Topic Modeling/topic modeling example/Topic Modeling using LDA algorithm on Amazon product review and Demonetization Tweets dataset.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LDA Demonstration\n",
    "\n",
    "\n",
    "In this notebook, we will build LDA models on various datasets. We'll use the gensim implementation of LDA, though sklearn also comes with one. \n",
    "\n",
    "We will also use the library spacy for preprocessing (specifically lemmatisation). Though you can also perform lemmatisation in NLTK, it is slightly more convenient and less verbose in spacy. For visualising the topics and the word-topic distributions (interactively!), we'll use the 'pyLDAvis' module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries  \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import nltk\n",
    "import re, random, os\n",
    "import string, pprint\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# spacy for basic preprocessing, optional, can use nltk as well (lemmatisation etc.)\n",
    "import spacy\n",
    "\n",
    "# gensim for LDA \n",
    "import gensim\n",
    "import gensim.corpora as corpora\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "# Plotting tools\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim  # don't skip this\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amazon Product Reviews Dataset - Echo/Alexa\n",
    "\n",
    "For building topic models, let's experiment with the Amazon product reviews dataset. We have a list of reviews of a few amazon products such as Kindle, Echo (Alexa) etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('amazon_product_review.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>asins</th>\n",
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       "      <th>keys</th>\n",
       "      <th>...</th>\n",
       "      <th>reviews.rating</th>\n",
       "      <th>reviews.sourceURLs</th>\n",
       "      <th>reviews.text</th>\n",
       "      <th>reviews.title</th>\n",
       "      <th>reviews.userCity</th>\n",
       "      <th>reviews.userProvince</th>\n",
       "      <th>reviews.username</th>\n",
       "      <th>sizes</th>\n",
       "      <th>upc</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>AVpe7AsMilAPnD_xQ78G</td>\n",
       "      <td>B00QJDU3KY</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon Devices,mazon.co.uk</td>\n",
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       "      <td>169 mm x 117 mm x 9.1 mm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kindlepaperwhite/b00qjdu3ky</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://www.amazon.com/Kindle-Paperwhite-High-...</td>\n",
       "      <td>I initially had trouble deciding between the p...</td>\n",
       "      <td>Paperwhite voyage, no regrets!</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cristina M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205 grams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AVpe7AsMilAPnD_xQ78G</td>\n",
       "      <td>B00QJDU3KY</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon Devices,mazon.co.uk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-08T20:21:53Z</td>\n",
       "      <td>2017-07-18T23:52:58Z</td>\n",
       "      <td>169 mm x 117 mm x 9.1 mm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kindlepaperwhite/b00qjdu3ky</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://www.amazon.com/Kindle-Paperwhite-High-...</td>\n",
       "      <td>Allow me to preface this with a little history...</td>\n",
       "      <td>One Simply Could Not Ask For More</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ricky</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205 grams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AVpe7AsMilAPnD_xQ78G</td>\n",
       "      <td>B00QJDU3KY</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon Devices,mazon.co.uk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-08T20:21:53Z</td>\n",
       "      <td>2017-07-18T23:52:58Z</td>\n",
       "      <td>169 mm x 117 mm x 9.1 mm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kindlepaperwhite/b00qjdu3ky</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://www.amazon.com/Kindle-Paperwhite-High-...</td>\n",
       "      <td>I am enjoying it so far. Great for reading. Ha...</td>\n",
