X_train[\"cleaned tweets\"])\n",
        "clf.fit(vectorized, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "3mNpb6E5JCRh"
      },
      "outputs": [],
      "source": [
        "vectorized = count_vectorize.transform(X_test[\"cleaned tweets\"])\n",
        "res=clf.predict(vectorized)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_-JWKvpLgHh",
        "outputId": "d721353d-22a3-4407-eb77-55f964102b1c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8507692307692307"
            ]
          },
          "execution_count": 100,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "metrics.accuracy_score(res, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "tHfS70h-Nsct",
        "outputId": "cbf1f85d-4a9c-4c83-a71c-dc4cd922ad4e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns; sns.set()\n",
        "mat = confusion_matrix(y_test, res)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[\"pos\",\"neg\",\"neu\"], yticklabels=[\"pos\",\"neg\",\"neu\"])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RTlCasjPOmJ6",
        "outputId": "fd9f488c-1346-4fed-a97e-3b5373f8d43a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "BernoulliNB()"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.naive_bayes import BernoulliNB\n",
        "clf = BernoulliNB()\n",
        "vectorized = count_vectorize.fit_transform(X_train[\"cleaned tweets\"])\n",
        "clf.fit(vectorized, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "ot2m1nODPWmH"
      },
      "outputs": [],
      "source": [
        "vectorized = count_vectorize.transform(X_test[\"cleaned tweets\"])\n",
        "res=clf.predict(vectorized)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lyzNAB1PXX_",
        "outputId": "a6d4fddb-487b-49eb-91dd-4ad0cdd0857f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Negative', 'Neutral', 'Positive', ..., 'Neutral', 'Positive',\n",
              "       'Positive'], dtype='<U8')"
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "rrT_9AxDPYUD",
        "outputId": "858ef380-8b89-418b-cb59-4528c1ce486e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(89.18, 0.5, 'predicted label')"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns; sns.set()\n",
        "mat = confusion_matrix(y_test, res)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[\"pos\",\"neg\",\"neu\"], yticklabels=[\"pos\",\"neg\",\"neu\"])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m8jvkBqXPa7D",
        "outputId": "0d5e9d3c-14ed-4062-fe33-21bf2f989714"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8530769230769231"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "metrics.accuracy_score(res, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BW266EeZPf4T",
        "outputId": "71132cc6-8cbc-4d53-a0a1-b9a3add0cca8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logisticRegr = LogisticRegression()\n",
        "vectorized = count_vectorize.fit_transform(X_train[\"cleaned tweets\"])\n",
        "logisticRegr.fit(vectorized, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "Vx91BaYYQD6m"
      },
      "outputs": [],
      "source": [
        "vectorized = count_vectorize.transform(X_test[\"cleaned tweets\"])\n",
        "res=logisticRegr.predict(vectorized)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zJb-AhaQJFI",
        "outputId": "d5334172-664f-4f03-b3e6-d7df99bd058d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Positive', 'Neutral', 'Positive', ..., 'Neutral', 'Positive',\n",
