└── bitcoin_sentiment_analysis.ipynb


/bitcoin_sentiment_analysis.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |   "cells": [
   3 |     {
   4 |       "cell_type": "markdown",
   5 |       "metadata": {
   6 |         "id": "view-in-github",
   7 |         "colab_type": "text"
   8 |       },
   9 |       "source": [
  10 |         "<a href=\"https://colab.research.google.com/github/PRATHOSH-ANBARASAN/bitcoin-sentiment-analysis/blob/main/bitcoin_sentiment_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  11 |       ]
  12 |     },
  13 |     {
  14 |       "cell_type": "markdown",
  15 |       "metadata": {
  16 |         "id": "7-1VYXJM0Fcf"
  17 |       },
  18 |       "source": [
  19 |         "# IMPORT THE LIBRARIES\n",
  20 |         "\n",
  21 |         "---\n",
  22 |         "\n"
  23 |       ]
  24 |     },
  25 |     {
  26 |       "cell_type": "markdown",
  27 |       "metadata": {
  28 |         "id": "qAc3EDVgFPdU"
  29 |       },
  30 |       "source": []
  31 |     },
  32 |     {
  33 |       "cell_type": "code",
  34 |       "execution_count": null,
  35 |       "metadata": {
  36 |         "id": "8b7nJA18bjF8"
  37 |       },
  38 |       "outputs": [],
  39 |       "source": [
  40 |         "import numpy as np\n",
  41 |         "import pandas as pd\n",
  42 |         "import re\n",
  43 |         "import matplotlib.pyplot as plt\n",
  44 |         "from textblob import TextBlob"
  45 |       ]
  46 |     },
  47 |     {
  48 |       "cell_type": "code",
  49 |       "execution_count": null,
  50 |       "metadata": {
  51 |         "id": "6iSsmhSlfD24"
  52 |       },
  53 |       "outputs": [],
  54 |       "source": []
  55 |     },
  56 |     {
  57 |       "cell_type": "markdown",
  58 |       "metadata": {
  59 |         "id": "m5CehKU60KLr"
  60 |       },
  61 |       "source": [
  62 |         "# IMPORT DATASET"
  63 |       ]
  64 |     },
  65 |     {
  66 |       "cell_type": "code",
  67 |       "execution_count": null,
  68 |       "metadata": {
  69 |         "colab": {
  70 |           "background_save": true,
  71 |           "base_uri": "https://localhost:8080/"
  72 |         },
  73 |         "id": "UJb__9a2cA51",
  74 |         "outputId": "1a52605d-6a48-4756-eebd-a54f054acd08"
  75 |       },
  76 |       "outputs": [
  77 |         {
  78 |           "data": {
  79 |             "text/html": [
  80 |               "\n",
  81 |               "     <input type=\"file\" id=\"files-6a5e2010-411d-4256-a053-fb3ad5d6d152\" name=\"files[]\" multiple disabled\n",
  82 |               "        style=\"border:none\" />\n",
  83 |               "     <output id=\"result-6a5e2010-411d-4256-a053-fb3ad5d6d152\">\n",
  84 |               "      Upload widget is only available when the cell has been executed in the\n",
  85 |               "      current browser session. Please rerun this cell to enable.\n",
  86 |               "      </output>\n",
  87 |               "      <script>// Copyright 2017 Google LLC\n",
  88 |               "//\n",
  89 |               "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
  90 |               "// you may not use this file except in compliance with the License.\n",
  91 |               "// You may obtain a copy of the License at\n",
  92 |               "//\n",
  93 |               "//      http://www.apache.org/licenses/LICENSE-2.0\n",
  94 |               "//\n",
  95 |               "// Unless required by applicable law or agreed to in writing, software\n",
  96 |               "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
  97 |               "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
  98 |               "// See the License for the specific language governing permissions and\n",
  99 |               "// limitations under the License.\n",
 100 |               "\n",
 101 |               "/**\n",
 102 |               " * @fileoverview Helpers for google.colab Python module.\n",
 103 |               " */\n",
 104 |               "(function(scope) {\n",
 105 |               "function span(text, styleAttributes = {}) {\n",
 106 |               "  const element = document.createElement('span');\n",
 107 |               "  element.textContent = text;\n",
 108 |               "  for (const key of Object.keys(styleAttributes)) {\n",
 109 |               "    element.style[key] = styleAttributes[key];\n",
 110 |               "  }\n",
 111 |               "  return element;\n",
 112 |               "}\n",
 113 |               "\n",
 114 |               "// Max number of bytes which will be uploaded at a time.\n",
 115 |               "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
 116 |               "\n",
 117 |               "function _uploadFiles(inputId, outputId) {\n",
 118 |               "  const steps = uploadFilesStep(inputId, outputId);\n",
 119 |               "  const outputElement = document.getElementById(outputId);\n",
 120 |               "  // Cache steps on the outputElement to make it available for the next call\n",
 121 |               "  // to uploadFilesContinue from Python.\n",
 122 |               "  outputElement.steps = steps;\n",
 123 |               "\n",
 124 |               "  return _uploadFilesContinue(outputId);\n",
 125 |               "}\n",
 126 |               "\n",
 127 |               "// This is roughly an async generator (not supported in the browser yet),\n",
 128 |               "// where there are multiple asynchronous steps and the Python side is going\n",
 129 |               "// to poll for completion of each step.\n",
 130 |               "// This uses a Promise to block the python side on completion of each step,\n",
 131 |               "// then passes the result of the previous step as the input to the next step.\n",
 132 |               "function _uploadFilesContinue(outputId) {\n",
 133 |               "  const outputElement = document.getElementById(outputId);\n",
 134 |               "  const steps = outputElement.steps;\n",
 135 |               "\n",
 136 |               "  const next = steps.next(outputElement.lastPromiseValue);\n",
 137 |               "  return Promise.resolve(next.value.promise).then((value) => {\n",
 138 |               "    // Cache the last promise value to make it available to the next\n",
 139 |               "    // step of the generator.\n",
 140 |               "    outputElement.lastPromiseValue = value;\n",
 141 |               "    return next.value.response;\n",
 142 |               "  });\n",
 143 |               "}\n",
 144 |               "\n",
 145 |               "/**\n",
 146 |               " * Generator function which is called between each async step of the upload\n",
 147 |               " * process.\n",
 148 |               " * @param {string} inputId Element ID of the input file picker element.\n",
 149 |               " * @param {string} outputId Element ID of the output display.\n",
 150 |               " * @return {!Iterable<!Object>} Iterable of next steps.\n",
 151 |               " */\n",
 152 |               "function* uploadFilesStep(inputId, outputId) {\n",
 153 |               "  const inputElement = document.getElementById(inputId);\n",
 154 |               "  inputElement.disabled = false;\n",
 155 |               "\n",
 156 |               "  const outputElement = document.getElementById(outputId);\n",
 157 |               "  outputElement.innerHTML = '';\n",
 158 |               "\n",
 159 |               "  const pickedPromise = new Promise((resolve) => {\n",
 160 |               "    inputElement.addEventListener('change', (e) => {\n",
 161 |               "      resolve(e.target.files);\n",
 162 |               "    });\n",
 163 |               "  });\n",
 164 |               "\n",
 165 |               "  const cancel = document.createElement('button');\n",
 166 |               "  inputElement.parentElement.appendChild(cancel);\n",
 167 |               "  cancel.textContent = 'Cancel upload';\n",
 168 |               "  const cancelPromise = new Promise((resolve) => {\n",
 169 |               "    cancel.onclick = () => {\n",
 170 |               "      resolve(null);\n",
 171 |               "    };\n",
 172 |               "  });\n",
