├── README.md └── Mining_Online_Reviews_using_Topic_Modeling_(LDA).ipynb /README.md: -------------------------------------------------------------------------------- 1 | # Mining Online Reviews using Topic Modeling (LDA) 2 | -------------------------------------------------------------------------------- /Mining_Online_Reviews_using_Topic_Modeling_(LDA).ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Mining Online Reviews using Topic Modeling (LDA)", 7 | "version": "0.3.2", 8 | "provenance": [], 9 | "collapsed_sections": [] 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | } 15 | }, 16 | "cells": [ 17 | { 18 | "metadata": { 19 | "id": "U32BHTBUq7D6", 20 | "colab_type": "code", 21 | "colab": { 22 | "base_uri": "https://localhost:8080/", 23 | "height": 1063 24 | }, 25 | "outputId": "6f2ba14c-1484-4a32-e02e-0cc4106fed82" 26 | }, 27 | "cell_type": "code", 28 | "source": [ 29 | "!pip install PyDrive\n", 30 | "!pip install gensim\n", 31 | "!pip install pyldavis\n", 32 | "!python -m spacy download en" 33 | ], 34 | "execution_count": 84, 35 | "outputs": [ 36 | { 37 | "output_type": "stream", 38 | "text": [ 39 | "Requirement already satisfied: PyDrive in /usr/local/lib/python3.6/dist-packages (1.3.1)\n", 40 | "Requirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.6/dist-packages (from PyDrive) (1.6.7)\n", 41 | "Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from PyDrive) (4.1.3)\n", 42 | "Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.6/dist-packages (from PyDrive) (3.13)\n", 43 | "Requirement already satisfied: six<2dev,>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (1.11.0)\n", 44 | "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (3.0.0)\n", 45 | "Requirement already satisfied: httplib2<1dev,>=0.9.2 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (0.11.3)\n", 46 | "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.2.2)\n", 47 | "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.4.4)\n", 48 | "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (4.0)\n", 49 | "Requirement already satisfied: gensim in /usr/local/lib/python3.6/dist-packages (3.6.0)\n", 50 | "Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.11.0)\n", 51 | "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.14.6)\n", 52 | "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.7.1)\n", 53 | "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.6/dist-packages (from gensim) (0.19.1)\n", 54 | "Requirement already satisfied: bz2file in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (0.98)\n", 55 | "Requirement already satisfied: boto3 in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (1.9.23)\n", 56 | "Requirement already satisfied: boto>=2.32 in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (2.49.0)\n", 57 | "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (2.18.4)\n", 58 | "Requirement already satisfied: botocore<1.13.0,>=1.12.23 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim) (1.12.23)\n", 59 | "Requirement already satisfied: s3transfer<0.2.0,>=0.1.10 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim) (0.1.13)\n", 60 | "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim) (0.9.3)\n", 61 | "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (2018.8.24)\n", 62 | "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (1.22)\n", 63 | "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (3.0.4)\n", 64 | "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (2.6)\n", 65 | "Requirement already satisfied: docutils>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.13.0,>=1.12.23->boto3->smart-open>=1.2.1->gensim) (0.14)\n", 66 | "Requirement already satisfied: python-dateutil<3.0.0,>=2.1; python_version >= \"2.7\" in /usr/local/lib/python3.6/dist-packages (from botocore<1.13.0,>=1.12.23->boto3->smart-open>=1.2.1->gensim) (2.5.3)\n", 67 | "Requirement already satisfied: pyldavis in /usr/local/lib/python3.6/dist-packages (2.1.2)\n", 68 | "Requirement already satisfied: pytest in /usr/local/lib/python3.6/dist-packages (from pyldavis) (3.8.2)\n", 69 | "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (1.14.6)\n", 70 | "Requirement already satisfied: pandas>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.22.0)\n", 71 | "Requirement already satisfied: joblib>=0.8.4 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.12.5)\n", 72 | "Requirement already satisfied: jinja2>=2.7.2 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (2.10)\n", 73 | "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.16.0)\n", 74 | "Requirement already satisfied: wheel>=0.23.0 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.32.1)\n", 75 | "Requirement already satisfied: funcy in /usr/local/lib/python3.6/dist-packages (from pyldavis) (1.11)\n", 76 | "Requirement already satisfied: numexpr in /usr/local/lib/python3.6/dist-packages (from pyldavis) (2.6.8)\n", 77 | "Requirement already satisfied: scipy>=0.18.0 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.19.1)\n", 78 | "Requirement already satisfied: pluggy>=0.7 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (0.7.1)\n", 79 | "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (1.7.0)\n", 80 | "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (1.11.0)\n", 81 | "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (1.2.1)\n", 82 | "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (39.1.0)\n", 83 | "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (4.3.0)\n", 84 | "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (18.2.0)\n", 85 | "Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.17.0->pyldavis) (2.5.3)\n", 86 | "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.17.0->pyldavis) (2018.5)\n", 87 | "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.7.2->pyldavis) (1.0)\n", 88 | "Requirement already satisfied: en_core_web_sm==2.0.