              "       'Positive'], dtype=object)"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTJEkpRlQKqE",
        "outputId": "90963125-c2ef-40c3-99bf-06aa6ae46b52"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9338461538461539"
            ]
          },
          "execution_count": 111,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "metrics.accuracy_score(res, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "S12nJArWQRT0",
        "outputId": "1c8c0904-3661-43d2-8f38-66e430840b2a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(89.18, 0.5, 'predicted label')"
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "mat = confusion_matrix(y_test, res)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[\"pos\",\"neg\",\"neu\"], yticklabels=[\"pos\",\"neg\",\"neu\"])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3AilSfElQUlU",
        "outputId": "368f964a-130b-423d-c2dd-50f524bb0dac"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:1208: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "LinearSVC()"
            ]
          },
          "execution_count": 113,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "clf=LinearSVC()\n",
        "vectorized = count_vectorize.fit_transform(X_train[\"cleaned tweets\"])\n",
        "clf.fit(vectorized, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "fsNblz7dRKHI"
      },
      "outputs": [],
      "source": [
        "vectorized = count_vectorize.transform(X_test[\"cleaned tweets\"])\n",
        "res=clf.predict(vectorized)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKyteu0cRdE5",
        "outputId": "e65757f1-9b3d-4620-f8be-69e3356e3e2f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9357692307692308"
            ]
          },
          "execution_count": 115,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "metrics.accuracy_score(res, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "85Mg1p3jRgPE",
        "outputId": "8acbcb0f-00f3-4710-e7f9-619a82e3c762"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(89.18, 0.5, 'predicted label')"
            ]
          },
          "execution_count": 116,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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XucsTIrwOIzo6GiEhIRg7diyARzMnp06dEm7oww8/xIoVK4zf2hUREYEPP/wQ9vb2+OKLLypeuQIWL56L7OwrSEg4gvT0DOzZcwjNmjVFQsLjK3YLCrS4eTMJzZs3NfFKLxbbd3tAu2dfmcet3/CFLvGWcduyYQPorj8OU93167Bs2KDK6jMnT/sM/V3Hjm1x8eLVpzxbeUKBsW7dOnz55Zdo0KCB8Xs8a9SogcWLF1eosVdffRUtW7bE8OHD4ePjU/FqFTZp0kw4OzdDly6B2LlzD4qKiuHgYIcHD/JKPS43Nw8ajb1CVcrLws0N1n5vQBu196nHLRt5QTPqI+T+z+NBbZWtLQz5+cZtw8OHUP9tUP1F9bTP0F+9/roPQkImISRknkIVmiYUGD/99BPWrFmDMWPGQK1+9BQvLy8kJiYKN3T06FH06dMHEyY8GhhLSEgwnq1UJwaDASdPxqJePXeMGTMc+fkFqFnTodRjNBoH5OU9VKhCedn26o7i+D+gv5P+xDGLenXh9M3XyF28DCXxCcb9klYLtf3jQFXZ28NQUCBLvebg75+hP3l5eWLnzrUIDv4S0dFnTLyCcoQC4+HDh3B3dwfw+N6qOp0OVlZWwg0tWbIEW7duNd7LpEWLFkhOLntwxdxZWlrAy8sTly5dha9vc+N+OztbeHl5mu0pZWWz7dUD2j1Pnl1YuLnBadG3yP9xHbR795c6pku8BcvGjwdnrRo3KtVleVn8+RkCgPr16yEqaiPmz1+CjRu3K1xZ2YQCo3Xr1li5cmWpfWvXrkXbtm0r1NjfF2dZW1tX6PlKcXGpjcGD+8Le3g5qtRrdunXGkCH9cPhwNHbu3IPmzZuif/93YWNjg5CQyUhIuPRSDHhavf4a1M7OKDx0pNR+tbMznJZ8i4Ltvz51IFO7Zx8chg6G2tkZ6tq1YT9sCLS798hUtTJMfYbq1nXDnj2bEB7+E374Yb3SpZokNEsyc+ZMjB07Flu2bMHDhw/Rs2dP2NvbY8WKFcIN2dvbIysry3iGcvr0aWg0mmerWmaSJGHMmOFYunQe1Go1kpNTMXXqV4iMfPSX84MPxmLhwtlYs2YxYmPj8NFHQQpXLA/bd3ui8OhxSFptqf12fXvDsl49OIwaCYdRI437M3r0BgAU7NwFi7p14bJ21aPtXbtf+BkSU5+hGTMmw8vLEzNnfoaZMz8zPsfZuZmCFT+d8LeGS5KEhIQEpKamwt3dHb6+vsbxDBHx8fEIDQ1FSkoKfHx8cOvWLYSHh+P111+vUMFcGm4