 173 |               "\n",
 174 |               "  // Wait for the user to pick the files.\n",
 175 |               "  const files = yield {\n",
 176 |               "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
 177 |               "    response: {\n",
 178 |               "      action: 'starting',\n",
 179 |               "    }\n",
 180 |               "  };\n",
 181 |               "\n",
 182 |               "  cancel.remove();\n",
 183 |               "\n",
 184 |               "  // Disable the input element since further picks are not allowed.\n",
 185 |               "  inputElement.disabled = true;\n",
 186 |               "\n",
 187 |               "  if (!files) {\n",
 188 |               "    return {\n",
 189 |               "      response: {\n",
 190 |               "        action: 'complete',\n",
 191 |               "      }\n",
 192 |               "    };\n",
 193 |               "  }\n",
 194 |               "\n",
 195 |               "  for (const file of files) {\n",
 196 |               "    const li = document.createElement('li');\n",
 197 |               "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
 198 |               "    li.append(span(\n",
 199 |               "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
 200 |               "        `last modified: ${\n",
 201 |               "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
 202 |               "                                    'n/a'} - `));\n",
 203 |               "    const percent = span('0% done');\n",
 204 |               "    li.appendChild(percent);\n",
 205 |               "\n",
 206 |               "    outputElement.appendChild(li);\n",
 207 |               "\n",
 208 |               "    const fileDataPromise = new Promise((resolve) => {\n",
 209 |               "      const reader = new FileReader();\n",
 210 |               "      reader.onload = (e) => {\n",
 211 |               "        resolve(e.target.result);\n",
 212 |               "      };\n",
 213 |               "      reader.readAsArrayBuffer(file);\n",
 214 |               "    });\n",
 215 |               "    // Wait for the data to be ready.\n",
 216 |               "    let fileData = yield {\n",
 217 |               "      promise: fileDataPromise,\n",
 218 |               "      response: {\n",
 219 |               "        action: 'continue',\n",
 220 |               "      }\n",
 221 |               "    };\n",
 222 |               "\n",
 223 |               "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
 224 |               "    let position = 0;\n",
 225 |               "    do {\n",
 226 |               "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
 227 |               "      const chunk = new Uint8Array(fileData, position, length);\n",
 228 |               "      position += length;\n",
 229 |               "\n",
 230 |               "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
 231 |               "      yield {\n",
 232 |               "        response: {\n",
 233 |               "          action: 'append',\n",
 234 |               "          file: file.name,\n",
 235 |               "          data: base64,\n",
 236 |               "        },\n",
 237 |               "      };\n",
 238 |               "\n",
 239 |               "      let percentDone = fileData.byteLength === 0 ?\n",
 240 |               "          100 :\n",
 241 |               "          Math.round((position / fileData.byteLength) * 100);\n",
 242 |               "      percent.textContent = `${percentDone}% done`;\n",
 243 |               "\n",
 244 |               "    } while (position < fileData.byteLength);\n",
 245 |               "  }\n",
 246 |               "\n",
 247 |               "  // All done.\n",
 248 |               "  yield {\n",
 249 |               "    response: {\n",
 250 |               "      action: 'complete',\n",
 251 |               "    }\n",
 252 |               "  };\n",
 253 |               "}\n",
 254 |               "\n",
 255 |               "scope.google = scope.google || {};\n",
 256 |               "scope.google.colab = scope.google.colab || {};\n",
 257 |               "scope.google.colab._files = {\n",
 258 |               "  _uploadFiles,\n",
 259 |               "  _uploadFilesContinue,\n",
 260 |               "};\n",
 261 |               "})(self);\n",
 262 |               "</script> "
 263 |             ],
 264 |             "text/plain": [
 265 |               "<IPython.core.display.HTML object>"
 266 |             ]
 267 |           },
 268 |           "metadata": {},
 269 |           "output_type": "display_data"
 270 |         }
 271 |       ],
 272 |       "source": [
 273 |         "from google.colab import files\n",
 274 |         "uploaded = files.upload()"
 275 |       ]
 276 |     },
 277 |     {
 278 |       "cell_type": "markdown",
 279 |       "metadata": {
 280 |         "id": "2RNedUY1c8Mx"
 281 |       },
 282 |       "source": [
 283 |         "# New Section"
 284 |       ]
 285 |     },
 286 |     {
 287 |       "cell_type": "markdown",
 288 |       "metadata": {
 289 |         "id": "HdF8_wsd0eAc"
 290 |       },
 291 |       "source": [
 292 |         "# IMPORT THE DATA"
 293 |       ]
 294 |     },
 295 |     {
 296 |       "cell_type": "markdown",
 297 |       "metadata": {
 298 |         "id": "2uFID9vH0jKW"
 299 |       },
 300 |       "source": [
 301 |         "# REMOVE NAN VALUE ROW IN HASHTAG COLUMN"
 302 |       ]
 303 |     },
 304 |     {
 305 |       "cell_type": "code",
 306 |       "execution_count": null,
 307 |       "metadata": {
 308 |         "colab": {
 309 |           "base_uri": "https://localhost:8080/",
 310 |           "height": 669
 311 |         },
 312 |         "id": "68Je6uO1fOlA",
 313 |         "outputId": "3acefeea-1081-465d-f562-ad03cf80070b"
 314 |       },
 315 |       "outputs": [
 316 |         {
 317 |           "data": {
 318 |             "text/html": [
 319 |               "\n",
 320 |               "  <div id=\"df-7cb09ba3-f7ec-435b-b28f-0efe587bb767\">\n",
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 322 |               "      <div>\n",
 323 |               "<style scoped>\n",
 324 |               "    .dataframe tbody tr th:only-of-type {\n",
 325 |               "        vertical-align: middle;\n",
 326 |               "    }\n",
 327 |               "\n",
 328 |               "    .dataframe tbody tr th {\n",
 329 |               "        vertical-align: top;\n",
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 339 |               "      <th></th>\n",
 340 |               "      <th>user_name</th>\n",
 341 |               "      <th>user_location</th>\n",
 342 |               "      <th>user_description</th>\n",
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 348 |               "      <th>date</th>\n",
 349 |               "      <th>text</th>\n",
 350 |               "      <th>hashtags</th>\n",
 351 |               "      <th>source</th>\n",
 352 |               "      <th>is_retweet</th>\n",
 353 |               "    </tr>\n",
 354 |               "  </thead>\n",
 355 |               "  <tbody>\n",
 356 |               "    <tr>\n",
 357 |               "      <th>0</th>\n",
 358 |               "      <td>DeSota Wilson</td>\n",
 359 |               "      <td>Atlanta, GA</td>\n",
 360 |               "      <td>Biz Consultant, real estate, fintech, startups...</td>\n",
 361 |               "      <td>2009-04-26 20:05:09</td>\n",
 362 |               "      <td>8534</td>\n",
 363 |               "      <td>7605</td>\n",
 364 |               "      <td>4838</td>\n",
 365 |               "      <td>False</td>\n",
 366 |               "      <td>2021-02-10 23:59:04</td>\n",
 367 |               "      <td>Blue Ridge Bank shares halted by NYSE after #b...</td>\n",
 368 |               "      <td>['bitcoin']</td>\n",
 369 |               "      <td>Twitter Web App</td>\n",
 370 |               "      <td>False</td>\n",
 371 |               "    </tr>\n",
 372 |               "    <tr>\n",
 373 |               "      <th>1</th>\n",
 374 |               "      <td>CryptoND</td>\n",