       "      <td>Great for those that just want an e-reader</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tedd Gardiner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205 grams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AVpe7AsMilAPnD_xQ78G</td>\n",
       "      <td>B00QJDU3KY</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon Devices,mazon.co.uk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-08T20:21:53Z</td>\n",
       "      <td>2017-07-18T23:52:58Z</td>\n",
       "      <td>169 mm x 117 mm x 9.1 mm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kindlepaperwhite/b00qjdu3ky</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://www.amazon.com/Kindle-Paperwhite-High-...</td>\n",
       "      <td>I bought one of the first Paperwhites and have...</td>\n",
       "      <td>Love / Hate relationship</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dougal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205 grams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AVpe7AsMilAPnD_xQ78G</td>\n",
       "      <td>B00QJDU3KY</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon Devices,mazon.co.uk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-08T20:21:53Z</td>\n",
       "      <td>2017-07-18T23:52:58Z</td>\n",
       "      <td>169 mm x 117 mm x 9.1 mm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kindlepaperwhite/b00qjdu3ky</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://www.amazon.com/Kindle-Paperwhite-High-...</td>\n",
       "      <td>I have to say upfront - I don't like coroporat...</td>\n",
       "      <td>I LOVE IT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miljan David Tanic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205 grams</td>\n",
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       "<p>5 rows × 27 columns</p>\n",
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       "                     id       asins   brand                  categories  \\\n",
       "0  AVpe7AsMilAPnD_xQ78G  B00QJDU3KY  Amazon  Amazon Devices,mazon.co.uk   \n",
       "1  AVpe7AsMilAPnD_xQ78G  B00QJDU3KY  Amazon  Amazon Devices,mazon.co.uk   \n",
       "2  AVpe7AsMilAPnD_xQ78G  B00QJDU3KY  Amazon  Amazon Devices,mazon.co.uk   \n",
       "3  AVpe7AsMilAPnD_xQ78G  B00QJDU3KY  Amazon  Amazon Devices,mazon.co.uk   \n",
       "4  AVpe7AsMilAPnD_xQ78G  B00QJDU3KY  Amazon  Amazon Devices,mazon.co.uk   \n",
       "\n",
       "  colors             dateAdded           dateUpdated  \\\n",
       "0    NaN  2016-03-08T20:21:53Z  2017-07-18T23:52:58Z   \n",
       "1    NaN  2016-03-08T20:21:53Z  2017-07-18T23:52:58Z   \n",
       "2    NaN  2016-03-08T20:21:53Z  2017-07-18T23:52:58Z   \n",
       "3    NaN  2016-03-08T20:21:53Z  2017-07-18T23:52:58Z   \n",
       "4    NaN  2016-03-08T20:21:53Z  2017-07-18T23:52:58Z   \n",
       "\n",
       "                  dimension  ean                         keys    ...      \\\n",
       "0  169 mm x 117 mm x 9.1 mm  NaN  kindlepaperwhite/b00qjdu3ky    ...       \n",
       "1  169 mm x 117 mm x 9.1 mm  NaN  kindlepaperwhite/b00qjdu3ky    ...       \n",
       "2  169 mm x 117 mm x 9.1 mm  NaN  kindlepaperwhite/b00qjdu3ky    ...       \n",
       "3  169 mm x 117 mm x 9.1 mm  NaN  kindlepaperwhite/b00qjdu3ky    ...       \n",
       "4  169 mm x 117 mm x 9.1 mm  NaN  kindlepaperwhite/b00qjdu3ky    ...       \n",
       "\n",
       "  reviews.rating                                 reviews.sourceURLs  \\\n",
       "0            5.0  https://www.amazon.com/Kindle-Paperwhite-High-...   \n",
       "1            5.0  https://www.amazon.com/Kindle-Paperwhite-High-...   \n",
       "2            4.0  https://www.amazon.com/Kindle-Paperwhite-High-...   \n",
       "3            5.0  https://www.amazon.com/Kindle-Paperwhite-High-...   \n",
       "4            5.0  https://www.amazon.com/Kindle-Paperwhite-High-...   \n",
       "\n",
       "                                        reviews.text  \\\n",
       "0  I initially had trouble deciding between the p...   \n",
       "1  Allow me to preface this with a little history...   \n",
       "2  I am enjoying it so far. Great for reading. Ha...   \n",
       "3  I bought one of the first Paperwhites and have...   \n",
       "4  I have to say upfront - I don't like coroporat...   \n",
       "\n",
       "                                reviews.title reviews.userCity  \\\n",
       "0              Paperwhite voyage, no regrets!              NaN   \n",