al4abxqXh5TO1DkMoMMaNG4fw8PAn9gcFBWHZsmXCheTl5eHcuXMAAH9//2e6NysDwzQGhmkMjPI998Kt06dPP3X/mTMVG8nVaDRo37499Ho9AECr1cLW1rZCr0FEyjEZGH+usygpKXlizcXt27dRt25d4Yb27duHuXPn4u7duwAedXF4mwKi6sVkYKSnP5pblyTJ+Puf3N3djWsqRISFhWHRokXw8/Or0NgHEZkPk4Exf/58AI/GG4YMGfJcDTk6OlaLa0aIqGxCf+qtra2fuLHR5cuXha8wBR7dSW3jxo3IycmBVqs1/hBR9SE0S/LOO+9gx44dcHR0NO7LycnBgAEDcPjwYaGG/nrtyJ+3YHyWMQzOkpjGWRLTOEtSvueeJcnPz4eDw9+vl9AgNzdXuAjeepGo+hPqkjRq1Ah795a+XmD//v1o1Ih/zYheJkJnGMHBwRgzZgyioqLg4eGB5ORknDp16onrS4joxSZ0hvHmm29i165daNGiBbRaLXx9ffHbb7+hVatWVV0fEZkR4WtJzAUHPU3joKdpHPQs3zMNes6aNQtz5swBAEydOrXMu6AtWLDgOcsjouqizMB49dVXjb97enrKUgwRmTd2SV4w7JKYxi5J+Z6pSyL6jeDt27eveEVEVC2VGRgzZswotZ2ZmQkAqFWrFnJycgAAbm5uOHjwYBWWR0TmpMzAOHTo8f0SIiIikJOTg0mTJsHW1hZarRZLliwx3sWMiF4OQmMY7dq1w/Hjx0t9S3hJSQk6deqEmJiYKi3w7ziGYRrHMEzjGEb5TI1hCC3csrOzQ3x8fKl9CQkJ/LYsopeM0NLwiRMnYvTo0ejSpQvq1KmD9PR0HD58uNrc4pCIKofwtOr169exd+9eZGZmwsXFBb169ULjxo2rur4nsEtiGrskprFLUr7nvrwdABo3bgwvLy9kZWXB1dW1UgojoupFaAwjNzcXU6ZMga+vL3r06AEAOHjwIBYuXFilxRGReREKjNDQUDg4OODQoUPGmRJ/f39ERUVVaXFEZF6EuiSnTp0yTqv+eRGak5MT7t27V6XFEZF5ETrD0Gg0uH//fql9aWlpT9xcmYhebEKBMXjwYEycOBExMTEwGAyIi4vDtGnTMGzYsKquj4jMiNC0qiRJWLt2LTZv3oy0tDS4u7tj6NChGDFiRJnfk1FVOK1qGqdVTeO0avmea1pVr9cjJCQEc+bMwYgRIyq1MCKqXsrtklhYWCA6Olr2MwkiMj9CXZLvv/8eeXl5CAoKgrW1tRx1lcneroGi7Zu7Il2J0iWYNW3acaVLMHtWzl5lHhMKjICAAGRlZUGtVsPJyanU2caRI0cqpUhRDAzTGBimMTDKZyowhNZhhIWFVVoxRFR9CQVGmzZtqroOIqoGhAKjuLgY4eHhiIyMRGZmJlxdXdG7d2+MGzcONjY2VV0jEZkJocD48ssvkZiYiBkzZqBevXpITU3FihUrkJGRgfnz51d1jURkJoQC4+DBg9i/fz9q1qwJ4NGl7m+88YbxylUiejkILQ13dnaGVqstta+oqIjXkhC9ZITOMPr164fRo0dj+PDhcHNzQ3p6OjZs2IB+/fqVun8J71FC9GITWofRpUuX8l9IpZLlHiVch2Ea12GYxnUY5XvudRh/vUcJEb28hMYwiIgABgYRVQADg4iEMTCISBgDg4iEMTCISBgDg4iEMTCISBgDg4iEMTCISBgDg4iEMTCISBgDg4iEMTCISBgDg4iEMTCISBgDg4iEMTCISBgDg4iECX2nZ2UIDAwsdRPnP23dulWuEojoOckWGNOmTTP+XlRUhMjISLi6usrVPBFVAtm6JG3atDH+dOrUCfPnz8eZM2fkar7SNGrUAPeyr2DVqoXGfUOGvI9Ll08g8+5FbNq8Eq+84qhghcr417iRiDm1Gw/zbmLVD4/fGysrK2zetBLXr8ZAV5yKgM4v7r1rNm79Xwz5eCL83+6LGXO/Ne6/8McljJ4Ugrd6DUanPkPx+cz/xt2sbOPxM79fwKigaWjXIxA9Akc88bqjgqahU5+haNt9IAaO+BcOHT/1xGPkotgYRn5+PrKyspRq/pktXDgbv/9+wbjdrFkTLFk6D6P/+TkaNngT2gItFi6aq2CFyki7k4F58xdjzY+bnzgWHX0GI0ZOwJ07GQpUJh8X59r4dOQwDOhT+haiuXn5GNzvXezb9iP2bfsJdna2mDnvO+NxW9saGPBeD0wZ/8+nvu70yWNxeOdGnN6/HV/+50RM/yqsVODISZExDIPBgJSUFIwaNUqu5ivFoEF9kfMgF5dPn0MjL08AwNBh/RG1+yCiox+dLc2e/S3OxR2Ag4M98vMfKlmurHbsiAIAvNnqDdSr527cX1JSgiVLfwAA6PUGRWqTS/e3OwAA/n35GjIyH/8x7NS+danH/Ufg+xg5/j+N2y2ae6NFc2+cio176ut6N25o/F2lUkGn1yE98y5cnJ0qs3whioxhWFhYwMPDo1qNYWg0Dpg56zP07v0fGDlymHF/s2ZNcTrmd+N2YmIyiotL0LhJQ5yP+0OJUsnM/X4+AY0b1q/Qc/41NRQxZ+NQXFyCDm1b4TWfJlVUnWmyBUabNm2Qn5+PpKQkvPbaa3I1W2m++GIK1v70C9JS00vtd7C3w4PcvFL7cnPzoHFwkLM8qiauXE9E+JqNWPp1aIWetzzsK5TodIiJjcPNpNtQq5UZTZCt1aNHj6JPnz6YMGECACAhIQFjx46Vq/nn4uvbHG+/0wFLl6564lj+wwLU1JQOB43GAXn5+XKVR9VEckoaxk2ZhemTx6KV3+sVfr6VpSU6tW+Nk2fO4fDxmCqosHyynWEsWbIEW7duxSeffAIAaNGiBZKTk+Vq/rl06tQOnp6v4srVkwAAe3s7WFhYwKdZE+zffxQtWjQzPrZBAw/Y2Fjj+rVEpcolM5SWnoHRk/4Ln478AO/36vpcr6XX63E79U4lVVYxsgUGALi4uJTatra2lrP5Z7Z69UZs3brLuD1p0ieo7/kqJk+aCReX2jh0eDveeqs1zp//A7NmfY6dO/e+VAOewKNxKUtLS1hYqGFhYQEbGxvodDro9XpYW1sbB7ytra1gY2ODoqIihSuufDqdHnq9Hnq9AXqDAUVFxbCwsMC9+/fx8YTp+CCwL4YO6PPE8wwGA0pKdNDpdJAkoKioGGq1ClZWVriZdBupaelo3dIXFhYW2HPwGM6e/wOf/+vpMypVTbbAsLe3R1ZWlvGDc/r0aWg0Grmafy5abSG02kLjdv7DAhQVFiErKxtZWdmYNHEGVq9ZBCenV3D4cDTGfhqsYLXKmBEyCV/MmmLc/vAfgZg951vMnvMdLv5xDA0aeAAAonb/DABo1KQtkpJSFKm1qqz46WeEr95g3P5t7yGM+/gfUKlUSElLx/LVG7D8L8djD/wKADh7/g98POHxpECrLv3wpn8L/LhsASRJwvLVG3Bj1nxYWKhR/9W6+Gb2dDT3bizfP+wvVJIkSXI0FB8fj9DQUKSkpMDHxwe3bt1CeHg4Xn+9Yn05e7sGVVPgC6JIV6J0CWZNm3Zc6RLMnpWzV5nHZAsMAMjLy8O5c+cAAP7+/qhZs2aFX4OBYRoDwzQGRvlMBYasYxgajQbt27eHXq8HAGi1Wtja2spZAhE9B9kCY9++fZg7dy7u3r0LAJAkCSqVCpcuXZKrBCJ6TrIFRlhYGBYtWgQ/Pz/FFp0Q0fORLTAcHR3RsmVLuZojoiog25/67t27Y+PGjcjJyYFWqzX+EFH1IdssiY+Pz+NGVapnHsPgLIlpnCUxjbMk5TOLWZLLly/L1RQRVRGOPhKRMAYGEQljYBCRMAYGEQljYBCRMAYGEQljYBCRMAYGEQljYBCRMAYGEQljYBCRMAYGEQljYBCRMAYGEQljYBCRMAYGEQljYBCRMAYGEQljYBCRMAYGEQljYBCRMAYGEQljYBCRMNluZERE1R/PMIhIGAODiIQxMIhIGAODiIQxMIhIGAODiIQxMIhIGAODiIQxMIhIGAODiIQxMIhIGAPDBG9vbyxZsgT9+vVDz549sXfvXuOxY8eOoX///ujbty9GjBiBpKQkAMDNmzcxdOhQvP/++3jvvfewatUqpcqvct7e3oiIiEBgYCC6du1a6v25cOEChg8fjoEDB2LgwIE4cuSI8dj69evRo0cPBAYGYsmSJWjbtq0C1cvjWd6j06dPY+DAgcbH/X1bURKVqWnTptLSpUslSZKkGzduSG3atJGysrKkrKwsqW3bttK1a9ckSZKkX375RRo0aJAkSZI0Z84cKSIiwvgaOTk58hcuk6ZNm0rr1q2TJEmSzp49K3Xs2FGSJEl68OCB1K9fPykjI0OSJEnKyMiQOnXqJD148EC6dOmS1LFjR+nevXuSJD16v9q0aaPMP0AGz/IexcTESAMGDDC+xt+3lWSpdGCZu8GDBwMAvLy80Lx5c5w/fx4qlQo+Pj5o3LgxACAwMBBfffUV8vPz0bp1a4SFhUGr1aJt27Zo166dkuVXud69ewMA/Pz8kJmZiaKiIsTFxSElJQWffPKJ8XEqlQpJSUmIi4tDQEAAnJycAACDBg3Crl27FKldLhV9j8wZA6OS9ezZE35+foiOjsb333+Pbdu24ZtvvlG6rCpjY2MDALCwsAAA6HQ6SJIEb29vbNiw4YnHx8XFyVqfOajoe3T27FlIf/nWiaKiInkKFcAxjHJs27YNAHDr1i1cvHgRfn5+8PPzw+XLl3Hjxg0AwK+//ormzZvDwcEBSUlJcHFxwcCBAzF+/HgkJCQoWb4i/P39kZSUhJiYGOO++Ph4SJKENm3a4C0yBZEAAASKSURBVNixY8jOzgbw6L17GZl6jzw8PHD79m08ePAAkiQhMjJSwUpL4xlGOfR6Pfr37w+tVovZs2ejdu3aAIAFCxYgODgYOp0OTk5OCAsLAwBERUVh165dsLKygkqlQkhIiJLlK8LR0RHLly9HWFgY5s2bh5KSEnh4eCAiIgI+Pj4YPXo0hg0bBgcHB7Rr1w4ajUbpkmVn6j1yc3PDqFGjMHDgQDg7O6N169a4du2a0iUD4DdumeTt7Y1z587B3t5e6VJeKPn5+XBwcAAALF26FElJSS90t+1FwjMMkt23336Lc+fOGf+qzp49W+mSSBDPMIhIGAc9iUgYA4OIhDEwiEgYA4Oq1OnTp9G5c2ehx27fvh0ffPDBM7XzPM8lcQyMl0yXLl1w8uRJpcugaoqBQaXodDqlSyAzxsB4iUydOhVpaWkYO3Ys/P398f333yMlJQXe3t7YsmUL3n77bYwYMeKp3Yi/npkYDAasXLkS3bp1Q9u2bTFp0iTk5OQI1fDn8/z9/dG7d2/s37+/1HFJkjB79my0atUKvXr1wqlTp4zH8vLyEBISgo4dO6JTp05YuHAh9Hr9c74rVBEMjJdIWFgY6tati4iICMTFxZW6UjI2Nha7d+8W+v6OdevW4cCBA1i/fj2OHz8OR0dH4cVXHh4e2LBhA37//XcEBQVh6tSpyMzMNB6Pj49H/fr1ERMTg4kTJyIoKMgYRtOnT4elpSX27duHHTt2IDo6Glu2bKngu0DPg4FBAIAJEybAzs4ONWrUKPexmzZtwmeffYY6derA2toaQUFB2Lt3r1B35t1334WbmxvUajV69+4NT09PxMfHG487OTlhxIgRsLKyQu/evdGwYUMcOXIEWVlZOHr0KEJCQmBnZ4fatWtj5MiRZnVh1suAS8MJAFCnTh3hx6alpWH8+PFQqx//vVGr1bh37x7c3NxMPnfHjh1Ys2YNUlNTAQAFBQW4f/++8bibmxtUKpVxu27dusjMzERaWhp0Oh06duxoPGYwGODu7i5cNz0/BgYBQKn/pLa2tigsLDRu6/V64+XowKNwmTdvHlq1alWhNlJTUzFz5kz8+OOP8Pf3h4WFBfr161fqMRkZGZAkyVjPnTt30KVLF+PZTExMDCwt+bFVCrskLxlnZ2fcvn3b5GMaNmyIoqIiHDlyBCUlJQgPD0dxcbHx+AcffIBFixYZzxKys7Nx4MCBctvWarVQqVTGb9vatm3bE5dtZ2dnY+3atSgpKUFUVBRu3LiBgIAAuLq6okOHDvj666+Rn58Pg8GA5ORknDlzpqJvAT0HBsZLZsyYMQgPD8ebb75Z5gCnRqNBaGgoZs6cic6dO8PW1rZUl+Wjjz5Cly5d8PHHH8Pf3x9DhgwpNQ5RlsaNG+Pjjz/GsGHD8NZbb+Hq1ato2bJlqcf4+voiKSkJ7dq1w6JFi7BkyRK88sorAB59B0lJSQl69+6N1q1bY+LEibh79+5zvBtUUbxalYiE8QyDiIQxMIhIGAODiIQxMIhIGAODiIQxMIhIGAODiIQxMIhIGAODiIT9f0K7YmuEaPYBAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "mat = confusion_matrix(y_test, res)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[\"pos\",\"neg\",\"neu\"], yticklabels=[\"pos\",\"neg\",\"neu\"])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SKgfAN75Rh89",
        "outputId": "b6d44ecd-8cc5-4c37-87ef-f0aff5863a49"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AdaBoostClassifier(n_estimators=100, random_state=0)"
            ]
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "clf = AdaBoostClassifier(n_estimators=100, random_state=0)\n",
        "vectorized = count_vectorize.fit_transform(X_train[\"cleaned tweets\"])\n",