 375 |               "      <td>NaN</td>\n",
 376 |               "      <td>😎 BITCOINLIVE is a Dutch platform aimed at inf...</td>\n",
 377 |               "      <td>2019-10-17 20:12:10</td>\n",
 378 |               "      <td>6769</td>\n",
 379 |               "      <td>1532</td>\n",
 380 |               "      <td>25483</td>\n",
 381 |               "      <td>False</td>\n",
 382 |               "      <td>2021-02-10 23:58:48</td>\n",
 383 |               "      <td>😎 Today, that's this #Thursday, we will do a \"...</td>\n",
 384 |               "      <td>['Thursday', 'Btc', 'wallet', 'security']</td>\n",
 385 |               "      <td>Twitter for Android</td>\n",
 386 |               "      <td>False</td>\n",
 387 |               "    </tr>\n",
 388 |               "    <tr>\n",
 389 |               "      <th>3</th>\n",
 390 |               "      <td>Crypto is the future</td>\n",
 391 |               "      <td>NaN</td>\n",
 392 |               "      <td>I will post a lot of buying signals for BTC tr...</td>\n",
 393 |               "      <td>2019-09-28 16:48:12</td>\n",
 394 |               "      <td>625</td>\n",
 395 |               "      <td>129</td>\n",
 396 |               "      <td>14</td>\n",
 397 |               "      <td>False</td>\n",
 398 |               "      <td>2021-02-10 23:54:33</td>\n",
 399 |               "      <td>$BTC A big chance in a billion! Price: \\487264...</td>\n",
 400 |               "      <td>['Bitcoin', 'FX', 'BTC', 'crypto']</td>\n",
 401 |               "      <td>dlvr.it</td>\n",
 402 |               "      <td>False</td>\n",
 403 |               "    </tr>\n",
 404 |               "    <tr>\n",
 405 |               "      <th>4</th>\n",
 406 |               "      <td>Alex Kirchmaier 🇦🇹🇸🇪 #FactsSuperspreader</td>\n",
 407 |               "      <td>Europa</td>\n",
 408 |               "      <td>Co-founder @RENJERJerky | Forbes 30Under30 | I...</td>\n",
 409 |               "      <td>2016-02-03 13:15:55</td>\n",
 410 |               "      <td>1249</td>\n",
 411 |               "      <td>1472</td>\n",
 412 |               "      <td>10482</td>\n",
 413 |               "      <td>False</td>\n",
 414 |               "      <td>2021-02-10 23:54:06</td>\n",
 415 |               "      <td>This network is secured by 9 508 nodes as of t...</td>\n",
 416 |               "      <td>['BTC']</td>\n",
 417 |               "      <td>Twitter Web App</td>\n",
 418 |               "      <td>False</td>\n",
 419 |               "    </tr>\n",
 420 |               "    <tr>\n",
 421 |               "      <th>5</th>\n",
 422 |               "      <td>ZerrBenz™ ⚔ ✪ 20732</td>\n",
 423 |               "      <td>Bkk, Thailand</td>\n",
 424 |               "      <td>I'm a cat slave 🐱 Interested in Blockchain · T...</td>\n",
 425 |               "      <td>2010-01-12 07:00:04</td>\n",
 426 |               "      <td>742</td>\n",
 427 |               "      <td>716</td>\n",
 428 |               "      <td>2444</td>\n",
 429 |               "      <td>False</td>\n",
 430 |               "      <td>2021-02-10 23:53:30</td>\n",
 431 |               "      <td>💹 Trade #Crypto on #Binance \\n\\n📌 Enjoy #Cashb...</td>\n",
 432 |               "      <td>['Crypto', 'Binance', 'Cashback']</td>\n",
 433 |               "      <td>Twitter Web App</td>\n",
 434 |               "      <td>False</td>\n",
 435 |               "    </tr>\n",
 436 |               "  </tbody>\n",
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 447 |               "  </svg>\n",
 448 |               "      </button>\n",
 449 |               "      \n",
 450 |               "  <style>\n",
 451 |               "    .colab-df-container {\n",
 452 |               "      display:flex;\n",
 453 |               "      flex-wrap:wrap;\n",
 454 |               "      gap: 12px;\n",
 455 |               "    }\n",
 456 |               "\n",
 457 |               "    .colab-df-convert {\n",
 458 |               "      background-color: #E8F0FE;\n",
 459 |               "      border: none;\n",
 460 |               "      border-radius: 50%;\n",
 461 |               "      cursor: pointer;\n",
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 465 |               "      padding: 0 0 0 0;\n",
 466 |               "      width: 32px;\n",
 467 |               "    }\n",
 468 |               "\n",
 469 |               "    .colab-df-convert:hover {\n",
 470 |               "      background-color: #E2EBFA;\n",
 471 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 472 |               "      fill: #174EA6;\n",
 473 |               "    }\n",
 474 |               "\n",
 475 |               "    [theme=dark] .colab-df-convert {\n",
 476 |               "      background-color: #3B4455;\n",
 477 |               "      fill: #D2E3FC;\n",
 478 |               "    }\n",
 479 |               "\n",
 480 |               "    [theme=dark] .colab-df-convert:hover {\n",
 481 |               "      background-color: #434B5C;\n",
 482 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 483 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 484 |               "      fill: #FFFFFF;\n",
 485 |               "    }\n",
 486 |               "  </style>\n",
 487 |               "\n",
 488 |               "      <script>\n",
 489 |               "        const buttonEl =\n",
 490 |               "          document.querySelector('#df-7cb09ba3-f7ec-435b-b28f-0efe587bb767 button.colab-df-convert');\n",
 491 |               "        buttonEl.style.display =\n",
 492 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 493 |               "\n",
 494 |               "        async function convertToInteractive(key) {\n",
 495 |               "          const element = document.querySelector('#df-7cb09ba3-f7ec-435b-b28f-0efe587bb767');\n",
 496 |               "          const dataTable =\n",
 497 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 498 |               "                                                     [key], {});\n",
 499 |               "          if (!dataTable) return;\n",
 500 |               "\n",
 501 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
 502 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 503 |               "            + ' to learn more about interactive tables.';\n",
 504 |               "          element.innerHTML = '';\n",
 505 |               "          dataTable['output_type'] = 'display_data';\n",
 506 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
 507 |               "          const docLink = document.createElement('div');\n",
 508 |               "          docLink.innerHTML = docLinkHtml;\n",
 509 |               "          element.appendChild(docLink);\n",
 510 |               "        }\n",
 511 |               "      </script>\n",
 512 |               "    </div>\n",
 513 |               "  </div>\n",
 514 |               "  "
 515 |             ],
 516 |             "text/plain": [
 517 |               "                                  user_name  user_location  \\\n",
 518 |               "0                             DeSota Wilson    Atlanta, GA   \n",
 519 |               "1                                  CryptoND            NaN   \n",
 520 |               "3                      Crypto is the future            NaN   \n",
 521 |               "4  Alex Kirchmaier 🇦🇹🇸🇪 #FactsSuperspreader         Europa   \n",
 522 |               "5                       ZerrBenz™ ⚔ ✪ 20732  Bkk, Thailand   \n",
 523 |               "\n",
 524 |               "                                    user_description         user_created  \\\n",
 525 |               "0  Biz Consultant, real estate, fintech, startups...  2009-04-26 20:05:09   \n",
 526 |               "1  😎 BITCOINLIVE is a Dutch platform aimed at inf...  2019-10-17 20:12:10   \n",
 527 |               "3  I will post a lot of buying signals for BTC tr...  2019-09-28 16:48:12   \n",
 528 |               "4  Co-founder @RENJERJerky | Forbes 30Under30 | I...  2016-02-03 13:15:55   \n",
 529 |               "5  I'm a cat slave 🐱 Interested in Blockchain · T...  2010-01-12 07:00:04   \n",
 530 |               "\n",
 531 |               "   user_followers  user_friends  user_favourites  user_verified  \\\n",