       "1           One Simply Could Not Ask For More              NaN   \n",
       "2  Great for those that just want an e-reader              NaN   \n",
       "3                    Love / Hate relationship              NaN   \n",
       "4                                   I LOVE IT              NaN   \n",
       "\n",
       "  reviews.userProvince    reviews.username  sizes upc     weight  \n",
       "0                  NaN          Cristina M    NaN NaN  205 grams  \n",
       "1                  NaN               Ricky    NaN NaN  205 grams  \n",
       "2                  NaN       Tedd Gardiner    NaN NaN  205 grams  \n",
       "3                  NaN              Dougal    NaN NaN  205 grams  \n",
       "4                  NaN  Miljan David Tanic    NaN NaN  205 grams  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now filter the dataframe to only one product - Amazon Echo. If you are not aware of Echo, <a href=\"https://www.amazon.in/Amazon-Echo-control-weather-Powered/dp/B0749YXL1J?tag=googinhydr18418-21\">here's the amazon page</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>AVpfpK8KLJeJML43BCuD</td>\n",
       "      <td>B01BH83OOM</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon Devices,Home,Smart Home &amp; Connected Liv...</td>\n",
       "      <td>Black</td>\n",
       "      <td>2017-01-04T03:51:17Z</td>\n",
       "      <td>2017-08-13T08:31:07Z</td>\n",
       "      <td>4.8 in x 6.6 in x 3.2 in</td>\n",
       "      <td>8.416670e+11</td>\n",
       "      <td>amazontapalexaenabledportablebluetoothspeaker/...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://www.amazon.com/dp/B01BH83OOM,https://w...</td>\n",
       "      <td>IMPORTANT UPDATE (3/8/17): As you read my orig...</td>\n",
       "      <td>The Echo's Little Sister Is AMAZING!! 3,</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Deanokat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.416670e+11</td>\n",
       "      <td>1.75 lbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>AVpfpK8KLJeJML43BCuD</td>\n",
       "      <td>B01BH83OOM</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon Devices,Home,Smart Home &amp; Connected Liv...</td>\n",
       "      <td>Black</td>\n",
       "      <td>2017-01-04T03:51:17Z</td>\n",
       "      <td>2017-08-13T08:31:07Z</td>\n",
       "      <td>4.8 in x 6.6 in x 3.2 in</td>\n",
       "      <td>8.416670e+11</td>\n",
       "      <td>amazontapalexaenabledportablebluetoothspeaker/...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://www.amazon.com/dp/B01BH83OOM,https://w...</td>\n",
       "      <td>It was just a few weeks ago that I was bemoani...</td>\n",
       "      <td>Tap Alexa on the go!</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Heather A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.416670e+11</td>\n",
       "      <td>1.75 lbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>AVpfpK8KLJeJML43BCuD</td>\n",
       "      <td>B01BH83OOM</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon Devices,Home,Smart Home &amp; Connected Liv...</td>\n",
       "      <td>Black</td>\n",
       "      <td>2017-01-04T03:51:17Z</td>\n",
       "      <td>2017-08-13T08:31:07Z</td>\n",
       "      <td>4.8 in x 6.6 in x 3.2 in</td>\n",
       "      <td>8.416670e+11</td>\n",
       "      <td>amazontapalexaenabledportablebluetoothspeaker/...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://www.amazon.com/dp/B01BH83OOM,https://w...</td>\n",
       "      <td>The Amazon Tap is a portable Bluetooth speaker...</td>\n",
       "      <td>Amazon Tap Review - Best Bluetooth Speaker in ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quentin Moore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.416670e+11</td>\n",
       "      <td>1.75 lbs</td>\n",
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       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>AVpfpK8KLJeJML43BCuD</td>\n",
       "      <td>B01BH83OOM</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon Devices,Home,Smart Home &amp; Connected Liv...</td>\n",
       "      <td>Black</td>\n",
       "      <td>2017-01-04T03:51:17Z</td>\n",
       "      <td>2017-08-13T08:31:07Z</td>\n",
       "      <td>4.8 in x 6.6 in x 3.2 in</td>\n",
       "      <td>8.416670e+11</td>\n",
       "      <td>amazontapalexaenabledportablebluetoothspeaker/...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>http://reviews.bestbuy.com/3545/5097300/review...</td>\n",