        "clf.fit(vectorized, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "TtTjtljqR5NY"
      },
      "outputs": [],
      "source": [
        "vectorized = count_vectorize.transform(X_test[\"cleaned tweets\"])\n",
        "res=clf.predict(vectorized)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdxPnHodR9M4",
        "outputId": "f8d3eaa3-3434-432b-d67a-dd9e864df921"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8442307692307692"
            ]
          },
          "execution_count": 119,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "metrics.accuracy_score(res, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "0Wcf1OwzR_Ju",
        "outputId": "c58f88d0-7da6-4b63-904e-66bc1114fddd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(89.18, 0.5, 'predicted label')"
            ]
          },
          "execution_count": 120,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "mat = confusion_matrix(y_test, res)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[\"pos\",\"neg\",\"neu\"], yticklabels=[\"pos\",\"neg\",\"neu\"])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXDKVfwKSAti",
        "outputId": "38319bb5-37df-4b92-d8e6-8487ba1d59d1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RidgeClassifier()"
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import RidgeClassifier\n",
        "clf = RidgeClassifier()\n",
        "vectorized = count_vectorize.fit_transform(X_train[\"cleaned tweets\"])\n",
        "clf.fit(vectorized, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "Uv6uW5GkSMHP"
      },
      "outputs": [],
      "source": [
        "vectorized = count_vectorize.transform(X_test[\"cleaned tweets\"])\n",
        "res=clf.predict(vectorized)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kN47d2yLSOn1",
        "outputId": "6dd0c52e-209a-4524-9b55-3fab4ab924e8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9276923076923077"
            ]
          },
          "execution_count": 123,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "metrics.accuracy_score(res, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "ZvZIi1vASPyg",
        "outputId": "7d0c5de2-e9c7-4d8b-c419-46e960adbe98"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(89.18, 0.5, 'predicted label')"
            ]
          },
          "execution_count": 124,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "mat = confusion_matrix(y_test, res)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[\"pos\",\"neg\",\"neu\"], yticklabels=[\"pos\",\"neg\",\"neu\"])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZJ3EAsfSRMM",
        "outputId": "cf076fc3-7715-402e-e870-bbc92e34405f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PassiveAggressiveClassifier(random_state=0)"
            ]
          },
          "execution_count": 131,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import PassiveAggressiveClassifier\n",
        "clf = PassiveAggressiveClassifier(random_state=0)\n",
        "vectorized = count_vectorize.fit_transform(X_train[\"cleaned tweets\"])\n",
        "clf.fit(vectorized, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "id": "DoPTgjwPSpcD"
      },
      "outputs": [],
      "source": [
        "vectorized = count_vectorize.transform(X_test[\"cleaned tweets\"])\n",
        "res=clf.predict(vectorized)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSz-8V1rSrcw",
        "outputId": "8c57f015-7dd1-4e8c-ded3-015b404e5e6b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9323076923076923"