 532 |               "0            8534          7605             4838          False   \n",
 533 |               "1            6769          1532            25483          False   \n",
 534 |               "3             625           129               14          False   \n",
 535 |               "4            1249          1472            10482          False   \n",
 536 |               "5             742           716             2444          False   \n",
 537 |               "\n",
 538 |               "                  date                                               text  \\\n",
 539 |               "0  2021-02-10 23:59:04  Blue Ridge Bank shares halted by NYSE after #b...   \n",
 540 |               "1  2021-02-10 23:58:48  😎 Today, that's this #Thursday, we will do a \"...   \n",
 541 |               "3  2021-02-10 23:54:33  $BTC A big chance in a billion! Price: \\487264...   \n",
 542 |               "4  2021-02-10 23:54:06  This network is secured by 9 508 nodes as of t...   \n",
 543 |               "5  2021-02-10 23:53:30  💹 Trade #Crypto on #Binance \\n\\n📌 Enjoy #Cashb...   \n",
 544 |               "\n",
 545 |               "                                    hashtags               source  is_retweet  \n",
 546 |               "0                                ['bitcoin']      Twitter Web App       False  \n",
 547 |               "1  ['Thursday', 'Btc', 'wallet', 'security']  Twitter for Android       False  \n",
 548 |               "3         ['Bitcoin', 'FX', 'BTC', 'crypto']              dlvr.it       False  \n",
 549 |               "4                                    ['BTC']      Twitter Web App       False  \n",
 550 |               "5          ['Crypto', 'Binance', 'Cashback']      Twitter Web App       False  "
 551 |             ]
 552 |           },
 553 |           "execution_count": 9,
 554 |           "metadata": {},
 555 |           "output_type": "execute_result"
 556 |         }
 557 |       ],
 558 |       "source": [
 559 |         "df. dropna(subset = [\"hashtags\"], inplace=True)\n",
 560 |         "df.head(5)\n"
 561 |       ]
 562 |     },
 563 |     {
 564 |       "cell_type": "markdown",
 565 |       "metadata": {
 566 |         "id": "ralLXRvz0w0l"
 567 |       },
 568 |       "source": [
 569 |         "# TOTAL VALUE SIZE"
 570 |       ]
 571 |     },
 572 |     {
 573 |       "cell_type": "code",
 574 |       "execution_count": null,
 575 |       "metadata": {
 576 |         "colab": {
 577 |           "base_uri": "https://localhost:8080/"
 578 |         },
 579 |         "id": "pPojU5DtfW6V",
 580 |         "outputId": "44d883de-543d-4474-9e04-78b1cdf74654"
 581 |       },
 582 |       "outputs": [
 583 |         {
 584 |           "name": "stdout",
 585 |           "output_type": "stream",
 586 |           "text": [
 587 |             "Tweet Size:  38416\n"
 588 |           ]
 589 |         }
 590 |       ],
 591 |       "source": [
 592 |         "print(\"Tweet Size: \", len(df))\n"
 593 |       ]
 594 |     },
 595 |     {
 596 |       "cell_type": "markdown",
 597 |       "metadata": {
 598 |         "id": "bi8NYwiT03Sc"
 599 |       },
 600 |       "source": [
 601 |         "# GET ONLY TEXTS"
 602 |       ]
 603 |     },
 604 |     {
 605 |       "cell_type": "code",
 606 |       "execution_count": null,
 607 |       "metadata": {
 608 |         "id": "W4A8QnaAb0Xu"
 609 |       },
 610 |       "outputs": [],
 611 |       "source": [
 612 |         "df = df[['text']]\n",
 613 |         "df.columns = ['tweets']\n"
 614 |       ]
 615 |     },
 616 |     {
 617 |       "cell_type": "markdown",
 618 |       "metadata": {
 619 |         "id": "HTqhyJgi09Ms"
 620 |       },
 621 |       "source": [
 622 |         "# CREATE A FUNCTION TO CLEAN THE TWEETS"
 623 |       ]
 624 |     },
 625 |     {
 626 |       "cell_type": "code",
 627 |       "execution_count": null,
 628 |       "metadata": {
 629 |         "id": "wnZk1syCfs6N"
 630 |       },
 631 |       "outputs": [],
 632 |       "source": [
 633 |         "def cleanTwt(twt):\n",
 634 |         "    twt = re.sub(\"#bitcoin\", 'bitcoin', twt)\n",
 635 |         "    twt = re.sub(\"#Bitcoin\", 'Bitcoin', twt)\n",
 636 |         "    twt = re.sub('#[A-Za-z0-9]+', '', twt)\n",
 637 |         "    twt = re.sub('\\\\n', '', twt)\n",
 638 |         "    twt = re.sub('https:\\/\\/\\S+', '', twt)\n",
 639 |         "    return twt\n"
 640 |       ]
 641 |     },
 642 |     {
 643 |       "cell_type": "markdown",
 644 |       "metadata": {
 645 |         "id": "GJDZ_fal1DKF"
 646 |       },
 647 |       "source": []
 648 |     },
 649 |     {
 650 |       "cell_type": "code",
 651 |       "execution_count": null,
 652 |       "metadata": {
 653 |         "colab": {
 654 |           "base_uri": "https://localhost:8080/",
 655 |           "height": 423
 656 |         },
 657 |         "id": "fZ5-oFTSfs7N",
 658 |         "outputId": "ddf670a2-a136-4d9c-bc91-f5b8b259d81b"
 659 |       },
 660 |       "outputs": [
 661 |         {
 662 |           "data": {
 663 |             "text/html": [
 664 |               "\n",
 665 |               "  <div id=\"df-0f5a0813-392b-4a8b-82d7-4b0826d26c55\">\n",
 666 |               "    <div class=\"colab-df-container\">\n",
 667 |               "      <div>\n",
 668 |               "<style scoped>\n",
 669 |               "    .dataframe tbody tr th:only-of-type {\n",
 670 |               "        vertical-align: middle;\n",
 671 |               "    }\n",
 672 |               "\n",
 673 |               "    .dataframe tbody tr th {\n",
 674 |               "        vertical-align: top;\n",
 675 |               "    }\n",
 676 |               "\n",
 677 |               "    .dataframe thead th {\n",
 678 |               "        text-align: right;\n",
 679 |               "    }\n",
 680 |               "</style>\n",
 681 |               "<table border=\"1\" class=\"dataframe\">\n",
 682 |               "  <thead>\n",
 683 |               "    <tr style=\"text-align: right;\">\n",
 684 |               "      <th></th>\n",
 685 |               "      <th>tweets</th>\n",
 686 |               "      <th>cleaned_tweets</th>\n",
 687 |               "    </tr>\n",
 688 |               "  </thead>\n",
 689 |               "  <tbody>\n",
 690 |               "    <tr>\n",
 691 |               "      <th>0</th>\n",
 692 |               "      <td>Blue Ridge Bank shares halted by NYSE after #b...</td>\n",
 693 |               "      <td>Blue Ridge Bank shares halted by NYSE after bi...</td>\n",
 694 |               "    </tr>\n",
 695 |               "    <tr>\n",
 696 |               "      <th>1</th>\n",
 697 |               "      <td>😎 Today, that's this #Thursday, we will do a \"...</td>\n",
 698 |               "      <td>😎 Today, that's this , we will do a \"🎬 Take 2\"...</td>\n",
 699 |               "    </tr>\n",
 700 |               "    <tr>\n",
 701 |               "      <th>3</th>\n",
 702 |               "      <td>$BTC A big chance in a billion! Price: \\487264...</td>\n",
 703 |               "      <td>$BTC A big chance in a billion! Price: \\487264...</td>\n",
 704 |               "    </tr>\n",
 705 |               "    <tr>\n",
 706 |               "      <th>4</th>\n",
 707 |               "      <td>This network is secured by 9 508 nodes as of t...</td>\n",
 708 |               "      <td>This network is secured by 9 508 nodes as of t...</td>\n",
 709 |               "    </tr>\n",
 710 |               "    <tr>\n",
 711 |               "      <th>5</th>\n",
 712 |               "      <td>💹 Trade #Crypto on #Binance \\n\\n📌 Enjoy #Cashb...</td>\n",
 713 |               "      <td>💹 Trade  on  📌 Enjoy  10% of the Trading fee📌 ...</td>\n",
 714 |               "    </tr>\n",
 715 |               "    <tr>\n",
 716 |               "      <th>...</th>\n",
 717 |               "      <td>...</td>\n",
 718 |               "      <td>...</td>\n",
 719 |               "    </tr>\n",
 720 |               "    <tr>\n",
 721 |               "      <th>48577</th>\n",
 722 |               "      <td>$BTC pop or drop?\\n\\n#volatility in 3...2...1....</td>\n",