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz#egg=en_core_web_sm==2.0.0 in /usr/local/lib/python3.6/dist-packages (2.0.0)\n", 89 | "\n", 90 | "\u001b[93m Linking successful\u001b[0m\n", 91 | " /usr/local/lib/python3.6/dist-packages/en_core_web_sm -->\n", 92 | " /usr/local/lib/python3.6/dist-packages/spacy/data/en\n", 93 | "\n", 94 | " You can now load the model via spacy.load('en')\n", 95 | "\n" 96 | ], 97 | "name": "stdout" 98 | } 99 | ] 100 | }, 101 | { 102 | "metadata": { 103 | "id": "z3LQhDUitB_I", 104 | "colab_type": "code", 105 | "colab": { 106 | "base_uri": "https://localhost:8080/", 107 | "height": 71 108 | }, 109 | "outputId": "be88b89d-e4fe-4371-fb8b-c9c030169fe9" 110 | }, 111 | "cell_type": "code", 112 | "source": [ 113 | "import os\n", 114 | "from pydrive.auth import GoogleAuth\n", 115 | "from pydrive.drive import GoogleDrive\n", 116 | "from google.colab import auth\n", 117 | "from oauth2client.client import GoogleCredentials\n", 118 | "import nltk\n", 119 | "from nltk import FreqDist\n", 120 | "nltk.download('stopwords')" 121 | ], 122 | "execution_count": 85, 123 | "outputs": [ 124 | { 125 | "output_type": "stream", 126 | "text": [ 127 | "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", 128 | "[nltk_data] Package stopwords is already up-to-date!\n" 129 | ], 130 | "name": "stdout" 131 | }, 132 | { 133 | "output_type": "execute_result", 134 | "data": { 135 | "text/plain": [ 136 | "True" 137 | ] 138 | }, 139 | "metadata": { 140 | "tags": [] 141 | }, 142 | "execution_count": 85 143 | } 144 | ] 145 | }, 146 | { 147 | "metadata": { 148 | "id": "UVOx_UHJtHlf", 149 | "colab_type": "code", 150 | "colab": {} 151 | }, 152 | "cell_type": "code", 153 | "source": [ 154 | "auth.authenticate_user()\n", 155 | "gauth = GoogleAuth()\n", 156 | "gauth.credentials = GoogleCredentials.get_application_default()\n", 157 | "drive = GoogleDrive(gauth)" 158 | ], 159 | "execution_count": 0, 160 | "outputs": [] 161 | }, 162 | { 163 | "metadata": { 164 | "id": "OKXm0rrnt1j4", 165 | "colab_type": "code", 166 | "colab": {} 167 | }, 168 | "cell_type": "code", 169 | "source": [ 170 | "download = drive.CreateFile({'id': '1-a2dbUDDHsmkWOXoDYvgvCa8WBkEMzbr'})" 171 | ], 172 | "execution_count": 0, 173 | "outputs": [] 174 | }, 175 | { 176 | "metadata": { 177 | "id": "TeAxx6Btuf-m", 178 | "colab_type": "code", 179 | "colab": { 180 | "base_uri": "https://localhost:8080/", 181 | "height": 35 182 | }, 183 | "outputId": "dbcf73e3-661b-40fd-a475-d57b4bd36df4" 184 | }, 185 | "cell_type": "code", 186 | "source": [ 187 | "ls" 188 | ], 189 | "execution_count": 88, 190 | "outputs": [ 191 | { 192 | "output_type": "stream", 193 | "text": [ 194 | "adc.json automotive_rev_json.gz \u001b[0m\u001b[01;34msample_data\u001b[0m/\n" 195 | ], 196 | "name": "stdout" 197 | } 198 | ] 199 | }, 200 | { 201 | "metadata": { 202 | "id": "1mR48mvAuYqP", 203 | "colab_type": "code", 204 | "colab": {} 205 | }, 206 | "cell_type": "code", 207 | "source": [ 208 | "download.GetContentFile('automotive_rev_json.gz')" 209 | ], 210 | "execution_count": 0, 211 | "outputs": [] 212 | }, 213 | { 214 | "metadata": { 215 | "id": "v7ftghxl_SzE", 216 | "colab_type": "code", 217 | "colab": {} 218 | }, 219 | "cell_type": "code", 220 | "source": [ 221 | "import pandas as pd\n", 222 | "pd.set_option(\"display.max_colwidth\", 200)\n", 223 | "import numpy as np\n", 224 | "import json\n", 225 | "import re\n", 226 | "import gzip\n", 227 | "import spacy\n", 228 | "\n", 229 | "import gensim\n", 230 | "from gensim import corpora\n", 231 | "\n", 232 | "import pyLDAvis\n", 233 | "import pyLDAvis.gensim\n", 234 | "import matplotlib.pyplot as plt\n", 235 | "import seaborn as sns\n", 236 | "%matplotlib inline" 237 | ], 238 | "execution_count": 0, 239 | "outputs": [] 240 | }, 241 | { 242 | "metadata": { 243 | "id": "k-_gLizownvx", 244 | "colab_type": "code", 245 | "colab": {} 246 | }, 247 | "cell_type": "code", 248 | "source": [ 249 | "def parse(path):\n", 250 | " g = gzip.open(path, 'rb')\n", 251 | " for l in g:\n", 252 | " yield eval(l)\n", 253 | " \n", 254 | "def getDF(path):\n", 255 | " i = 0\n", 256 | " df = {}\n", 257 | " for d in parse(path):\n", 258 | " df[i] = d\n", 259 | " i += 1\n", 260 | " return pd.DataFrame.from_dict(df, orient='index')" 261 | ], 262 | "execution_count": 0, 263 | "outputs": [] 264 | }, 265 | { 266 | "metadata": { 267 | "id": "FPle7KMpCVuc", 268 | "colab_type": "code", 269 | "colab": { 270 | "base_uri": "https://localhost:8080/", 271 | "height": 35 272 | }, 273 | "outputId": "0e836477-df77-4f96-8df5-3319afddfb50" 274 | }, 275 | "cell_type": "code", 276 | "source": [ 277 | "df = getDF('automotive_rev_json.gz')\n", 278 | "df.shape" 279 | ], 280 | "execution_count": 92, 281 | "outputs": [ 282 | { 283 | "output_type": "execute_result", 284 | "data": { 285 | "text/plain": [ 286 | "(20473, 9)" 287 | ] 288 | }, 289 | "metadata": { 290 | "tags": [] 291 | }, 292 | "execution_count": 92 293 | } 294 | ] 295 | }, 296 | { 297 | "metadata": { 298 | "id": "17zMP_rlEnCo", 299 | "colab_type": "code", 300 | "colab": { 301 | "base_uri": "https://localhost:8080/", 302 | "height": 459 303 | }, 304 | "outputId": "7f4dcd45-133b-49a5-b5fe-7f85e8d741b6" 305 | }, 306 | "cell_type": "code", 307 | "source": [ 308 | "df.head()" 309 | ], 310 | "execution_count": 93, 311 | "outputs": [ 312 | { 313 | "output_type": "execute_result", 314 | "data": { 315 | "text/html": [ 316 | "
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reviewerIDasinreviewerNamehelpfulreviewTextoverallsummaryunixReviewTimereviewTime
0A3F73SC1LY51OOB00002243XAlan Montgomery[4, 4]I needed a set of jumper cables for my new car and these had good reviews and were at a good price. They have been used a few times already and do what they are supposed to - no complaints there....5.0Work Well - Should Have Bought Longer Ones131353920008 17, 2011
1A20S66SKYXULG2B00002243Xalphonse[1, 1]These long cables work fine for my truck, but the quality seems a little on the shabby side. For the money I was not expecting 200 dollar snap-on jumper cables but these seem more like what you wo...4.0Okay long cables131509440009 4, 2011
2A2I8LFSN2IS5EOB00002243XChris[0, 0]Can't comment much on these since they have not yet been used (I will come back and update my review is I find any issues after first use) ... but they are built solid, nice tough big hard clamps ...5.0Looks and feels heavy Duty137471040007 25, 2013