            ]
          },
          "execution_count": 133,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "metrics.accuracy_score(res, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "fibKoyZaSsw4",
        "outputId": "81ba8866-fb89-4629-9b80-23b1bd21d6a2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(89.18, 0.5, 'predicted label')"
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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4sWAZ3tvWYlXbh6KERCy9vcj9YS/azRwAcnftxS748X+w94mTZwyvNU1D0zTq+dYmLu4Y16/fAG5d0FlcXEy9enXMVKVxyncN37t3LzExMaSnpxMVFcWxY8fIzs5WfoL7P/7xD5555hnDbf3+/ve/4+9/a4Du3XffvY/SzcfDw40GDXw5ceI0Qa2alxjMvf36qaf8Kk1gOHbtyM2YbYb31r51KDxz3vBey8ujODkFa986FCUkkv3tf3Dq/TI3t+wEwP75YHJ37zN53eYwd+77vBrWFwcHe+LijrH5u52GealXjuPk5IiFhQXTpn1kxipLp9QliY6OZurUqdSpU8fwC29nZ8dnn312T43VrFmTZs2aERYWZgiLR42VlRXLls4nOno1p0+fZ8vWXfTu3Y3GjZ/Ezs6OyZPeRq/X4+Bgb+5STcKyuge2zZpwM2arYZrO3h79zZslltNn3zSMUxScOovO2ooa29dRY/s60OvJ/va/Jq3bXEaNmoRrNT+ee74n69dvJj+/wDDPw/Mp3NyfZPRbk/nlyK9mrLJ0SoGxdOlSlixZwtChQ7GwuPURX19fLl68qNzQDz/8QNeuXRk5ciRw69yON9988z5KNh+dTsdXX82loKCAUaMnAbBz526mT/+IVd8s5NzZWOITEsnKyiYp6TczV2sajl06kH/kV4pTLhumabm5WPxpEFPn6GDogrjNmkLRpSSS23Uj+bluFCWlUG36v0xatznp9Xr27TtEjZpevPHGqyXm5eTksmBBNIsXfYa7ezUzVVg6pcC4efMmXl5ewJ1d7qKionsarJw7dy6rV682PMukcePGXLp06V7rNauFCz7G08Odvv2GUlRUZJgeGbWURk+1oaZPAOvWbcLKyorjxyvH8X6HLh3J2bi1xLTCC/FYN6hneK+zs8OqpjeFF+IBsG5Yn+y1G9Hy8tBy88heuwG7Z1uZsuwKwcrSCl/f2ndNt7CwwMHBnhre1c1QlXFKgdGiRQsWLFhQYtqyZcto1ere/pH/fHKWjY3NPX3enD6f/wH+/g3o0XMgeXl5hum2trY89ZQfAD4+3kR+8SHz5i8iM/O6uUo1GZsmjbD0qEbO70dHbsv9fg/W9epg/3ww2FhTZUgYhWcvUJRw6/B5wYnTOHbvgs7WBp2tDU49u1J47oI5voLJuLtXo2+fl3F0dMDCwoIOHdrRr193vv9+D+3bBxPQ9CksLCxwdnYiIiKcjIxMTp46Z+6y76IUGJMnT2bbtm2EhIRw8+ZNOnXqxObNm5kwYYJyQ46OjqSlpRn2UA4cOICzs/P9VW1itWrVYOjQMJo2bURS4i9kpJ8hI/0MAwb0xM7Oluhl88nMOMu+vTHEHviZ8PDK8fhIx66dyP1+D1pObonp+szrpI2fhss/B1Njx3+wedqfa5PeM8xPnxGBlXd1vDauwitmFZY1vEif+qGpyzcpTdMYOvRVLl44ROqV43z4wRTGjJ3Kxo3bqOpShejoz0m7epJTJ/fi61ubbt3CyM/PN3fZd1G+a7imaRw7dozk5GS8vLxo0qSJYTxDxdGjRwkPDycpKQl/f3/i4+OJjIzk6aefvqeC5dRw4+TUcOPk1PCyGTs1XCkwhg0bRmRk5F3TR4wYwfz585ULycrK4vDhwwAEBgbe17NZJTCMk8AwTgKjbA98LcmBAwf+cvrBgwfvqRBnZ2dat25NcXExALm5udjbV47Dj0I8DowGxu3zLAoLC+865yIxMRFvb2/lhrZu3cp7773H1atXgVtdHHlMgRCPFqOBcfnyrWPrmqYZXt/m5eVlOKdCRUREBHPmzCEgIOCexj6EEBWH0cCYNWsWcGu8oW/fvg/UkIuLS4W/ZkQIYZzSn3obG5u7Hmx06tQp1q9fr9xQhw4d+Prrr8nMzCQ3N9fwI4R4dCgdJXn++edZv349Li4uhmmZmZn07NmT77//XqmhP147cvsRjPczhiFHSYyToyTGyVGSsj3wUZLs7GycnJxKTHN2dubGjRvKRcijF4V49Cl1SerVq8eWLVtKTNu2bRv16tUr5RNCiMeR0h7G2LFjGTp0KJs3b8bHx4dLly6xf//+u64vEUI83pT2MJ555hk2bNhA48aNyc3NpUmTJmzcuJHmzZuXd31CiApE+VqSikIGPY2TQU/jZNCzbPc16DllyhRmzJgBwLhx40p9Ctrs2ZXjykwhhJHAqFmzpuF17dp33+RDCFH5SJfkMSNdEuOkS1K2++qS7N+/X2nlqncNF0I8+koNjEmTJpV4n5qaCkDVqlUNDyTy9PRkx44d5VieEKIiKTUwdu6887yEqKgoMjMzGT16NPb29uTm5jJ37lyqVq1qkiKFEBWD0hhGUFAQu3fvLnGX8MLCQoKDg4mNjS3XAv9MxjCMkzEM42QMo2zGxjCUTtxycHDg6NGjJaYdO3ZM7pYlRCWjdGr4qFGjGDJkCCEhIVSvXp3Lly/z/fffP3KPOBRCPBjlw6rnzp1jy5YtpKam4u7uTufOnalfv35513cX6ZIYJ10S46RLUrYHvrwdoH79+vj6+pKWloaHh8dDKUwI8WhRGsO4ceMGY8aMoUmTJnTs2BGAHTt28Omnn5ZrcUKIikUpMMLDw3FycmLnzp2GIyWBgYFs3ry5XIsTQlQsSl2S/fv3Gw6r3r4IzdXVlWvXrpVrcUKIikVpD8PZ2ZmMjIwS01JSUu56uLIQ4vGmFBh9+vRh1KhRxMbGotfriYuLY/z48fTv37+86xNCVCBKh1U1TWPZsmWsWrWKlJQUvLy86NevHwMHDiz1PhnlRQ6rGieHVY2Tw6ple6DDqsXFxUycOJEZM2YwcODAh1qYEOLRUmaXxNLSkr1795p8T0IIUfEodUkWLlxIVlYWI0aMwMbGxhR1lcrWzses7Vd0xXq9uUuo0HJTdpu7hArP2s231HlKgdGuXTvS0tKwsLDA1dW1xN7Grl27HkqRqiQwjJPAME4Co2zGAkPpPIyIiIiHVowQ4tGlFBgtW7Ys7zqEEI8ApcAoKCggMjKSmJgYUlNT8fDwoEuXLgwbNgxbW9vyrlEIUUEoBcbUqVO5ePEikyZNokaNGiQnJ/Pll19y5coVZs2aVd41CiEqCKXA2LFjB9u2baNKlSrArUvdmzZtarhyVQhROSidGu7m5kZubm6Jafn5+XItiRCVjNIeRvfu3RkyZAhhYWF4enpy+fJlVqxYQffu3Us8v0SeUSLE403pPIyQkJCyV6TTmeQZJXIehnFyHoZxch5G2R74PIw/PqNECFF5KY1hCCEESGAIIe6BBIYQQpkEhhBCmQSGEEKZBIYQQpkEhhBCmQSGEEKZBIYQQpkEhhBCmQSGEEKZBIYQQpkEhhBCmQSGEEKZBIYQQpkEhhBCmQSGEEKZBIYQQpkEhhBCmdI9PR+G0NDQEg9xvm316tWmKkEI8YBMFhjjx483vM7PzycmJgYPDw9TNS+EeAhM1iVp2bKl4Sc4OJhZs2Zx8OBBUzX/wJYs+Yz4iz9xNfUEvx77gUGD+hvmDRrUnxPHd3Mt7RQb/huNl5enGSs1DxsbGxZ8+RHnzx4g49ppfjq0lc6dngegVctmfLdpJamXf+W35KN8s/JLqld//P5YfL36v/QdPIrA57ox6b2PDdOP/HqSIaMn8rfOfQju2o93Jr/P1bR0w/yDPx9h0IjxBHUMpWPowFLXfyjuKE8/+yJzFywt1+9hjNnGMLKzs0lLSzNX8/ds9uzPaej3N9w9GhEaOpip4eMIDGxM27ZBTJ82ntDe/0N1r8bExyeybNl8c5drclZWliQlpRDyQiiubv6Eh89m5ddR1K5dkyeecGHhohXUaxCEb/2WZGVls2jhJ+Yu+aFzd6vGG6/1p2fXko8QvZGVTZ/uL7J1zVdsXbMUBwd7Js+88/3t7e3o+VJHxgz/n1LXXVhUxAdzvqRJI79yq1+FWcYw9Ho9SUlJDBo0yFTNP7CTJ88YXmuahqZp+PrWpsUzAaxdG2OYP3PWrT0RX9/aXLiQYK5yTS4nJ5fpM+78EsRs2s7F+Es0a9aEdes2lVj2iy+WsHPHGlOXWO46PPcsAMdPneVK6p0/hsGtW5RY7u+hL/Pa8P81vG/cyI/GjfzYfyiu1HUvXbmWv7VsRnpG5kOu+t6YZQzD0tISHx+fR24MY+5n7xMW1gcHB3vi4o7x3Xc7afFMQInB3Nuvn2rkV6kC4888PNxo2MCXEydO3zUvODiIEyfO/MWnKoeffzlG/bq1lJdPuXyFdTFb+XbxPN7/5ItyrKxsJh3DaNSoEY6OjjRv3vyRCwuAUaMnUc3Nn+dDerH+P9+Rn1/A1m27CA19iaef9sfOzo5JE0ej1+uxd7A3d7lmY2VlRfTS+SyLXs3p0+dLzGvc+EkmT3qL8RNmmKk68zp97iKRS75mzPAhyp+Z9WkUI4aE4VAB/k+ZLDB++OEHunbtysiRIwE4duwYb775pqmaf2j0ej379h2iZo3qvDE0jJ079zBjxies+mYBZ07vIz4hiaysbJKTfzN3qWah0+lY+tVcCgoKGDV6Uol59erVYeN/o3l7TDh79j46A94Py6WkFIaNmcKEt96kecDTSp/ZtSeWmzm5vPhCu3KuTo3JuiRz585l9erVvP766wA0btyYS5cumar5h87Sygpf39oARH25lKgvb41cN6hfl39NGMXx43fvilcGCxd8jKeHOy+9HEZRUZFheq1aNdiy+Rven/kZK1Y8fuMXZUm5fIUho//FG68N4OXO7ZU/F/vTLxw/dYZ23f4OQHb2TSwsLTh7Pp55H4aXV7mlMllgALi7u5d4b2NjY8rm75u7ezWee+5ZNm3aTm5uHu1DgunXtzuvvjoCW1tb6tWrw4kTp/Hx8ebzLz5k/ueLycy8bu6yTe7z+R/wpH8DOnbuR15enmG6t3d1tm35N19ELmHBwmgzVli+ioqKKS4uprhYT7FeT35+AZaWllzLyGDwyAkMCO1Gv55d7/qcXq+nsLCIoqIiNA3y8wuwsNBhbW3NyNdfZUhYX8Oys+Z8iYebK28O+rspv5qByQLD0dGRtLQ0w6DggQMHcHZ2NlXzD0TTNIYODWP+vJlYWFhw6VIyY8dNZWPMNlxcqrBs6Tx8fWuTlZXNsuh/M3VqhLlLNrlatWrwxtAw8vLySE78xTB92PDx1K9Xh3r16vDulDG8O2WMYV5V14bmKLXcfLl0JZGLVxjeb9yyk2GDX0Gn05GUcpkvFq/giz/MP7R9HQA//fIrg0feOSjQPKQ7zwQ25qv5s3F0dMDR0cEwz87WBnt7O1yqmOd3R6dpmmaKho4ePUp4eDhJSUn4+/sTHx9PZGQkTz+t1pe7zdbOp5wqfDwU6/XmLqFCy03Zbe4SKjxrN99S55ksMACysrI4fPgwAIGBgVSpUuWe1yGBYZwEhnESGGWrMIEBUFBQQHFxseG9vf29HSqSwDBOAsM4CYyyGQsMk41hbN26lffee4+rV68Ct8YFdDodJ0+eNFUJQogHZLLAiIiIYM6cOQQEBGBhIbfhEOJRZLLAcHFxoVmzZqZqTghRDkz2p75Dhw58/fXXZGZmkpuba/gRQjw6TDbo6e/vf6dRne6+xzBk0NM4GfQ0TgY9y1YhBj1PnTplqqaEEOVERh+FEMokMIQQyiQwhBDKJDCEEMokMIQQyiQwhBDKJDCEEMokMIQQyiQwhBDKJDCEEMokMIQQyiQwhBDKJDCEEMokMIQQyiQwhBDKJDCEEMokMIQQyiQwhBDKJDCEEMokMIQQyiQwhBDKJDCEEMokMIQQykz+9HYhxKNL9jCEEMokMIQQyiQwhBDKJDCEEMokMIQQyiQwhBDKJDCEEMokMIQQyiQwhBDKJDCEEMokMIQQyiQwjPDz82Pu3Ll0796dTp06sWXLFsO8H3/8kR49etCtWzcGDhxIQkICABcuXKBfv368/PLLvPTSSyxatMhc5Zc7Pz8/oqKiCA0NpX379iW2z5EjRwgLC6NXr1706tWLXbt2GeYtX76cjh07Ehoayty5c2nVqpUZqjeN+9lGBw4coFevXobl/vzerDRRqoYNG2rz5s3TNE3Tzp8/r7Vs2VJLS0vT0tLStFatWmlnz57VNE3T/v3vf2u9ex7J1GEAAAYQSURBVPfWNE3TZsyYoUVFRRnWkZmZafrCTaRhw4ZadHS0pmma9tNPP2lt2rTRNE3Trl+/rnXv3l27cuWKpmmaduXKFS04OFi7fv26dvLkSa1NmzbatWvXNE27tb1atmxpni9gAvezjWJjY7WePXsa1vHn9+ZkZe7Aquj69OkDgK+vL40aNeKXX35Bp9Ph7+9P/fr1AQgNDWXatGlkZ2fTokULIiIiyM3NpVWrVgQFBZmz/HLXpUsXAAICAkhNTSU/P5+4uDiSkpJ4/fXXDcvpdDoSEhKIi4ujXbt2uLq6AtC7d282bNhgltpN5V63UUUmgfGQderUiYCAAPbu3cvChQtZs2YNH330kbnLKje2trYAWFpaAlBUVISmafj