 723 |               "      <td>$BTC pop or drop? in 3...2...1... Bitcoin  or</td>\n",
 724 |               "    </tr>\n",
 725 |               "    <tr>\n",
 726 |               "      <th>48578</th>\n",
 727 |               "      <td>Bill Gates advised against investing in #Bitco...</td>\n",
 728 |               "      <td>Bill Gates advised against investing in Bitcoi...</td>\n",
 729 |               "    </tr>\n",
 730 |               "    <tr>\n",
 731 |               "      <th>48579</th>\n",
 732 |               "      <td>#Bitcoin price in US Dollar #BTC $usd #BTCUSD ...</td>\n",
 733 |               "      <td>Bitcoin price in US Dollar  $usd  $XBT  BTC Pr...</td>\n",
 734 |               "    </tr>\n",
 735 |               "    <tr>\n",
 736 |               "      <th>48580</th>\n",
 737 |               "      <td>Market Id : BTC-AUD\\nLast Price :74260.91\\n24 ...</td>\n",
 738 |               "      <td>Market Id : BTC-AUDLast Price :74260.9124 Hour...</td>\n",
 739 |               "    </tr>\n",
 740 |               "    <tr>\n",
 741 |               "      <th>48581</th>\n",
 742 |               "      <td>Bitcoin retesting ATH ❤️\\n#Bitcoin #btc #Crypto</td>\n",
 743 |               "      <td>Bitcoin retesting ATH ❤️Bitcoin</td>\n",
 744 |               "    </tr>\n",
 745 |               "  </tbody>\n",
 746 |               "</table>\n",
 747 |               "<p>38416 rows × 2 columns</p>\n",
 748 |               "</div>\n",
 749 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f5a0813-392b-4a8b-82d7-4b0826d26c55')\"\n",
 750 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
 751 |               "              style=\"display:none;\">\n",
 752 |               "        \n",
 753 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 754 |               "       width=\"24px\">\n",
 755 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
 756 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
 757 |               "  </svg>\n",
 758 |               "      </button>\n",
 759 |               "      \n",
 760 |               "  <style>\n",
 761 |               "    .colab-df-container {\n",
 762 |               "      display:flex;\n",
 763 |               "      flex-wrap:wrap;\n",
 764 |               "      gap: 12px;\n",
 765 |               "    }\n",
 766 |               "\n",
 767 |               "    .colab-df-convert {\n",
 768 |               "      background-color: #E8F0FE;\n",
 769 |               "      border: none;\n",
 770 |               "      border-radius: 50%;\n",
 771 |               "      cursor: pointer;\n",
 772 |               "      display: none;\n",
 773 |               "      fill: #1967D2;\n",
 774 |               "      height: 32px;\n",
 775 |               "      padding: 0 0 0 0;\n",
 776 |               "      width: 32px;\n",
 777 |               "    }\n",
 778 |               "\n",
 779 |               "    .colab-df-convert:hover {\n",
 780 |               "      background-color: #E2EBFA;\n",
 781 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 782 |               "      fill: #174EA6;\n",
 783 |               "    }\n",
 784 |               "\n",
 785 |               "    [theme=dark] .colab-df-convert {\n",
 786 |               "      background-color: #3B4455;\n",
 787 |               "      fill: #D2E3FC;\n",
 788 |               "    }\n",
 789 |               "\n",
 790 |               "    [theme=dark] .colab-df-convert:hover {\n",
 791 |               "      background-color: #434B5C;\n",
 792 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 793 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 794 |               "      fill: #FFFFFF;\n",
 795 |               "    }\n",
 796 |               "  </style>\n",
 797 |               "\n",
 798 |               "      <script>\n",
 799 |               "        const buttonEl =\n",
 800 |               "          document.querySelector('#df-0f5a0813-392b-4a8b-82d7-4b0826d26c55 button.colab-df-convert');\n",
 801 |               "        buttonEl.style.display =\n",
 802 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 803 |               "\n",
 804 |               "        async function convertToInteractive(key) {\n",
 805 |               "          const element = document.querySelector('#df-0f5a0813-392b-4a8b-82d7-4b0826d26c55');\n",
 806 |               "          const dataTable =\n",
 807 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 808 |               "                                                     [key], {});\n",
 809 |               "          if (!dataTable) return;\n",
 810 |               "\n",
 811 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
 812 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 813 |               "            + ' to learn more about interactive tables.';\n",
 814 |               "          element.innerHTML = '';\n",
 815 |               "          dataTable['output_type'] = 'display_data';\n",
 816 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
 817 |               "          const docLink = document.createElement('div');\n",
 818 |               "          docLink.innerHTML = docLinkHtml;\n",
 819 |               "          element.appendChild(docLink);\n",
 820 |               "        }\n",
 821 |               "      </script>\n",
 822 |               "    </div>\n",
 823 |               "  </div>\n",
 824 |               "  "
 825 |             ],
 826 |             "text/plain": [
 827 |               "                                                  tweets  \\\n",
 828 |               "0      Blue Ridge Bank shares halted by NYSE after #b...   \n",
 829 |               "1      😎 Today, that's this #Thursday, we will do a \"...   \n",
 830 |               "3      $BTC A big chance in a billion! Price: \\487264...   \n",
 831 |               "4      This network is secured by 9 508 nodes as of t...   \n",
 832 |               "5      💹 Trade #Crypto on #Binance \\n\\n📌 Enjoy #Cashb...   \n",
 833 |               "...                                                  ...   \n",
 834 |               "48577  $BTC pop or drop?\\n\\n#volatility in 3...2...1....   \n",
 835 |               "48578  Bill Gates advised against investing in #Bitco...   \n",
 836 |               "48579  #Bitcoin price in US Dollar #BTC $usd #BTCUSD ...   \n",
 837 |               "48580  Market Id : BTC-AUD\\nLast Price :74260.91\\n24 ...   \n",
 838 |               "48581    Bitcoin retesting ATH ❤️\\n#Bitcoin #btc #Crypto   \n",
 839 |               "\n",
 840 |               "                                          cleaned_tweets  \n",
 841 |               "0      Blue Ridge Bank shares halted by NYSE after bi...  \n",
 842 |               "1      😎 Today, that's this , we will do a \"🎬 Take 2\"...  \n",
 843 |               "3      $BTC A big chance in a billion! Price: \\487264...  \n",
 844 |               "4      This network is secured by 9 508 nodes as of t...  \n",
 845 |               "5      💹 Trade  on  📌 Enjoy  10% of the Trading fee📌 ...  \n",
 846 |               "...                                                  ...  \n",
 847 |               "48577    $BTC pop or drop? in 3...2...1... Bitcoin  or    \n",
 848 |               "48578  Bill Gates advised against investing in Bitcoi...  \n",
 849 |               "48579  Bitcoin price in US Dollar  $usd  $XBT  BTC Pr...  \n",
 850 |               "48580  Market Id : BTC-AUDLast Price :74260.9124 Hour...  \n",
 851 |               "48581                  Bitcoin retesting ATH ❤️Bitcoin    \n",
 852 |               "\n",
 853 |               "[38416 rows x 2 columns]"
 854 |             ]
 855 |           },
 856 |           "execution_count": 13,
 857 |           "metadata": {},
 858 |           "output_type": "execute_result"
 859 |         }
 860 |       ],
 861 |       "source": [
 862 |         "df['cleaned_tweets'] = df['tweets'].apply(cleanTwt)\n",
 863 |         "df\n"
 864 |       ]
 865 |     },
 866 |     {
 867 |       "cell_type": "markdown",
 868 |       "metadata": {
 869 |         "id": "hwwUp2261GFD"
 870 |       },
 871 |       "source": [
 872 |         "# CREATE FUNCTION TO GET SUNJECTIVITY AND POLARITY"