       "      <td>Look at this product as a portable speaker fir...</td>\n",
       "      <td>Great for what it does</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Casey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.416670e+11</td>\n",
       "      <td>1.75 lbs</td>\n",
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       "      <th>856</th>\n",
       "      <td>AVpfpK8KLJeJML43BCuD</td>\n",
       "      <td>B01BH83OOM</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon Devices,Home,Smart Home &amp; Connected Liv...</td>\n",
       "      <td>Black</td>\n",
       "      <td>2017-01-04T03:51:17Z</td>\n",
       "      <td>2017-08-13T08:31:07Z</td>\n",
       "      <td>4.8 in x 6.6 in x 3.2 in</td>\n",
       "      <td>8.416670e+11</td>\n",
       "      <td>amazontapalexaenabledportablebluetoothspeaker/...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>http://reviews.bestbuy.com/3545/5097300/review...</td>\n",
       "      <td>This Amazon tap is not only a great Bluetooth ...</td>\n",
       "      <td>Awesome, smart little portable speaker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tazman3582</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.416670e+11</td>\n",
       "      <td>1.75 lbs</td>\n",
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       "<p>5 rows × 27 columns</p>\n",
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      "text/plain": [
       "                       id       asins   brand  \\\n",
       "852  AVpfpK8KLJeJML43BCuD  B01BH83OOM  Amazon   \n",
       "853  AVpfpK8KLJeJML43BCuD  B01BH83OOM  Amazon   \n",
       "854  AVpfpK8KLJeJML43BCuD  B01BH83OOM  Amazon   \n",
       "855  AVpfpK8KLJeJML43BCuD  B01BH83OOM  Amazon   \n",
       "856  AVpfpK8KLJeJML43BCuD  B01BH83OOM  Amazon   \n",
       "\n",
       "                                            categories colors  \\\n",
       "852  Amazon Devices,Home,Smart Home & Connected Liv...  Black   \n",
       "853  Amazon Devices,Home,Smart Home & Connected Liv...  Black   \n",
       "854  Amazon Devices,Home,Smart Home & Connected Liv...  Black   \n",
       "855  Amazon Devices,Home,Smart Home & Connected Liv...  Black   \n",
       "856  Amazon Devices,Home,Smart Home & Connected Liv...  Black   \n",
       "\n",
       "                dateAdded           dateUpdated                 dimension  \\\n",
       "852  2017-01-04T03:51:17Z  2017-08-13T08:31:07Z  4.8 in x 6.6 in x 3.2 in   \n",
       "853  2017-01-04T03:51:17Z  2017-08-13T08:31:07Z  4.8 in x 6.6 in x 3.2 in   \n",
       "854  2017-01-04T03:51:17Z  2017-08-13T08:31:07Z  4.8 in x 6.6 in x 3.2 in   \n",
       "855  2017-01-04T03:51:17Z  2017-08-13T08:31:07Z  4.8 in x 6.6 in x 3.2 in   \n",
       "856  2017-01-04T03:51:17Z  2017-08-13T08:31:07Z  4.8 in x 6.6 in x 3.2 in   \n",
       "\n",
       "              ean                                               keys  \\\n",
       "852  8.416670e+11  amazontapalexaenabledportablebluetoothspeaker/...   \n",
       "853  8.416670e+11  amazontapalexaenabledportablebluetoothspeaker/...   \n",
       "854  8.416670e+11  amazontapalexaenabledportablebluetoothspeaker/...   \n",
       "855  8.416670e+11  amazontapalexaenabledportablebluetoothspeaker/...   \n",
       "856  8.416670e+11  amazontapalexaenabledportablebluetoothspeaker/...   \n",
       "\n",
       "       ...    reviews.rating  \\\n",
       "852    ...               5.0   \n",
       "853    ...               5.0   \n",
       "854    ...               5.0   \n",
       "855    ...               5.0   \n",
       "856    ...               5.0   \n",
       "\n",
       "                                    reviews.sourceURLs  \\\n",
       "852  https://www.amazon.com/dp/B01BH83OOM,https://w...   \n",
       "853  https://www.amazon.com/dp/B01BH83OOM,https://w...   \n",
       "854  https://www.amazon.com/dp/B01BH83OOM,https://w...   \n",
       "855  http://reviews.bestbuy.com/3545/5097300/review...   \n",
       "856  http://reviews.bestbuy.com/3545/5097300/review...   \n",
       "\n",
       "                                          reviews.text  \\\n",
       "852  IMPORTANT UPDATE (3/8/17): As you read my orig...   \n",
       "853  It was just a few weeks ago that I was bemoani...   \n",
       "854  The Amazon Tap is a portable Bluetooth speaker...   \n",
       "855  Look at this product as a portable speaker fir...   \n",