5+bFixYq7lo+LizNpfRXBvW6jn376Ce0Pd53Iz883TaEKZAyjDGvWrAEgPj6eEydOEBAQQEBAAKdOneL8+fMArFu3jkaNGuHk5ERCQgLu7u706tWL4cOHc+zYMXOWbxaBgYEkJCQQGxtrmHb06FE0TaNly5b8+OOPpKenA7e2XWVkbBv5+PiQmJjI9evX0TSNmJgYM1ZakuxhlKG4uJgePXqQm5vL9OnTqVatGgCzZ89m7NixFBUV4erqSkREBACbN29mw4YNWFtbo9PpmDhxojnLNwsXFxe++OILIiIimDlzJoWFhfj4+BAVFYW/vz9Dhgyhf//+ODk5ERQUhLOzs7lLNjlj28jT05NBgwbRq1cv3NzcaNGiBWfPnjV3yYDcccsoPz8/Dh8+jKOjo7lLeaxkZ2fj5OQEwLx580hISHisu22PE9nDECb38ccfc/jwYcNf1enTp5u7JKFI9jCEEMpk0FMIoUwCQwihTAJDCKFMAkOUqwMHDtC2bVulZdeuXcuAAQPuq50H+axQJ4FRyYSEhLBv3z5zlyEeURIYooSioiJzlyAqMAmMSmTcuHGkpKTw5ptvEhgYyMKFC0lKSsLPz49vv/2W5557joEDB/5lN+KPeyZ6vZ4FCxbwwgsv0KpVK0aPHk1mZqZSDbc/FxgYSJcuXdi2bVuJ+ZqmMX36dJo3b07nzp3Zv3+/YV5WVhYTJ06kTZs2BAcH8+mnn1JcXPyAW0XcCwmMSiQiIgJvb2+ioqKIi4srcaXkoUOH2LRpk9L9O6Kjo9m+fTvLly9n9+7duLi4KJ985ePjw4oVK/j5558ZMWIE48aNIzU11TD/6NGj1KpVi9jYWEaNGsWIESMMYTRhwgSsrKzYunUr69evZ+/evXz77bf3uBXEg5DAEACMHDkSBwcH7Ozsylz2m2++4e2336Z69erY2NgwYsQItmzZotSdefHFF/H09MTCwoIuXbpQu3Ztjh49apjv6urKwIEDsba2pkuXLtStW5ddu3aRlpbGDz/8wMSJE3FwcKBatWq89tprFerCrMpATg0XAFSvXl152ZSUFIYPH46FxZ2/NxYWFly7dg1PT0+jn12/fj1LliwhOTkZgJycHDIyMgzzPT090el0hvfe3t6kpqaSkpJCUVERbdq0MczT6/V4eXkp1y0enASGACjxS2pvb09eXp7hfXFxseFydLgVLjNnzqR58+b31EZycjKTJ0/mq6++IjAwEEtLS7p3715imStXrqBpmqGe3377jZCQEMPeTGxsLFZW8t/WXKRLUsm4ubmRmJhodJm6deuSn5/Prl27KCwsJDIykoKCAsP8AQMGMGfOHMNeQnp6Otu3by+z7dzcXHQ6neFuW2vWrLnrsu309HSWLVtGYWEhmzdv5vz587Rr1w4PDw+effZZPvjgA7Kzs9Hr9Vy6dImDBw/e6yYQD0ACo5IZOnQokZGRPPPMM6UOcDo7OxMeHs7kyZNp27Yt9vb2Jbosr776KiEhIQwePJjAwED69u1bYhyiNPXr12fw4MH079+fv/3tb5w5c4ZmzZqVWKZJkyYkJCQQFBTEnDlzmDt3Lk888QRw6x4khYWFdOnShRYtWjBq1CiuXr36AFtD3Cu5WlUIoUz2MIQQyiQwhBDKJDCEEMokMIQQyiQwhBDKJDCEEMokMIQQyiQwhBDKJDCEEMr+H0pO0gx8a4wwAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "mat = confusion_matrix(y_test, res)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[\"pos\",\"neg\",\"neu\"], yticklabels=[\"pos\",\"neg\",\"neu\"])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b2Oqp32_S7yu",
        "outputId": "f17bb052-4345-4e92-e951-fcbfe0ed8a39"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Perceptron()"
            ]
          },
          "execution_count": 135,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import Perceptron\n",
        "clf = Perceptron(random_state=0)\n",
        "vectorized = count_vectorize.fit_transform(X_train[\"cleaned tweets\"])\n",
        "clf.fit(vectorized, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "id": "LxtF0kjITH0-"
      },
      "outputs": [],
      "source": [
        "vectorized = count_vectorize.transform(X_test[\"cleaned tweets\"])\n",
        "res=clf.predict(vectorized)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "16e4dwV1TJDT",
        "outputId": "9d096ddf-6327-416e-e82d-db9a793e736e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9276923076923077"
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "metrics.accuracy_score(res, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "exgwYwdATKgg",
        "outputId": "e6b7ed3f-e871-4d63-f316-9e08609bdc44"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(89.18, 0.5, 'predicted label')"
            ]
          },
          "execution_count": 138,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "mat = confusion_matrix(y_test, res)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[\"pos\",\"neg\",\"neu\"], yticklabels=[\"pos\",\"neg\",\"neu\"])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VfArOlmBTMHO"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}