3A3GT2EWQSO45ZGB00002243XDeusEx[19, 19]I absolutley love Amazon!!! For the price of a set of cheap Booster/Jumper Cables in a brick and morter store, you can buy extra long and heavy duty jumpers! First off, don't be the person that ...5.0Excellent choice for Jumper Cables!!!129288960012 21, 2010
4A3ESWJPAVRPWB4B00002243XE. Hernandez[0, 0]I purchased the 12' feet long cable set and they arrived in a retail cardboard box with handle, which could be used as a storage and carrying case for these cables, although I will not need to sto...5.0Excellent, High Quality Starter Cables134136000007 4, 2012
\n", 408 | "
" 409 | ], 410 | "text/plain": [ 411 | " reviewerID asin reviewerName helpful \\\n", 412 | "0 A3F73SC1LY51OO B00002243X Alan Montgomery [4, 4] \n", 413 | "1 A20S66SKYXULG2 B00002243X alphonse [1, 1] \n", 414 | "2 A2I8LFSN2IS5EO B00002243X Chris [0, 0] \n", 415 | "3 A3GT2EWQSO45ZG B00002243X DeusEx [19, 19] \n", 416 | "4 A3ESWJPAVRPWB4 B00002243X E. Hernandez [0, 0] \n", 417 | "\n", 418 | " reviewText \\\n", 419 | "0 I needed a set of jumper cables for my new car and these had good reviews and were at a good price. They have been used a few times already and do what they are supposed to - no complaints there.... \n", 420 | "1 These long cables work fine for my truck, but the quality seems a little on the shabby side. For the money I was not expecting 200 dollar snap-on jumper cables but these seem more like what you wo... \n", 421 | "2 Can't comment much on these since they have not yet been used (I will come back and update my review is I find any issues after first use) ... but they are built solid, nice tough big hard clamps ... \n", 422 | "3 I absolutley love Amazon!!! For the price of a set of cheap Booster/Jumper Cables in a brick and morter store, you can buy extra long and heavy duty jumpers! First off, don't be the person that ... \n", 423 | "4 I purchased the 12' feet long cable set and they arrived in a retail cardboard box with handle, which could be used as a storage and carrying case for these cables, although I will not need to sto... \n", 424 | "\n", 425 | " overall summary unixReviewTime \\\n", 426 | "0 5.0 Work Well - Should Have Bought Longer Ones 1313539200 \n", 427 | "1 4.0 Okay long cables 1315094400 \n", 428 | "2 5.0 Looks and feels heavy Duty 1374710400 \n", 429 | "3 5.0 Excellent choice for Jumper Cables!!! 1292889600 \n", 430 | "4 5.0 Excellent, High Quality Starter Cables 1341360000 \n", 431 | "\n", 432 | " reviewTime \n", 433 | "0 08 17, 2011 \n", 434 | "1 09 4, 2011 \n", 435 | "2 07 25, 2013 \n", 436 | "3 12 21, 2010 \n", 437 | "4 07 4, 2012 " 438 | ] 439 | }, 440 | "metadata": { 441 | "tags": [] 442 | }, 443 | "execution_count": 93 444 | } 445 | ] 446 | }, 447 | { 448 | "metadata": { 449 | "id": "jjLyeSxg41OQ", 450 | "colab_type": "code", 451 | "colab": {} 452 | }, 453 | "cell_type": "code", 454 | "source": [ 455 | "def freq_words(x, terms = 30):\n", 456 | " all_words = ' '.join([text for text in x])\n", 457 | " all_words = all_words.split()\n", 458 | " \n", 459 | " fdist = FreqDist(all_words)\n", 460 | " words_df = pd.DataFrame({'word':list(fdist.keys()), 'count':list(fdist.values())})\n", 461 | " \n", 462 | " # selecting top 20 most frequent words\n", 463 | " d = words_df.nlargest(columns=\"count\", n = terms) \n", 464 | " plt.figure(figsize=(20,5))\n", 465 | " ax = sns.barplot(data=d, x= \"word\", y = \"count\")\n", 466 | " ax.set(ylabel = 'Count')\n", 467 | " plt.show()" 468 | ], 469 | "execution_count": 0, 470 | "outputs": [] 471 | }, 472 | { 473 | "metadata": { 474 | "id": "pennLlyl5Whp", 475 | "colab_type": "code", 476 | "colab": { 477 | "base_uri": "https://localhost:8080/", 478 | "height": 426 479 | }, 480 | "outputId": "057ab4de-0c87-4d9a-edf8-7a4bbc7e8949" 481 | }, 482 | "cell_type": "code", 483 | "source": [ 484 | "freq_words(df['reviewText'])" 485 | ], 486 | "execution_count": 95, 487 | "outputs": [ 488 | { 489 | "output_type": "stream", 490 | "text": [ 491 | "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:342: DeprecationWarning: pandas.core.common.is_categorical_dtype is deprecated. import from the public API: pandas.api.types.is_categorical_dtype instead\n", 492 | " elif is_categorical(y):\n", 493 | "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n", 494 | " stat_data = remove_na(group_data)\n" 495 | ], 496 | "name": "stderr" 497 | }, 498 | { 499 | "output_type": "display_data", 500 | "data": { 501 | "image/png": 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CZ0XEFsDyzFxS12uQJEmSJEnS2NQ5QPh3KX59rt1+g7RdBCxqm7YOmD9I27uB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\ntZWd6/X093cyk5AkSeNNRDy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502 | "text/plain": [ 503 | "" 504 | ] 505 | }, 506 | "metadata": { 507 | "tags": [] 508 | } 509 | } 510 | ] 511 | }, 512 | { 513 | "metadata": { 514 | "id": "jUc1_gTcLfYt", 515 | "colab_type": "code", 516 | "colab": {} 517 | }, 518 | "cell_type": "code", 519 | "source": [ 520 | "# replace \"n't\" with \" not\"\n", 521 | "df['reviewText'] = df['reviewText'].str.replace(\"n\\'t\", \" not\")\n", 522 | "\n", 523 | "# remove unwanted characters, numbers and symbols\n", 524 | "df['reviewText'] = df['reviewText'].str.replace(\"[^a-zA-Z#]\", \" \")" 525 | ], 526 | "execution_count": 0, 527 | "outputs": [] 528 | }, 529 | { 530 | "metadata": { 531 | "id": "wUUY2XIj6dF_", 532 | "colab_type": "code", 533 | "colab": { 534 | "base_uri": "https://localhost:8080/", 535 | "height": 426 536 | }, 537 | "outputId": "1af3a5d2-8890-4de1-fc54-9a67f5581a40" 538 | }, 539 | "cell_type": "code", 540 | "source": [ 541 | "freq_words(df['reviewText'])" 542 | ], 543 | "execution_count": 97, 544 | "outputs": [ 545 | { 546 | "output_type": "stream", 547 | "text": [ 548 | "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:342: DeprecationWarning: pandas.core.common.is_categorical_dtype is deprecated. import from the public API: pandas.api.types.is_categorical_dtype instead\n", 549 | " elif is_categorical(y):\n", 550 | "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n", 