 873 |       ]
 874 |     },
 875 |     {
 876 |       "cell_type": "code",
 877 |       "execution_count": null,
 878 |       "metadata": {
 879 |         "id": "Ehye_yv-f_71"
 880 |       },
 881 |       "outputs": [],
 882 |       "source": [
 883 |         "def getSubjectivity(twt):\n",
 884 |         "    return TextBlob(twt).sentiment.subjectivity\n",
 885 |         "\n",
 886 |         "def getPolarity(twt):\n",
 887 |         "    return TextBlob(twt).sentiment.polarity\n",
 888 |         "\n",
 889 |         "df['subjectivity'] = df['cleaned_tweets'].apply(getSubjectivity)\n",
 890 |         "df['polarity'] = df['cleaned_tweets'].apply(getPolarity)\n"
 891 |       ]
 892 |     },
 893 |     {
 894 |       "cell_type": "code",
 895 |       "execution_count": null,
 896 |       "metadata": {
 897 |         "colab": {
 898 |           "base_uri": "https://localhost:8080/",
 899 |           "height": 206
 900 |         },
 901 |         "id": "TLRQZZYxgKh1",
 902 |         "outputId": "a7c2dc0a-8ee2-4da7-91d9-eae036e156fc"
 903 |       },
 904 |       "outputs": [
 905 |         {
 906 |           "data": {
 907 |             "text/html": [
 908 |               "\n",
 909 |               "  <div id=\"df-92a251ec-1175-4309-ac53-fe7d5252164e\">\n",
 910 |               "    <div class=\"colab-df-container\">\n",
 911 |               "      <div>\n",
 912 |               "<style scoped>\n",
 913 |               "    .dataframe tbody tr th:only-of-type {\n",
 914 |               "        vertical-align: middle;\n",
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 916 |               "\n",
 917 |               "    .dataframe tbody tr th {\n",
 918 |               "        vertical-align: top;\n",
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 922 |               "        text-align: right;\n",
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 924 |               "</style>\n",
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 926 |               "  <thead>\n",
 927 |               "    <tr style=\"text-align: right;\">\n",
 928 |               "      <th></th>\n",
 929 |               "      <th>tweets</th>\n",
 930 |               "      <th>cleaned_tweets</th>\n",
 931 |               "      <th>subjectivity</th>\n",
 932 |               "      <th>polarity</th>\n",
 933 |               "    </tr>\n",
 934 |               "  </thead>\n",
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 937 |               "      <th>0</th>\n",
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 939 |               "      <td>Blue Ridge Bank shares halted by NYSE after bi...</td>\n",
 940 |               "      <td>0.1</td>\n",
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 942 |               "    </tr>\n",
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 944 |               "      <th>1</th>\n",
 945 |               "      <td>😎 Today, that's this #Thursday, we will do a \"...</td>\n",
 946 |               "      <td>😎 Today, that's this , we will do a \"🎬 Take 2\"...</td>\n",
 947 |               "      <td>0.0</td>\n",
 948 |               "      <td>0.0</td>\n",
 949 |               "    </tr>\n",
 950 |               "    <tr>\n",
 951 |               "      <th>3</th>\n",
 952 |               "      <td>$BTC A big chance in a billion! Price: \\487264...</td>\n",
 953 |               "      <td>$BTC A big chance in a billion! Price: \\487264...</td>\n",
 954 |               "      <td>0.1</td>\n",
 955 |               "      <td>0.0</td>\n",
 956 |               "    </tr>\n",
 957 |               "    <tr>\n",
 958 |               "      <th>4</th>\n",
 959 |               "      <td>This network is secured by 9 508 nodes as of t...</td>\n",
 960 |               "      <td>This network is secured by 9 508 nodes as of t...</td>\n",
 961 |               "      <td>0.1</td>\n",
 962 |               "      <td>0.0</td>\n",
 963 |               "    </tr>\n",
 964 |               "    <tr>\n",
 965 |               "      <th>5</th>\n",
 966 |               "      <td>💹 Trade #Crypto on #Binance \\n\\n📌 Enjoy #Cashb...</td>\n",
 967 |               "      <td>💹 Trade  on  📌 Enjoy  10% of the Trading fee📌 ...</td>\n",
 968 |               "      <td>0.5</td>\n",
 969 |               "      <td>0.4</td>\n",
 970 |               "    </tr>\n",
 971 |               "  </tbody>\n",
 972 |               "</table>\n",
 973 |               "</div>\n",
 974 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-92a251ec-1175-4309-ac53-fe7d5252164e')\"\n",
 975 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
 976 |               "              style=\"display:none;\">\n",
 977 |               "        \n",
 978 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 979 |               "       width=\"24px\">\n",
 980 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
 981 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
 982 |               "  </svg>\n",
 983 |               "      </button>\n",
 984 |               "      \n",
 985 |               "  <style>\n",
 986 |               "    .colab-df-container {\n",
 987 |               "      display:flex;\n",
 988 |               "      flex-wrap:wrap;\n",
 989 |               "      gap: 12px;\n",
 990 |               "    }\n",
 991 |               "\n",
 992 |               "    .colab-df-convert {\n",
 993 |               "      background-color: #E8F0FE;\n",
 994 |               "      border: none;\n",
 995 |               "      border-radius: 50%;\n",
 996 |               "      cursor: pointer;\n",
 997 |               "      display: none;\n",
 998 |               "      fill: #1967D2;\n",
 999 |               "      height: 32px;\n",
1000 |               "      padding: 0 0 0 0;\n",
1001 |               "      width: 32px;\n",
1002 |               "    }\n",
1003 |               "\n",
1004 |               "    .colab-df-convert:hover {\n",
1005 |               "      background-color: #E2EBFA;\n",
1006 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1007 |               "      fill: #174EA6;\n",
1008 |               "    }\n",
1009 |               "\n",
1010 |               "    [theme=dark] .colab-df-convert {\n",
1011 |               "      background-color: #3B4455;\n",
1012 |               "      fill: #D2E3FC;\n",
1013 |               "    }\n",
1014 |               "\n",
1015 |               "    [theme=dark] .colab-df-convert:hover {\n",
1016 |               "      background-color: #434B5C;\n",
1017 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1018 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1019 |               "      fill: #FFFFFF;\n",
1020 |               "    }\n",
1021 |               "  </style>\n",
1022 |               "\n",
1023 |               "      <script>\n",
1024 |               "        const buttonEl =\n",
1025 |               "          document.querySelector('#df-92a251ec-1175-4309-ac53-fe7d5252164e button.colab-df-convert');\n",
1026 |               "        buttonEl.style.display =\n",
1027 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1028 |               "\n",
1029 |               "        async function convertToInteractive(key) {\n",
1030 |               "          const element = document.querySelector('#df-92a251ec-1175-4309-ac53-fe7d5252164e');\n",
1031 |               "          const dataTable =\n",
1032 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1033 |               "                                                     [key], {});\n",
1034 |               "          if (!dataTable) return;\n",
1035 |               "\n",
1036 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1037 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1038 |               "            + ' to learn more about interactive tables.';\n",
1039 |               "          element.innerHTML = '';\n",
1040 |               "          dataTable['output_type'] = 'display_data';\n",
1041 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
1042 |               "          const docLink = document.createElement('div');\n",
1043 |               "          docLink.innerHTML = docLinkHtml;\n",
1044 |               "          element.appendChild(docLink);\n",
1045 |               "        }\n",
1046 |               "      </script>\n",
1047 |               "    </div>\n",
1048 |               "  </div>\n",
1049 |               "  "
1050 |             ],
1051 |             "text/plain": [
1052 |               "                                              tweets  \\\n",
1053 |               "0  Blue Ridge Bank shares halted by NYSE after #b...   \n",
1054 |               "1  😎 Today, that's this #Thursday, we will do a \"...   \n",
1055 |               "3  $BTC A big chance in a billion! Price: \\487264...   \n",
1056 |               "4  This network is secured by 9 508 nodes as of t...   \n",
1057 |               "5  💹 Trade #Crypto on #Binance \\n\\n📌 Enjoy #Cashb...   \n",
1058 |               "\n",
1059 |               "                                      cleaned_tweets  subjectivity  polarity  \n",
1060 |               "0  Blue Ridge Bank shares halted by NYSE after bi...           0.1       0.0  \n",
1061 |               "1  😎 Today, that's this , we will do a \"🎬 Take 2\"...           0.0       0.0  \n",
1062 |               "3  $BTC A big chance in a billion! Price: \\487264...           0.1       0.0  \n",
1063 |               "4  This network is secured by 9 508 nodes as of t...           0.1       0.0  \n",
1064 |               "5  💹 Trade  on  📌 Enjoy  10% of the Trading fee📌 ...           0.5       0.4  "
1065 |             ]
1066 |           },
1067 |           "execution_count": 15,
1068 |           "metadata": {},
1069 |           "output_type": "execute_result"
1070 |         }
1071 |       ],
1072 |       "source": [
1073 |         "df.head(5)\n"
1074 |       ]
1075 |     },
1076 |     {
1077 |       "cell_type": "markdown",
1078 |       "metadata": {
1079 |         "id": "K40hDtNO1bnk"
1080 |       },
1081 |       "source": [
1082 |         "# CREATE A FUNCTION TO GET SENTIMENT TEXT"
1083 |       ]
1084 |     },
1085 |     {
1086 |       "cell_type": "code",
1087 |       "execution_count": null,
1088 |       "metadata": {
1089 |         "id": "gt8bzWOkgKgn"
1090 |       },
1091 |       "outputs": [],
1092 |       "source": [
1093 |         "def getSentiment(score):\n",
1094 |         "    if score < 0:\n",
1095 |         "        return \"negative\"\n",
1096 |         "    elif score == 0:\n",
1097 |         "        return \"neutral\"\n",
1098 |         "    else:\n",
1099 |         "        return \"positive\""
1100 |       ]
1101 |     },
1102 |     {
1103 |       "cell_type": "markdown",
1104 |       "metadata": {
1105 |         "id": "pmDUcE5B1k2D"
1106 |       },
1107 |       "source": [
1108 |         "# CREATE A COLUMN TO STORE THE TEXT SENTIMENT"
1109 |       ]
1110 |     },
1111 |     {
1112 |       "cell_type": "code",
1113 |       "execution_count": null,
1114 |       "metadata": {
1115 |         "colab": {
1116 |           "base_uri": "https://localhost:8080/",
1117 |           "height": 206
1118 |         },
1119 |         "id": "5gIqV8m8gcuE",
1120 |         "outputId": "326a637b-2a60-4442-d1af-b0ac52e9a693"
1121 |       },
1122 |       "outputs": [
1123 |         {
1124 |           "data": {
1125 |             "text/html": [
1126 |               "\n",
1127 |               "  <div id=\"df-bf1789f4-aeaa-48d4-a21b-8e598185ae68\">\n",
1128 |               "    <div class=\"colab-df-container\">\n",
1129 |               "      <div>\n",
1130 |               "<style scoped>\n",
1131 |               "    .dataframe tbody tr th:only-of-type {\n",
1132 |               "        vertical-align: middle;\n",
1133 |               "    }\n",
1134 |               "\n",
1135 |               "    .dataframe tbody tr th {\n",
1136 |               "        vertical-align: top;\n",
1137 |               "    }\n",
1138 |               "\n",
1139 |               "    .dataframe thead th {\n",
1140 |               "        text-align: right;\n",
1141 |               "    }\n",
1142 |               "</style>\n",
1143 |               "<table border=\"1\" class=\"dataframe\">\n",
1144 |               "  <thead>\n",
1145 |               "    <tr style=\"text-align: right;\">\n",
1146 |               "      <th></th>\n",
1147 |               "      <th>tweets</th>\n",
1148 |               "      <th>cleaned_tweets</th>\n",
1149 |               "      <th>subjectivity</th>\n",
1150 |               "      <th>polarity</th>\n",
1151 |               "      <th>sentiment</th>\n",
1152 |               "    </tr>\n",
1153 |               "  </thead>\n",
1154 |               "  <tbody>\n",
1155 |               "    <tr>\n",
1156 |               "      <th>0</th>\n",
1157 |               "      <td>Blue Ridge Bank shares halted by NYSE after #b...</td>\n",
1158 |               "      <td>Blue Ridge Bank shares halted by NYSE after bi...</td>\n",
1159 |               "      <td>0.1</td>\n",
1160 |               "      <td>0.0</td>\n",
1161 |               "      <td>neutral</td>\n",
1162 |               "    </tr>\n",
1163 |               "    <tr>\n",
1164 |               "      <th>1</th>\n",
1165 |               "      <td>😎 Today, that's this #Thursday, we will do a \"...</td>\n",
1166 |               "      <td>😎 Today, that's this , we will do a \"🎬 Take 2\"...</td>\n",
1167 |               "      <td>0.0</td>\n",
1168 |               "      <td>0.0</td>\n",
1169 |               "      <td>neutral</td>\n",
1170 |               "    </tr>\n",
1171 |               "    <tr>\n",
1172 |               "      <th>3</th>\n",
1173 |               "      <td>$BTC A big chance in a billion! Price: \\487264...</td>\n",
1174 |               "      <td>$BTC A big chance in a billion! Price: \\487264...</td>\n",
1175 |               "      <td>0.1</td>\n",
1176 |               "      <td>0.0</td>\n",
1177 |               "      <td>neutral</td>\n",
1178 |               "    </tr>\n",
1179 |               "    <tr>\n",
1180 |               "      <th>4</th>\n",
1181 |               "      <td>This network is secured by 9 508 nodes as of t...</td>\n",
1182 |               "      <td>This network is secured by 9 508 nodes as of t...</td>\n",
1183 |               "      <td>0.1</td>\n",
1184 |               "      <td>0.0</td>\n",
1185 |               "      <td>neutral</td>\n",
1186 |               "    </tr>\n",
1187 |               "    <tr>\n",
1188 |               "      <th>5</th>\n",
1189 |               "      <td>💹 Trade #Crypto on #Binance \\n\\n📌 Enjoy #Cashb...</td>\n",
1190 |               "      <td>💹 Trade  on  📌 Enjoy  10% of the Trading fee📌 ...</td>\n",
1191 |               "      <td>0.5</td>\n",
1192 |               "      <td>0.4</td>\n",
1193 |               "      <td>positive</td>\n",
1194 |               "    </tr>\n",
1195 |               "  </tbody>\n",
1196 |               "</table>\n",
1197 |               "</div>\n",
1198 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf1789f4-aeaa-48d4-a21b-8e598185ae68')\"\n",
1199 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
1200 |               "              style=\"display:none;\">\n",
1201 |               "        \n",
1202 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1203 |               "       width=\"24px\">\n",
1204 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
1205 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
1206 |               "  </svg>\n",
1207 |               "      </button>\n",
1208 |               "      \n",
1209 |               "  <style>\n",
1210 |               "    .colab-df-container {\n",
1211 |               "      display:flex;\n",
1212 |               "      flex-wrap:wrap;\n",
1213 |               "      gap: 12px;\n",
1214 |               "    }\n",
1215 |               "\n",
1216 |               "    .colab-df-convert {\n",
1217 |               "      background-color: #E8F0FE;\n",
1218 |               "      border: none;\n",
1219 |               "      border-radius: 50%;\n",
1220 |               "      cursor: pointer;\n",
1221 |               "      display: none;\n",
1222 |               "      fill: #1967D2;\n",
1223 |               "      height: 32px;\n",
1224 |               "      padding: 0 0 0 0;\n",
1225 |               "      width: 32px;\n",