551 | " stat_data = remove_na(group_data)\n" 552 | ], 553 | "name": "stderr" 554 | }, 555 | { 556 | "output_type": "display_data", 557 | "data": { 558 | "image/png": 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MzD+trTJJkiRJkiSNO2MNm95QaxWSJEmSJEl6Shhr2LT7MNO/WlUhkiRJkiRJGv/GGjbN\n6Li9EfBK4CYMmyRJkiRJktRhTGFTZs7uvB8RmwBn1FKRJEmSJEmSxq0NnsiDMvNR4M8rrkWSJEmS\nJEnj3Jh6NkXEjcBgx6RtgNtrqUiSJEmSJEnj1ljHbPpEx+1B4HfAbdWXI0mSJEmSpPFsTJfRZeb1\nwErg5eW/jTNzcORHSZIkSZIk6elmTGFTRBwHfA7YmuISuhMj4uN1FiZJkiRJkqTxZ6yX0e0GvCoz\nVwJERD9wA3B8XYVJkiRJkiRp/Bnrr9FtsCpoAsjMxykuq5MkSZIkSZJWG2vPph9ExKXAvPL+nsCt\noz0oIjYG7gA+BVwDnANMAO4H3paZyyLiUGAORXh1amaeHhEbAmcC2wIrgNmZeU9E7AicTDFI+e2Z\neeQY65ckSZIkSVIDRu3ZFBHbU4RB5wHbA9sBN2TmnDE8/yeAB8vbxwEnZeYM4G7giIiYCBwN7AHM\nBN4fEVsChwAPZeZ04NOsuVzvBOCozJwGTIqIvcfyIiVJkiRJktSMEcOmiNgduAnYLDMvyMz3A2cA\nR0bEy0d57IuAHYBvl5NmApeWty+jCJheCSzMzIcz87FyWdOA3YGLy7bzgGkRsRGwfWYu7HoOSZIk\nSZIk9YjRejYdA+yVmQ+vmpCZ/wvsC/zLKI/9PPCBjvsTM3NZeXsxxS/bTQUGOtqsM70cK2qwnLZk\niLaSJEmSJEnqEaON2dSXmXd0T8zMH0XEM4d7UES8HfheZv48IoZ83uGWtx7Th2u7lsmTN6G/f8Lq\n+wMjtK3TlCmbDTtvcYN1dBqppkUN1tFppJoAfttQHZ1Gq+nuhuroNlpdkiRJkiS1YbSwadMR5m01\nwry/BZ4fEfsAzwWWAY9ExMbl5XLbUOQZiyh6LK2yDXBLx/TbysHC+ygGFd+qq+2omciSJY+O1qQR\nAwNL2y5hHdY0Nr1YE/RuXZIkSZKkp76ROkCMdhndHRHxnu6JEfERYMFwD8rMgzNzp8zcGTiN4tfo\n5gGzyiazgCvL59gpIraIiE0pxmu6EbgaOLBsuy9wbWYuB+6KiOnl9P3L55AkSZIkSVKPGK1n04eB\nb5WXxS0EJlAEQr+j6L20Po4Bzo6IdwP3Amdl5vKI+BhwFcW4TMdm5sMRcSGwZ0TMp+gVdXj5HHOA\nUyJiA2BBZs5bzxokSZIkSZJUoxHDpsx8ANi5/FW6FwMrgIsy84axLiAzP9lxd88h5s8F5nZNWwHM\nHqLtncCMsS5bkiRJkiRJzRqtZxMAmXkNcE3NtUiSJEmSJGmcG23MJkmSJEmSJGnMDJskSZIkSZJU\nGcMmSZIkSZIkVcawSZIkSZIkSZUxbJIkSZIkSVJlxvRrdJLGlx9eenAry33ZGy5sZbmSJEmSpN5h\nzyZJkiRJkiRVxrBJkiRJkiRJlTFskiRJkiRJUmUMmyRJkiRJklQZwyZJkiRJkiRVxrBJkiRJkiRJ\nlTFskiRJkiRJUmUMmyRJkiRJklQZwyZJkiRJkiRVxrBJkiRJkiRJlTFskiRJkiRJUmUMmyRJkiRJ\nklQZwyZJkiRJkiRVxrBJkiRJkiRJlTFskiRJkiRJUmX663riiNgEOBN4NvBM4FPAbcA5wATgfuBt\nmbksIg4F5gArgVMz8/SI2LB8/LbACmB2Zt4TETsCJwODwO2ZeWRdr0GSJEmSJEnrp86eTfsCt2bm\nrsBBwH8AxwEnZeYM4G7giIiYCBwN7AHMBN4fEVsChwAPZeZ04NPA8eXzngAclZnTgEkRsXeNr0GS\nJEmSJEnrobaeTZl5Ycfd5wG/ogiT3lNOuwz4EJDAwsx8GCAibgKmAbsDZ5dt5wFfjYiNgO0zc2HH\nc+wBXFHX65AkSZIkSdLY1T5mU0TcDJxHcZncxMxcVs5aDGwNTAUGOh6yzvTMXElx2dxUYMkQbSVJ\nkiRJktQDauvZtEpmvioi/go4F+jrmNU3zEPWZ/pwbVebPHkT+vsnrL4/MELbOk2Zstmw8xY3WEen\nkWpa1GAdnUaqCeC3DdXRabSa7m6ojm6j1dWGXqxJkiRJktSsOgcIfzmwODPvy8z/iYh+YGlEbJyZ\njwHbUGQaiyh6LK2yDXBLx/TbysHC+ygGFd+qq+2IuciSJY9W9ZKelIGBpW2XsA5rGpterAl6s65e\nrEmSJEmSVL2ROhvUeRndq4EPAkTEs4FNKcZemlXOnwVcCSwAdoqILSJiU4rxmm4ErgYOLNvuC1yb\nmcuBuyJiejl9//I5JEmSJEmS1APqDJu+AvxJRNwIfBv4e+AY4LBy2pbAWWUvp48BV1GEUceWg4Vf\nCEyIiPnlYz9ePu8c4PhyIPGfZea8Gl+DJEmSJEmS1kOdv0b3GHDIELP2HKLtXGBu17QVwOwh2t4J\nzKioTEmSJEmSJFWo9l+jkyRJkiRJ0tOHYZMkSZIkSZIqY9gkSZIkSZKkyhg2SZIkSZIkqTKGTZIk\nSZIkSaqMYZMkSZIkSZIqY9gkSZIkSZKkyhg2SZIkSZIkqTKGTZIkSZIkSaqMYZMkSZIkSZIq0992\nAZKeHq7/rwNbWe6u+3y9leVKkiRJ0tOVPZskSZIkSZJUGXs2SXra+q/LZ7Wy3H1e/41WlitJkiRJ\nTbBnkyRJkiRJkipj2CRJkiRJkqTKGDZJkiRJkiSpMoZNkiRJkiRJqoxhkyRJkiRJkipj2CRJkiRJ\nkqTKGDZJkiRJkiSpMoZNkiRJkiRJqoxhkyRJkiRJkipj2CRJkiRJkqTK9Nf55BHxWWBGuZzjgYXA\nOcAE4H7gbZm5LCIOBeYAK4FTM/P0iNgQOBPYFlgBzM7MeyJiR+BkYBC4PTOPrPM1SJIkSZIkaexq\n69kUEbsBL8nMXYDXAScAxwEnZeYM4G7giIiYCBwN7AHMBN4fEVsChwAPZeZ04NMUYRXl8xyVmdOA\nSRGxd12vQZIkSZIkSeunzp5NNwDfL28/BEykCJPeU067DPgQkMDCzHwYICJuAqYBuwNnl23nAV+N\niI2A7TNzYcdz7AFcUePrkKRGnX/1AY0v8y17zW18mZIkSZKemmoLmzJzBfD78u47gMuB12bmsnLa\nYmBrYCow0PHQdaZn5sqIGCynLRmi7bAmT96E/v4Jq+8PjNC2TlOmbDbsvMUN1tFppJoWNVhHp5Fq\nAvhtQ3V0Gq2muxuqo9todbXBmsZmPNb0yYte21AlXcs96KpWlitJkiTpiat1zCaAiNiPImzaC/hp\nx6y+YR6yPtOHa7vakiWPjtakEQMDS9suYR3WNDa9WBP0Zl3WNDbWNHa9WpckSZL0dDfSH6xr/TW6\niHgt8E/A3uVlco9ExMbl7G0oOtAsouixxHDTy8HC+ygGFd9qiLaSJEmSJEnqAbX1bIqIScDngD0y\n88Fy8jxgFnBu+f+VwALgtIjYAnicYrymOcDmwIHAVcC+wLWZuTwi7oqI6Zk5H9gf+FJdr0GS1Ls+\nMv/AVpb72elfH3be22/+WIOVrHH2q/61leVKkiRJQ6nzMrqDgWcBF0XEqmmHUQRL7wbuBc4qA6SP\nUYRKg8CxmflwRFwI7BkR84FlwOHlc8wBTomIDYAFmTmvxtcgSZIkSZKk9VDnAOGnAqcOMWvPIdrO\nBeZ2TVsBzB6i7Z3AjIrKlCRJkiRJUoVqHyBckiS157D5X2xluWdNP6qV5UqSJKl9tQ4QLkmSJEmS\npKcXwyZJkiRJkiRVxsvoJElS4w6/8YzGl3nmjHWGgpQkSVINDJskSZKAw2+4sJXlnvnqg1tZriRJ\nUl28jE6SJEmSJEmVMWySJEmSJElSZbyMTpIkqUfNvv7SVpZ7xq5vaGW5kiTpqcGeTZIkSZIkSaqM\nPZskSZI0Zkdc/51WlvvVXfdsZbmSJGn92bNJkiRJkiRJlTFskiRJkiRJUmUMmyRJkiRJklQZwyZJ\nkiRJkiRVxrBJkiRJkiRJlfHX6CRJkjSuvfP6m1pZ7mm7TmtluZIk9Tp7NkmSJEmSJKkyhk2SJEmS\nJEmqjGGTJEmSJEmSKmPYJEmSJEmSpMoYNkmSJEmSJKkyhk2SJEmSJEmqTH+dTx4RLwEuAb6QmV+O\niOcB5wATgPuBt2Xmsog4FJgDrAROzczTI2JD4ExgW2AFMDsz74mIHYGTgUHg9sw8ss7XIEmSJEmS\npLGrLWyKiInAl4BrOiYfB5yUmV+PiM8AR0TE2cDRwCuAPwILI+JiYF/gocw8NCL2Ao4HDgZOAI7K\nzIURcV5E7J2ZV9T1OiRJkqQn4l033Nb4Mk999Y6NL1OSpG51Xka3DHg9sKhj2kzg0vL2ZcAewCuB\nhZn5cGY+BtwETAN2By4u284DpkXERsD2mbmw6zkkSZIkSZLUA2oLmzLz8TI86jQxM5eVtxcDWwNT\ngYGONutMz8yVFJfNTQWWDNFWkiRJkiRJPaDWMZtG0VfB9OHarjZ58ib0909YfX9ghLZ1mjJls2Hn\nLW6wjk4j1bRo2Dn1GqkmgN82VEen0Wq6u6E6uo1WVxusaWysaex6sS5rGhtrGrterMuaxmY81nTg\nN25vqJK1fX3WXw47730X39dgJWuc+KbntbJcSXo6aDpseiQiNi57PG1DkWksouixtMo2wC0d028r\nBwvvoxhUfKuutiPmIkuWPFpd9U/CwMDStktYhzWNTS/WBL1ZlzWNjTWNXS/WZU1jY01j14t1WdPY\nWNPY9WJdI9V00fx2ziEOmr7JsPNuvH7ZsPPqNGPXZ7SyXEm9b6Q/cDQdNs0DZgHnlv9fCSwATouI\nLYDHKcZrmgNsDhwIXEUxWPi1mbk8Iu6KiOmZOR/Yn2IQckmSJElSw+64+g+NL/Mlez2z8WVKWj91\n/hrdy4HPA9sByyPiAOBQ4MyIeDdwL3BWGSB9jCJUGgSOzcyHI+JCYM+ImE8x2Pjh5VPPAU6JiA2A\nBZk5r67XIEmSJEmSpPVTW9iUmT+g+PW5bnsO0XYuMLdr2gpg9hBt7wRmVFOlJEmSJEmSqlTbr9FJ\nkiRJkiTp6afNX6OTJEmSJKlSv7rksVaW+9z9Nh523u+/9vsGK1lj4qETh5234qzfNFjJGhMOe/aw\n81Z+7ScNVrLGBoe+sJXlPpXZs0mSJEmSJEmVsWeTJEmSJEnSMFae/4PGl7nBW14+4vzBi65rppAu\nfQfNHFM7ezZJkiRJkiSpMoZNkiRJkiRJqoxhkyRJkiRJkipj2CRJkiRJkqTKGDZJkiRJkiSpMoZN\nkiRJkiRJqoxhkyRJkiRJkipj2CRJkiRJkqTKGDZJkiRJkiSpMoZNkiRJkiRJqoxhkyRJkiRJkipj\n2CRJkiRJkqTKGDZJkiRJkiSpMoZNkiRJkiRJqoxhkyRJkiRJkipj2CRJkiRJkqTKGDZJkiRJkiSp\nMoZNkiRJkiRJqkx/2wU8ERHxBWBnYBA4KjMXtlySJEmSJEmSGIc9myJiV+AFmbkL8A7gxJZLkiRJ\nkiRJUmnchU3A7sC3ADLzx8DkiNi83ZIkSZIkSZIE4zNsmgoMdNwfKKdJkiRJkiSpZX2Dg4Nt17Be\nIuJU4NuZeUl5fz5wRGb+pN3KJEmSJEmSNB57Ni1i7Z5MzwHub6kWSZIkSZIkdRiPYdPVwAEAEfEy\nYFFmLm23JEmSJEmSJME4vIwOICL+FXg1sBL4+8y8reWSJEmSJEmSxDgNmyRJkiRJktSbxuNldJIk\nSZIkSepRhk2SJEmSJEmqjGHTECJiVkQcHhH/3nYto4mI7SLi1rbrGE1EbBoRv2h4mbOaXN54FxGv\ni4gjI+KAtmuBNfW0XcdoIuKQiMiImNF2LUNpcjuIiA0jYkFEnNXUMse7XlnPR/rei4gLImLjNurq\nJU/k2MDvoTUiYmpEnFLefnVE/El5+xcRsWlNy5wZEXPreO6qrM865frUOyLi8xFxXUTcFRH3lbe/\nOR6OybW28XLO10tWnX9GxJ9GxCvarkfrp8nz8v4mFjKeRMR2wFuA/2q5FD0JHZ/jN1ouZdzIzCsB\nygOl1g/OV9UzDuwBfDQzb2y7kG4tbAdbA8/IzMMaWt641wvr+Wjfe5n55kYL6kFP5NjA76G1ZeYD\nwLvLu0cA/w4sbq+i8cX1qbdk5gehCCqAl2Tmh8rPqPXjJ6lBrwE2Bb7fdiHqTYZN6zoJeAVwO/Cc\niPgGsAPwucz8atl74TPAcuA+4O8y84+tVduCiNgcOA+YCGwCvLe8fwqwL/AMihPwPoqDomcC8xsu\n8yTgFRFxDPBXwBbAhsD7MvPIlfk0AAAMPklEQVSHDdeylqHev8xsfSddHjCdDqyMiG9m5v49UM9L\ngedShhjAMW2enEfEhsCpwPNX1QO8HtgpIpZk5vUN1XE4MB2YAgTwOeBnrNk3/YriZG7VdnB0Zh7X\nQGlfAP4sIs6g2ObW2u4i4qfAD4GrM/P0BuoBhvzcjgb+H137rMxc2lRNHbUdDuxD8Vn+DNgR+O/M\nfGeDZYz2vfcL4CXAq4B/AR4DfgMcmpnL6yysfH92BZ4FvBj4J4oT7h0ovoPPXbUuRcSdwIzM/G0N\npYz2Hh1K8V24AvhRZr6LBra/iFgAHJKZP4uI5wKXlDWuXtcz8+pVn2FmPlL+Bf+OzDyzppruovis\n+oAlwG6ZeWtEXAW8EHgX8EbgxR09df4hIl5PcVz62oq3xU0j4lyKbevrwM3Ap4A/lvUdBFwIfCEz\nbyh78f0Y+DPgOGAGMAH4cmaeX2FdnbaPiMuB51HsR4+m6/MCDqTZ/flwx3u7AftT/CL0ZZn5mRZr\n+RpwOUVoeQbFMcxGFNvhOzPzl03U1mWDiDiZYn/xg8x8V0Q8p4naRtn2FgEvoDgm/0pmnhYRe9Hw\nPr2j1j8FzqV4P/qBt2bmvU0sexjd2+BPafh8ryu43JRiuz+Zru2th85FJwOfBJZHxC8z89ImF14e\n250FbAv8gTXHvWudX5XHnpcDizPz0w3Vts76TfFerfXd3EQtHTVtTtd5eUTMpOvcITOXVblcL6Nb\n1+eA64FfUqwQB1EcFL2vnH8isF9mvoZix3xgG0W2bCpwWmbuBnwc+CjFhnRXZr4a+DmwO8WGdUdm\nzgD+p+EaV32Og8AtZa1zKL5A2jbU+9crvgA83HbQ1OGvgWeV69VrgS1bructwB8yc1eKL/8vAVcC\nH28qaOrw0rKGN1IcdH8FOLisbQlwCOV20NSJCfBBICn2AUNtd88HjmsyaCp1f25fZuh9VpteDvwj\nsBPw+ojYosFlj/a9t8o/AB8s38cLgK0aqu8FwBuA4yn2mW8qb68ADgaIiB2Ae2oKmmD092gi8LrM\nnAa8KCJeSjPb3zmU7wHFe3QJ667rTfsBRTj518CtwC4RsQHwSuC3mfkdimOC2R0n3HeU2+K9VL8t\n7kARcO1Csa+cTBHQ7Qr8juK75ZsUwTPAnsDVFOHqtmVdrwE+UePlpC8E9gNmUgRcfUO0aXp/DkMf\nr3wImEbx/ixpuZYNgSvKE8hPAZ/PzN2BE4B/brC2Ti8EjmXtfXlTtQ237W1L8UeM6RTh6ap1qK19\nOsABwHfKz/Moij8qtql7G+yV872htrdeqW0JcCbwxaaDptJhwAPl9+5/UnwnD3V+1bmfaEr3+n0Y\n7X83D3VePtS5Q6UMm0Z2S2auAH4NTIqIZ1Mc9H4zIq6j+OvONi3W15bfALMiYj7wb6z5clp1GdGv\ngEkUB3g3l9Oua7LADn+zatmZeSvw5y3V0Wm490/r+h9gs4g4h+Jg/4KW6+lcnxYBy2gvAPteuX9a\ntb0NZuZ95bxrKQ422zLcdvf7zPxRy/V0fm7d+6w23Z2ZD2TmSoq/QLdVz1rfe13zvg58JSL+keLE\n5YGGaro1MweB+4Hby/p+Q/EX3S0iYgrFScLXGqpnqPfoQeCSiLge+Aua26+fT3HgCkUPuefRta5H\nRNP7qOuBnSlOkL5EETK9lKJX43BW9X4ear17sn6YmY9m5iMUIc4AcFr5We1G8VldBryubL8fxaVQ\nrwJ2Lo/3rqI4Zq7rhHh+Zi4vw9Lf0TvHBUMdr8wF5gF/R3Pb3HC1wJrLd14FfLL8vD5Oe+9h5778\nAYr1uanahtv2bga2jIibgSsoetJCe/t0KALdt0fE5ykuv7+lwWUPpXMbXErvnO+ttb15LrqWlwE3\nAWTmBRTB13DnV01fQbLW+k3RO/s6aPW7ufu8fEsaOHcwbBrZ4x23+yi6XP86M2eW/3bKzM+2VFub\n5lC8D9OBzoFtu9+vPopun9DeujbI2n8hnNBSHZ2Ge/+0rpUUB06nUFyudlq75ayzPm3EmnW8aZ3b\n25b0Tl0w/HbX1iXHw31u3fusNj3edb+teoZ9TzLzHIoD2/8DLouIF7VQU3d951GELbtT9Oppup6+\niNiIouv+qr8OLmioDsoTo19FxE4U37OPMvS6PtgxbcOay7qOYr+9M/AdipPtaRQHssOpc1vs3ra+\nCvxD+VldApCZDwG/joigCAa+S7G/Or3jmO8vMvOeimtbZbDr/kDH7bo/r5Gsc7ySmUcC76HoaXRd\nRDQ1JMdwx05/7Pj/wPKzmtFiD+2h9uVN1XYdQ29791L8wW7XzJxJ8QeXNvfpZOYdFJe23ggcHxFv\nb2rZw+jeBu9v4Xxvnf109/ZGsT/3XLSwgrXPL0c6v2r0+LN7/aboOdv2cXr3eXkj5zSGTetayTBj\nWWXmEljdXZ+IeG9E/GWDtfWKZ1GMLQLF5QwbDdMuKXoUQPFl1qRVn+PCVcuOiJ0prn9u21jfv7b0\n0n7hZRSXO8yn+OLYoeV6Oten51GsZw+1WlFhCTBYXiMOxRg3tzLC/qxmvbbd9ern1ivGtJ5ExD8D\nyzPzVIpehm1vj1D07JlNcWLwaI3LGek92gx4PDMfKNevv2HNQVsT2985FGHXXIZY18sg5XfA1hEx\ngeJEtDaZ+ROKHlaTshh76QGKyxs6w6a29k1QnID/sry8aTfWfAdfTDEm2Pcy83GK0HDfiNggIp4Z\nEV+qsaZdImJC2UtvIkN/Xm28Z93HK5PKMaPuKi/nexDYvKVauo+dFlCsZ0TEayKi8stBnoRGahth\n2/sFcF9mLo+INwATImKjNvfpEfFmivGJvgV8gjXnC23p3AY3oRi/tOnzvd+xpvfkdIbe3laUNfXK\nuWib+/KFFCEqEbEPxXrUE+dXQ6zfgwz93dyk7vPy4c4dKtVLJ5W94scUJ7jDje3zDuCMiLiRYkeQ\nTRXWQ84GPhARV1N8gU5l6L9Enk3RBf0aikGMu/9qUKdVn+NWwMsj4rvAv1JcN9u2dd6/iJjdck2d\n/jsiWh+wvPRz4K3l9vYdijEr2nQBxUHateXtd4/Svkl/B5xXdqvekKK+HwMvi4imxyr7Ir213fXy\n59YLRvveW+WXwLyImEfxF7vWf0kvM38DPELRw6lOw75HZe+i70TEQoofDfhs2a6p7e8yiktV5zL8\nuv7lst03gSYuZV1M0ZsCiu+57SguV13lemBuRLy4gVq6nURx6cWpFJ/VxyNia+BbwJspf00sM2+m\nCMi+B9xAMR5OXe6iuKTpGorAa6jPq439effxyiTgwxHx/XL/fktmPthSLd3Hnp8E3hgRN1Bsh99r\nqK6x+CTN1TbUtvct4AXlpaN/RvGrmifT7j79J8CXy/XomLKeNnVvg22c710DRHkc9yKKMGDKENtb\nL52Lfg/4SBQ/ktG0C4CJ5Xo9h+LHqXrl/Kp7/d6P9o9DhzovH+rcoVJ9g4NNnv9LkiRVIyKeRXGC\n9IpyjJSnnYjYDTg8Mw9ruxZ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559 | "text/plain": [ 560 | "" 561 | ] 562 | }, 563 | "metadata": { 564 | "tags": [] 565 | } 566 | } 567 | ] 568 | }, 569 | { 570 | "metadata": { 571 | "id": "nshGCS0WGLlQ", 572 | "colab_type": "code", 573 | "colab": {} 574 | }, 575 | "cell_type": "code", 576 | "source": [ 577 | "from nltk.corpus import stopwords\n", 578 | "stop_words = stopwords.words('english')" 579 | ], 580 | "execution_count": 0, 581 | "outputs": [] 582 | }, 583 | { 584 | "metadata": { 585 | "id": "B8MogVp8vC6l", 586 | "colab_type": "code", 587 | "colab": {} 588 | }, 589 | "cell_type": "code", 590 | "source": [ 591 | "# function to remove stopwords\n", 592 | "def remove_stopwords(rev):\n", 593 | " rev_new = \" \".join([i for i in rev if i not in stop_words])\n", 594 | " return rev_new" 595 | ], 596 | "execution_count": 0, 597 | "outputs": [] 598 | }, 599 | { 600 | "metadata": { 601 | "id": "KYxtiTbOwULK", 602 | "colab_type": "code", 603 | "colab": {} 604 | }, 605 | "cell_type": "code", 606 | "source": [ 607 | "# remove short words (length < 3)\n", 608 | "df['reviewText'] = df['reviewText'].apply(lambda x: ' '.join([w for w in x.split() if len(w)>2]))\n", 609 | "\n", 610 | "# remove stopwords from the text\n", 611 | "reviews = [remove_stopwords(r.split()) for r in df['reviewText']]\n", 612 | "\n", 613 | "# make entire text lowercase\n", 614 | "reviews = [r.lower() for r in reviews]" 615 | ], 616 | "execution_count": 0, 617 | "outputs": [] 618 | }, 619 | { 620 | "metadata": { 621 | "id": "9eSP1Ruj9fdT", 622 | "colab_type": "code", 623 | "colab": { 624 | "base_uri": "https://localhost:8080/", 625 | "height": 426 626 | }, 627 | "outputId": "1bfc8f92-c390-4d1c-cfa3-ed6b9982271f" 628 | }, 629 | "cell_type": "code", 630 | "source": [ 631 | "freq_words(reviews, 35)" 632 | ], 633 | "execution_count": 101, 634 | "outputs": [ 635 | { 636 | "output_type": "stream", 637 | "text": [ 638 | "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:342: DeprecationWarning: pandas.core.common.is_categorical_dtype is deprecated. import from the public API: pandas.api.types.is_categorical_dtype instead\n", 639 | " elif is_categorical(y):\n", 640 | "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n", 641 | " stat_data = remove_na(group_data)\n" 642 | ], 643 | "name": "stderr" 644 | }, 645 | { 646 | "output_type": "display_data", 647 | "data": { 648 | "image/png": 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JJgAAAABGI9kEAAAAwGg2rkfQqtoryeeTPC/JR5KckGSPJGcneVRr7aKqekSSpyTZnOS4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\nTwp7QnodeVf6vd9J8m9VtdS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649 | "text/plain": [ 650 | "" 651 | ] 652 | }, 653 | "metadata": { 654 | "tags": [] 655 | } 656 | } 657 | ] 658 | }, 659 | { 660 | "metadata": { 661 | "id": "x3MUTshKDvC4", 662 | "colab_type": "code", 663 | "colab": {} 664 | }, 665 | "cell_type": "code", 666 | "source": [ 667 | "nlp = spacy.load('en', disable=['parser', 'ner'])" 668 | ], 669 | "execution_count": 0, 670 | "outputs": [] 671 | }, 672 | { 673 | "metadata": { 674 | "id": "jZ6TkR4w_kwq", 675 | "colab_type": "code", 676 | "colab": {} 677 | }, 678 | "cell_type": "code", 679 | "source": [ 680 | "def lemmatization(texts, tags=['NOUN', 'ADJ']):\n", 681 | " output = []\n", 682 | " for sent in texts:\n", 683 | " doc = nlp(\" \".join(sent)) \n", 684 | " output.append([token.lemma_ for token in doc if token.pos_ in tags])\n", 685 | " return output" 686 | ], 687 | "execution_count": 0, 688 | "outputs": [] 689 | }, 690 | { 691 | "metadata": { 692 | "id": "A_r_uxa3Icq0", 693 | "colab_type": "code", 694 | "colab": {} 695 | }, 696 | "cell_type": "code", 697 | "source": [ 698 | "tokenized_reviews = pd.Series(reviews).apply(lambda x: x.split())\n", 699 | "print(tokenized_reviews[1])" 700 | ], 701 | "execution_count": 0, 702 | "outputs": [] 703 | }, 704 | { 705 | "metadata": { 706 | "id": "OKGHMX01NlBa", 707 | "colab_type": "code", 708 | "colab": { 709 | "base_uri": "https://localhost:8080/", 710 | "height": 73 711 | }, 712 | "outputId": "74723838-835d-4ecc-dc02-6aa7d3b11811" 713 | }, 714 | "cell_type": "code", 715 | "source": [ 716 | "print(tokenized_reviews[1])\n", 717 | "len(tokenized_reviews[1])" 718 | ], 719 | "execution_count": 105, 720 | "outputs": [ 721 | { 722 | "output_type": "stream", 723 | "text": [ 724 | "['these', 'long', 'cables', 'work', 'fine', 'truck', 'quality', 'seems', 'little', 'shabby', 'side', 'for', 'money', 'expecting', 'dollar', 'snap', 'jumper', 'cables', 'seem', 'like', 'would', 'see', 'chinese', 'knock', 'shop', 'like', 'harbor', 'freight', 'bucks']\n" 725 | ], 726 | "name": "stdout" 727 | }, 728 | { 729 | "output_type": "execute_result", 730 | "data": { 731 | "text/plain": [ 732 | "29" 733 | ] 734 | }, 735 | "metadata": { 736 | "tags": [] 737 | }, 738 | "execution_count": 105 739 | } 740 | ] 741 | }, 742 | { 743 | "metadata": { 744 | "id": "tRUfUgkD_k24", 745 | "colab_type": "code", 746 | "colab": {} 747 | }, 748 | "cell_type": "code", 749 | "source": [ 750 | "reviews_2 = lemmatization(tokenized_reviews)\n", 751 | "print(reviews_2[1])" 752 | ], 753 | "execution_count": 0, 754 | "outputs": [] 755 | }, 756 | { 757 | "metadata": { 758 | "id": "3Iw8zFgu_lC3", 759 | "colab_type": "code", 760 | "colab": { 761 | "base_uri": "https://localhost:8080/", 762 | "height": 73 763 | }, 764 | "outputId": "a04236f4-0f32-4551-9621-9f69fa439b85" 765 | }, 766 | "cell_type": "code", 767 | "source": [ 768 | "print(reviews_2[1])\n", 769 | "len(reviews_2[1])" 770 | ], 771 | "execution_count": 107, 772 | "outputs": [ 773 | { 774 | "output_type": "stream", 775 | "text": [ 776 | "['long', 'cable', 'fine', 'truck', 'quality', 'little', 'shabby', 'side', 'money', 'dollar', 'jumper', 'cable', 'chinese', 'shop', 'harbor', 'freight', 'buck']\n" 777 | ], 778 | "name": "stdout" 779 | }, 780 | { 781 | "output_type": "execute_result", 782 | "data": { 783 | "text/plain": [ 784 | "17" 785 | ] 786 | }, 787 | "metadata": { 788 | "tags": [] 789 | }, 790 | "execution_count": 107 791 | } 792 | ] 793 | }, 794 | { 795 | "metadata": { 796 | "id": "4xggnZ-u_lHd", 797 | "colab_type": "code", 798 | "colab": { 799 | "base_uri": "https://localhost:8080/", 800 | "height": 251 801 | }, 802 | "outputId": "0bd6f14e-acbe-4fe6-969c-4480a90640c6" 803 | }, 804 | "cell_type": "code", 805 | "source": [ 806 | "set(tokenized_reviews[1]) - set(reviews_2[1])" 807 | ], 808 | "execution_count": 108, 809 | "outputs": [ 810 | { 811 | "output_type": "execute_result", 812 | "data": { 813 | "text/plain": [ 814 | "{'bucks',\n", 815 | " 'cables',\n", 816 | " 'expecting',\n", 817 | " 'for',\n", 818 | " 'knock',\n", 819 | " 'like',\n", 820 | " 'see',\n", 821 | " 'seem',\n", 822 | " 'seems',\n", 823 | " 'snap',\n", 824 | " 'these',\n", 825 | " 'work',\n", 826 | " 'would'}" 827 | ] 828 | }, 829 | "metadata": { 830 | "tags": [] 831 | }, 832 | "execution_count": 108 833 | } 834 | ] 835 | }, 836 | { 837 | "metadata": { 838 | "id": "aZ9ZPfO-CEmD", 839 | "colab_type": "code", 840 | "colab": {} 841 | }, 842 | "cell_type": "code", 843 | "source": [ 844 | "reviews_3 = []\n", 845 | "for i in range(len(reviews_2)):\n", 846 | " reviews_3.append(' '.join(reviews_2[i]))\n", 847 | " \n", 848 | "df['reviews'] = reviews_3" 849 | ], 850 | "execution_count": 0, 851 | "outputs": [] 852 | }, 853 | { 854 | "metadata": { 855 | "id": "Zyj9zVQqCY6R", 856 | "colab_type": "code", 857 | "colab": { 858 | "base_uri": "https://localhost:8080/", 859 | "height": 426 860 | }, 861 | "outputId": "a71332fc-e6bf-4581-e3f8-4fb081bbb8f1" 862 | }, 863 | "cell_type": "code", 864 | "source": [ 865 | "freq_words(df['reviews'], 35)" 866 | ], 867 | "execution_count": 111, 868 | "outputs": [ 869 | { 870 | "output_type": "stream", 871 | "text": [ 872 | "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:342: DeprecationWarning: pandas.core.common.is_categorical_dtype is deprecated. import from the public API: pandas.api.types.is_categorical_dtype instead\n", 873 | " elif is_categorical(y):\n", 874 | "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n", 875 | " stat_data = remove_na(group_data)\n" 876 | ], 877 | "name": "stderr" 878 | }, 879 | { 880 | "output_type": "display_data", 881 | "data": { 882 | "image/png": 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883 | "text/plain": [ 884 | "" 885 | ] 886 | }, 887 | "metadata": { 888 | "tags": [] 889 | } 890 | } 891 | ] 892 | }, 893 | { 894 | "metadata": { 895 | "id": "FBtKwstViu_h", 896 | "colab_type": "code", 897 | "colab": {} 898 | }, 899 | "cell_type": "code", 900 | "source": [ 901 | "# Create the term dictionary of our corpus, where every unique term is assigned an index\n", 902 | "dictionary = corpora.Dictionary(reviews_2)" 903 | ], 904 | "execution_count": 0, 905 | "outputs": [] 906 | }, 907 | { 908 | "metadata": { 909 | "id": "rDoMrZfkiu-c", 910 | "colab_type": "code", 911 | "colab": {} 912 | }, 913 | "cell_type": "code", 914 | "source": [ 915 | "# Convert list of reviews (reviews_2) into a Document Term Matrix using the dictionary prepared above.\n", 916 | "doc_term_matrix = [dictionary.doc2bow(rev) for rev in reviews_2]" 917 | ], 918 | "execution_count": 0, 919 | "outputs": [] 920 | }, 921 | { 922 | "metadata": { 923 | "id": "X6JSF612qkm1", 924 | "colab_type": "code", 925 | "colab": {} 926 | }, 927 | "cell_type": "code", 928 | "source": [ 929 | "# Creating the object for LDA model using gensim library\n", 930 | "LDA = gensim.models.ldamodel.LdaModel" 931 | ], 932 | "execution_count": 0, 933 | "outputs": [] 934 | }, 935 | { 936 | "metadata": { 937 | "id": "U8MLxPhRiuh6", 938 | "colab_type": "code", 939 | "colab": {} 940 | }, 941 | "cell_type": "code", 942 | "source": [ 943 | "# Build LDA model\n", 944 | "lda_model = LDA(corpus=doc_term_matrix,\n", 945 | " id2word=dictionary,\n", 946 | " num_topics=7, \n", 947 | " random_state=100,\n", 948 | " chunksize=1000,\n", 949 | " passes=50)" 950 | ], 951 | "execution_count": 0, 952 | "outputs": [] 953 | }, 954 | { 955 | "metadata": { 956 | "id": "TB5lGOR3iucK", 957 | "colab_type": "code", 958 | "colab": { 959 | "base_uri": "https://localhost:8080/", 960 | "height": 289 961 | }, 962 | "outputId": "a6c66aa2-34c9-4b80-bc56-601eed395193" 963 | }, 964 | "cell_type": "code", 965 | "source": [ 966 | "lda_model.print_topics()" 967 | ], 968 | "execution_count": 119, 969 | "outputs": [ 970 | { 971 | "output_type": "execute_result", 972 | "data": { 973 | "text/plain": [ 974 | "[(0,\n", 975 | " '0.030*\"car\" + 0.026*\"oil\" + 0.020*\"filter\" + 0.018*\"engine\" + 0.016*\"device\" + 0.013*\"code\" + 0.012*\"vehicle\" + 0.011*\"app\" + 0.011*\"change\" + 0.008*\"bosch\"'),\n", 976 | " (1,\n", 977 | " '0.017*\"easy\" + 0.014*\"install\" + 0.014*\"door\" + 0.013*\"tape\" + 0.013*\"jeep\" + 0.011*\"front\" + 0.011*\"mat\" + 0.010*\"side\" + 0.010*\"headlight\" + 0.008*\"fit\"'),\n", 978 | " (2,\n", 979 | " '0.054*\"blade\" + 0.045*\"wiper\" + 0.019*\"windshield\" + 0.014*\"rain\" + 0.012*\"snow\" + 0.012*\"good\" + 0.011*\"year\" + 0.011*\"old\" + 0.011*\"car\" + 0.009*\"time\"'),\n", 980 | " (3,\n", 981 | " '0.044*\"car\" + 0.024*\"towel\" + 0.020*\"product\" + 0.018*\"clean\" + 0.017*\"good\" + 0.016*\"wax\" + 0.014*\"water\" + 0.013*\"use\" + 0.011*\"time\" + 0.011*\"wash\"'),\n", 982 | " (4,\n", 983 | " '0.051*\"light\" + 0.039*\"battery\" + 0.021*\"bulb\" + 0.019*\"power\" + 0.018*\"car\" + 0.014*\"bright\" + 0.013*\"unit\" + 0.011*\"charger\" + 0.010*\"phone\" + 0.010*\"charge\"'),\n", 984 | " (5,\n", 985 | " '0.022*\"tire\" + 0.015*\"hose\" + 0.013*\"use\" + 0.012*\"good\" + 0.010*\"easy\" + 0.010*\"pressure\" + 0.009*\"small\" + 0.009*\"trailer\" + 0.008*\"nice\" + 0.008*\"water\"'),\n", 986 | " (6,\n", 987 | " '0.048*\"product\" + 0.038*\"good\" + 0.027*\"price\" + 0.020*\"great\" + 0.020*\"leather\" + 0.019*\"quality\" + 0.010*\"work\" + 0.010*\"review\" + 0.009*\"amazon\" + 0.009*\"worth\"')]" 988 | ] 989 | }, 990 | "metadata": { 991 | "tags": [] 992 | }, 993 | "execution_count": 119 994 | } 995 | ] 996 | }, 997 | { 998 | "metadata": { 999 | "id": "4nRZyKPDCauL", 1000 | "colab_type": "code", 1001 | "colab": { 1002 | "base_uri": "https://localhost:8080/", 1003 | "height": 860 1004 | }, 1005 | "outputId": "b0ef59ff-5204-4ea1-80c9-f100cc9bf1cb" 1006 | }, 1007 | "cell_type": "code", 1008 | "source": [ 1009 | "# Visualize the topics\n", 1010 | "pyLDAvis.enable_notebook()\n", 1011 | "vis = pyLDAvis.gensim.prepare(lda_model, doc_term_matrix, dictionary)\n", 1012 | "vis" 1013 | ], 1014 | "execution_count": 120, 1015 | "outputs": [ 1016 | { 1017 | "output_type": "execute_result", 1018 | "data": { 1019 | "text/html": [ 1020 | "\n", 1021 | "\n", 1022 | "\n", 1023 | "\n", 1024 | "
\n", 1025 | "" 1061 | ], 1062 | "text/plain": [ 1063 | "PreparedData(topic_coordinates= Freq cluster topics x y\n", 1064 | "topic \n", 1065 | "3 21.143177 1 1 -0.023091 0.141023\n", 1066 | "5 18.369776 1 2 0.096649 0.048498\n", 1067 | "6 15.248682 1 3 -0.107528 0.202428\n", 1068 | "4 14.674014 1 4 -0.112982 -0.163065\n", 1069 | "0 11.917589 1 5 -0.154058 -0.131336\n", 1070 | "1 9.814881 1 6 0.285191 -0.062285\n", 1071 | "2 8.831884 1 7 0.015820 -0.035264, topic_info= Category Freq Term Total loglift logprob\n", 1072 | "term \n", 1073 | "319 Default 6340.000000 product 6340.000000 30.0000 30.0000\n", 1074 | "147 Default 4672.000000 light 4672.000000 29.0000 29.0000\n", 1075 | "952 Default 2615.000000 blade 2615.000000 28.0000 28.0000\n", 1076 | "39 Default 3131.000000 battery 3131.000000 27.0000 27.0000\n", 1077 | "928 Default 2179.000000 wiper 2179.000000 26.0000 26.0000\n", 1078 | "2 Default 9494.000000 car 9494.000000 25.0000 25.0000\n", 1079 | "529 Default 2742.000000 towel 2742.000000 24.0000 24.0000\n", 1080 | "17 Default 2242.000000 price 2242.000000 23.0000 23.0000\n", 1081 | "336 Default 2251.000000 tire 2251.000000 22.0000 22.0000\n", 1082 | "168 Default 1721.000000 oil 1721.000000 21.0000 21.0000\n", 1083 | "2584 Default 1670.000000 bulb 1670.000000 20.0000 20.0000\n", 1084 | "1316 Default 1703.000000 leather 1703.000000 19.0000 19.0000\n", 1085 | "525 Default 1810.000000 wax 1810.000000 18.0000 18.0000\n", 1086 | "32 Default 1980.000000 quality 1980.000000 17.0000 17.0000\n", 1087 | "62 Default 1610.000000 power 1610.000000 16.0000 16.0000\n", 1088 | "963 Default 1270.000000 filter 1270.000000 15.0000 15.0000\n", 1089 | "95 Default 2326.000000 clean 2326.000000 14.0000 14.0000\n", 1090 | "117 Default 1147.000000 engine 1147.000000 13.0000 13.0000\n", 1091 | "725 Default 1511.000000 hose 1511.000000 12.0000 12.0000\n", 1092 | "927 Default 938.000000 windshield 938.000000 11.0000 11.0000\n", 1093 | "1272 Default 1133.000000 bright 1133.000000 10.0000 10.0000\n", 1094 | "524 Default 2705.000000 water 2705.000000 9.0000 9.0000\n", 1095 | "757 Default 1380.000000 device 1380.000000 8.0000 8.0000\n", 1096 | "550 Default 1238.000000 wash 1238.000000 7.0000 7.0000\n", 1097 | "730 Default 1228.000000 unit 1228.000000 6.0000 6.0000\n", 1098 | "643 Default 1153.000000 paint 1153.000000 5.0000 5.0000\n", 1099 | "1187 Default 907.000000 charger 907.000000 4.0000 4.0000\n", 1100 | "723 Default 814.000000 code 814.000000 3.0000 3.0000\n", 1101 | "475 Default 955.000000 install 955.000000 2.0000 2.0000\n", 1102 | "962 Default 730.000000 door 730.000000 1.0000 1.0000\n", 1103 | "... ... ... ... ... ... ...\n", 1104 | "1185 Topic7 424.670807 winter 505.461731 2.2526 -4.7314\n", 1105 | "1012 Topic7 127.780693 vision 150.216125 2.2650 -5.9324\n", 1106 | "936 Topic7 259.161133 weather 392.846741 2.0108 -5.2253\n", 1107 | "534 Topic7 260.354858 original 446.545654 1.8873 -5.2207\n", 1108 | "340 Topic7 222.457581 cold 362.730499 1.9379 -5.3780\n", 1109 | "624 Topic7 152.985733 summer 225.406784 2.0392 -5.7524\n", 1110 | "820 Topic7 340.229797 window 780.110474 1.5970 -4.9531\n", 1111 | "261 Topic7 533.777405 old 1689.711670 1.2745 -4.5028\n", 1112 | "924 Topic7 311.730774 glass 722.981567 1.5856 -5.0406\n", 1113 | "1235 Topic7 239.638931 replacement 477.028687 1.7384 -5.3036\n", 1114 | "382 Topic7 328.168640 month 885.292664 1.4344 -4.9892\n", 1115 | "35 Topic7 438.155579 side 1477.705811 1.2111 -4.7002\n", 1116 | "538 Topic7 338.859558 clear 967.822327 1.3773 -4.9571\n", 1117 | "274 Topic7 545.789856 year 2317.310059 0.9809 -4.4805\n", 1118 | "141 Topic7 380.857422 last 1383.743652 1.1367 -4.8403\n", 1119 | "15 Topic7 435.130188 new 2132.450439 0.8374 -4.7071\n", 1120 | "262 Topic7 310.272888 one 1103.637939 1.1579 -5.0453\n", 1121 | "1025 Topic7 169.302109 style 302.441956 1.8466 -5.6510\n", 1122 | "622 Topic7 168.188354 streak 300.242065 1.8473 -5.6576\n", 1123 | "7 Topic7 557.692505 good 8126.021484 -0.2522 -4.4589\n", 1124 | "21 Topic7 441.052765 time 4586.766602 0.0850 -4.6936\n", 1125 | "495 Topic7 200.997726 design 500.037354 1.5154 -5.4794\n", 1126 | "2 Topic7 520.210632 car 9494.460938 -0.4774 -4.5285\n", 1127 | "290 Topic7 207.020477 driver 627.395142 1.3180 -5.4499\n", 1128 | "111 Topic7 319.139709 easy 3532.182373 0.0228 -5.0171\n", 1129 | "223 Topic7 287.590637 work 3309.058838 -0.0161 -5.1212\n", 1130 | "169 Topic7 234.091522 part 1395.900269 0.6412 -5.3270\n", 1131 | "475 Topic7 220.578003 install 955.325806 0.9610 -5.3865\n", 1132 | "47 Topic7 230.257477 day 1421.293457 0.6067 -5.3435\n", 1133 | "13 Topic7 233.346039 long 1771.797119 0.3996 -5.3302\n", 1134 | "\n", 1135 | "[437 rows x 6 columns], token_table= Topic Freq Term\n", 1136 | "term \n", 1137 | "8001 7 0.997643 acdelco\n", 1138 | "2796 4 0.684772 adapter\n", 1139 | "2796 7 0.313786 adapter\n", 1140 | "2088 6 0.998052 adhesive\n", 1141 | "509 2 0.662911 air\n", 1142 | "509 5 0.336860 air\n", 1143 | "1686 3 0.988734 alot\n", 1144 | "84 3 0.837086 amazon\n", 1145 | "84 4 0.057651 amazon\n", 1146 | "84 5 0.071487 amazon\n", 1147 | "84 7 0.032284 amazon\n", 1148 | "85 4 0.997211 amp\n", 1149 | "6281 7 0.997189 anco\n", 1150 | "6891 5 0.998472 app\n", 1151 | "607 1 0.466526 area\n", 1152 | "607 2 0.105107 area\n", 1153 | "607 3 0.040567 area\n", 1154 | "607 6 0.300568 area\n", 1155 | "607 7 0.086667 area\n", 1156 | "1578 7 0.996823 arm\n", 1157 | "2031 7 0.984294 audible\n", 1158 | "88 1 0.089456 auto\n", 1159 | "88 3 0.745469 auto\n", 1160 | "88 4 0.042864 auto\n", 1161 | "88 5 0.121139 auto\n", 1162 | "2878 4 0.101143 automatic\n", 1163 | "2878 5 0.898529 automatic\n", 1164 | "2010 6 0.993560 backing\n", 1165 | "38 2 0.983468 bag\n", 1166 | "38 4 0.015130 bag\n", 1167 | "... ... ... ...\n", 1168 | "927 7 0.999305 windshield\n", 1169 | "1185 4 0.158271 winter\n", 1170 | "1185 7 0.840815 winter\n", 1171 | "535 1 0.841607 wipe\n", 1172 | "535 7 0.156974 wipe\n", 1173 | "928 7 0.999651 wiper\n", 1174 | "80 4 0.998232 wire\n", 1175 | "2600 4 0.998173 wiring\n", 1176 | "223 1 0.168628 work\n", 1177 | "223 2 0.223024 work\n", 1178 | "223 3 0.262310 work\n", 1179 | "223 4 0.098215 work\n", 1180 | "223 5 0.059231 work\n", 1181 | "223 6 0.101842 work\n", 1182 | "223 7 0.087034 work\n", 1183 | "225 1 0.079150 worth\n", 1184 | "225 2 0.049201 worth\n", 1185 | "225 3 0.759412 worth\n", 1186 | "225 4 0.032088 worth\n", 1187 | "225 5 0.080220 worth\n", 1188 | "2367 6 0.989773 wrangler\n", 1189 | "2129 2 0.996307 wrap\n", 1190 | "2597 3 0.997510 wrench\n", 1191 | "274 1 0.248133 year\n", 1192 | "274 2 0.035386 year\n", 1193 | "274 3 0.223535 year\n", 1194 | "274 4 0.126871 year\n", 1195 | "274 5 0.130755 year\n", 1196 | "274 7 0.235618 year\n", 1197 | "16268 5 0.996310 zubie\n", 1198 | "\n", 1199 | "[725 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[4, 6, 7, 5, 1, 2, 3])" 1200 | ] 1201 | }, 1202 | "metadata": { 1203 | "tags": [] 1204 | }, 1205 | "execution_count": 120 1206 | } 1207 | ] 1208 | }, 1209 | { 1210 | "metadata": { 1211 | "id": "2Krd9ndB1S-0", 1212 | "colab_type": "code", 1213 | "colab": {} 1214 | }, 1215 | "cell_type": "code", 1216 | "source": [ 1217 | "# Print the Keyword in the 10 topics\n", 1218 | "lda_model.print_topics()" 1219 | ], 1220 | "execution_count": 0, 1221 | "outputs": [] 1222 | }, 1223 | { 1224 | "metadata": { 1225 | "id": "QQk4i7FQC6V_", 1226 | "colab_type": "code", 1227 | "colab": {} 1228 | }, 1229 | "cell_type": "code", 1230 | "source": [ 1231 | "" 1232 | ], 1233 | "execution_count": 0, 1234 | "outputs": [] 1235 | } 1236 | ] 1237 | } --------------------------------------------------------------------------------