1226 |               "    }\n",
1227 |               "\n",
1228 |               "    .colab-df-convert:hover {\n",
1229 |               "      background-color: #E2EBFA;\n",
1230 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1231 |               "      fill: #174EA6;\n",
1232 |               "    }\n",
1233 |               "\n",
1234 |               "    [theme=dark] .colab-df-convert {\n",
1235 |               "      background-color: #3B4455;\n",
1236 |               "      fill: #D2E3FC;\n",
1237 |               "    }\n",
1238 |               "\n",
1239 |               "    [theme=dark] .colab-df-convert:hover {\n",
1240 |               "      background-color: #434B5C;\n",
1241 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1242 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1243 |               "      fill: #FFFFFF;\n",
1244 |               "    }\n",
1245 |               "  </style>\n",
1246 |               "\n",
1247 |               "      <script>\n",
1248 |               "        const buttonEl =\n",
1249 |               "          document.querySelector('#df-bf1789f4-aeaa-48d4-a21b-8e598185ae68 button.colab-df-convert');\n",
1250 |               "        buttonEl.style.display =\n",
1251 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1252 |               "\n",
1253 |               "        async function convertToInteractive(key) {\n",
1254 |               "          const element = document.querySelector('#df-bf1789f4-aeaa-48d4-a21b-8e598185ae68');\n",
1255 |               "          const dataTable =\n",
1256 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1257 |               "                                                     [key], {});\n",
1258 |               "          if (!dataTable) return;\n",
1259 |               "\n",
1260 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1261 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1262 |               "            + ' to learn more about interactive tables.';\n",
1263 |               "          element.innerHTML = '';\n",
1264 |               "          dataTable['output_type'] = 'display_data';\n",
1265 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
1266 |               "          const docLink = document.createElement('div');\n",
1267 |               "          docLink.innerHTML = docLinkHtml;\n",
1268 |               "          element.appendChild(docLink);\n",
1269 |               "        }\n",
1270 |               "      </script>\n",
1271 |               "    </div>\n",
1272 |               "  </div>\n",
1273 |               "  "
1274 |             ],
1275 |             "text/plain": [
1276 |               "                                              tweets  \\\n",
1277 |               "0  Blue Ridge Bank shares halted by NYSE after #b...   \n",
1278 |               "1  😎 Today, that's this #Thursday, we will do a \"...   \n",
1279 |               "3  $BTC A big chance in a billion! Price: \\487264...   \n",
1280 |               "4  This network is secured by 9 508 nodes as of t...   \n",
1281 |               "5  💹 Trade #Crypto on #Binance \\n\\n📌 Enjoy #Cashb...   \n",
1282 |               "\n",
1283 |               "                                      cleaned_tweets  subjectivity  polarity  \\\n",
1284 |               "0  Blue Ridge Bank shares halted by NYSE after bi...           0.1       0.0   \n",
1285 |               "1  😎 Today, that's this , we will do a \"🎬 Take 2\"...           0.0       0.0   \n",
1286 |               "3  $BTC A big chance in a billion! Price: \\487264...           0.1       0.0   \n",
1287 |               "4  This network is secured by 9 508 nodes as of t...           0.1       0.0   \n",
1288 |               "5  💹 Trade  on  📌 Enjoy  10% of the Trading fee📌 ...           0.5       0.4   \n",
1289 |               "\n",
1290 |               "  sentiment  \n",
1291 |               "0   neutral  \n",
1292 |               "1   neutral  \n",
1293 |               "3   neutral  \n",
1294 |               "4   neutral  \n",
1295 |               "5  positive  "
1296 |             ]
1297 |           },
1298 |           "execution_count": 17,
1299 |           "metadata": {},
1300 |           "output_type": "execute_result"
1301 |         }
1302 |       ],
1303 |       "source": [
1304 |         "df['sentiment'] = df['polarity'].apply(getSentiment)\n",
1305 |         "\n",
1306 |         "\n",
1307 |         "df.head()\n"
1308 |       ]
1309 |     },
1310 |     {
1311 |       "cell_type": "markdown",
1312 |       "metadata": {
1313 |         "id": "FM-tTlfe1vQb"
1314 |       },
1315 |       "source": [
1316 |         "# CREATE A SCATTER PLOT TO SHOW SUBJECTIVITY AND POLARITY\n",
1317 |         "\n",
1318 |         "\n",
1319 |         "\n",
1320 |         "\n",
1321 |         "\n"
1322 |       ]
1323 |     },
1324 |     {
1325 |       "cell_type": "code",
1326 |       "execution_count": null,
1327 |       "metadata": {
1328 |         "colab": {
1329 |           "base_uri": "https://localhost:8080/",
1330 |           "height": 872
1331 |         },
1332 |         "id": "x9pt_NdIglwd",
1333 |         "outputId": "3778d785-0e34-4831-a73d-193f0e87a8f1"
1334 |       },
1335 |       "outputs": [
1336 |         {
1337 |           "data": {
1338 |             "image/png": 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\n",
1339 |             "text/plain": [
1340 |               "<Figure size 1400x1000 with 1 Axes>"
1341 |             ]
1342 |           },
1343 |           "metadata": {},
1344 |           "output_type": "display_data"
1345 |         }
1346 |       ],
1347 |       "source": [
1348 |         "plt.figure(figsize=(14,10))\n",
1349 |         "\n",
1350 |         "for i in range(0, 2000):\n",
1351 |         "    plt.scatter(df[\"polarity\"].iloc[[i]].values[0], df[\"subjectivity\"].iloc[[i]].values[0], color=\"Purple\")\n",
1352 |         "\n",
1353 |         "plt.title(\"Sentiment Analysis Scatter Plot\")\n",
1354 |         "plt.xlabel('polarity')\n",
1355 |         "plt.ylabel('subjectivity')\n",
1356 |         "plt.show()\n"
1357 |       ]
1358 |     },
1359 |     {
1360 |       "cell_type": "markdown",
1361 |       "metadata": {
1362 |         "id": "Vw0PJ3eS1-fn"
1363 |       },
1364 |       "source": [
1365 |         "CREATE A BAR CHART TO SHOW THE COUT OF POSITIVE, NEUTRAL AND NEGATIVE SENTIMENTS"
1366 |       ]
1367 |     },
1368 |     {
1369 |       "cell_type": "code",
1370 |       "execution_count": null,
1371 |       "metadata": {
1372 |         "colab": {
1373 |           "base_uri": "https://localhost:8080/",
1374 |           "height": 519
1375 |         },
1376 |         "id": "F1Sx5oTlg2xu",
1377 |         "outputId": "1b795340-6282-445c-a065-14ec2b582664"
1378 |       },
1379 |       "outputs": [
1380 |         {
1381 |           "data": {
1382 |             "image/png": 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\n",
1383 |             "text/plain": [
1384 |               "<Figure size 640x480 with 1 Axes>"
1385 |             ]
1386 |           },
1387 |           "metadata": {},
1388 |           "output_type": "display_data"
1389 |         }
1390 |       ],
1391 |       "source": [
1392 |         "df['sentiment'].value_counts().plot(kind=\"bar\")\n",
1393 |         "plt.title(\"Sentiment Analysis Scatter Plot\")\n",
1394 |         "plt.xlabel(\"Polarity\")\n",
1395 |         "plt.ylabel(\"Subjectivity\")\n",
1396 |         "plt.show()"
1397 |       ]
1398 |     },
1399 |     {
1400 |       "cell_type": "code",
1401 |       "execution_count": null,
1402 |       "metadata": {
1403 |         "id": "GEKuwP0dg2y1"
1404 |       },
1405 |       "outputs": [],
1406 |       "source": []
1407 |     }
1408 |   ],
1409 |   "metadata": {
1410 |     "colab": {
1411 |       "provenance": [],
1412 |       "include_colab_link": true
1413 |     },
1414 |     "kernelspec": {
1415 |       "display_name": "Python 3",
1416 |       "name": "python3"
1417 |     },
1418 |     "language_info": {
1419 |       "name": "python"
1420 |     }
1421 |   },
1422 |   "nbformat": 4,
1423 |   "nbformat_minor": 0
1424 | }


--------------------------------------------------------------------------------