       "856  This Amazon tap is not only a great Bluetooth ...   \n",
       "\n",
       "                                         reviews.title reviews.userCity  \\\n",
       "852           The Echo's Little Sister Is AMAZING!! 3,              NaN   \n",
       "853                               Tap Alexa on the go!              NaN   \n",
       "854  Amazon Tap Review - Best Bluetooth Speaker in ...              NaN   \n",
       "855                             Great for what it does              NaN   \n",
       "856             Awesome, smart little portable speaker              NaN   \n",
       "\n",
       "    reviews.userProvince  reviews.username  sizes           upc    weight  \n",
       "852                  NaN          Deanokat    NaN  8.416670e+11  1.75 lbs  \n",
       "853                  NaN         Heather A    NaN  8.416670e+11  1.75 lbs  \n",
       "854                  NaN     Quentin Moore    NaN  8.416670e+11  1.75 lbs  \n",
       "855                  NaN             Casey    NaN  8.416670e+11  1.75 lbs  \n",
       "856                  NaN        Tazman3582    NaN  8.416670e+11  1.75 lbs  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter for product id = amazon echo\n",
    "df = df[df['asins']==\"B01BH83OOM\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing\n",
    "\n",
    "Let's first do some preprocessing. For tokenisation, though one can use NLTK as well, let's try using gensim's ```simple_preprocess``` this time. The preprocessing pipeline is mentioned below.<br>\n",
    "\n",
    "1. Tokenize each review (using gensim)\n",
    "2. Remove stop words (including punctuations)\n",
    "3. Lemmatize (using spacy)\n",
    "\n",
    "Though you can build topic models without lemmatisation, it is actually quite important (and highly recommended) because otherwise you may end up getting topics having similar words for e.g. *speaker, speakers* etc. (which are basically referring to the same thing - speaker).\n",
    "\n",
    "Note that lemmatization uses POS tags of words, so we need to specify a list of POS tags - here we've used ```['NOUN', 'ADJ', 'VERB', 'ADV']``` ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['look', 'at', 'this', 'product', 'as', 'portable', 'speaker', 'first', 'and', 'an', 'echo', 'replacement', 'second', 'the', 'big', 'reason', 'to', 'buy', 'this', 'over', 'standard', 'bluetooth', 'speakers', 'is', 'this', 'has', 'wifi', 'streaming', 'enabled', 'when', 'you', 'are', 'at', 'home', 'you', 'can', 'stream', 'music', 'with', 'ease', 'to', 'it', 'voice', 'recognition', 'is', 'top', 'notch', 'and', 'it', 'really', 'shines', 'when', 'using', 'it', 'with', 'other', 'smart', 'home', 'accessories', 'the', 'only', 'problem', 'is', 'having', 'to', 'tap', 'the', 'microphone', 'button', 'for', 'it', 'to', 'listen', 'to', 'your', 'voice', 'if', 'you', 'want', 'bigger', 'sound', 'and', 'always', 'listening', 'features', 'spend', 'the', 'extra', 'money', 'on', 'the', 'amazon', 'echo']\n"
     ]
    }
   ],
   "source": [
    "# tokenize using gensim simple_preprocess\n",
    "def sent_to_words(sentences, deacc=True): # deacc=True removes punctuations\n",
    "    for sentence in sentences:\n",
    "        yield(gensim.utils.simple_preprocess(str(sentence)))  \n",
    "\n",
    "\n",
    "# convert to list\n",
    "data = df['reviews.text'].values.tolist()\n",
    "data_words = list(sent_to_words(data))\n",
    "\n",
    "# sample\n",
    "print(data_words[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below creates a list of stop words. The 'string' module in python comes with a list of punctuation characters, which we'll append to the builtin stopwords of NLTK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create list of stop words\n",
    "# string.punctuation (from the 'string' module) contains a list of punctuations\n",
    "# alexa and echo are also the stop words that will be used frequently in the reviews\n",
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english') + list(string.punctuation) + ['alexa','echo']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# functions for removing stopwords and lemmatization\n",
    "def remove_stopwords(texts):\n",
    "    return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
    "\n",
    "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    \"\"\"https://spacy.io/api/annotation\"\"\"\n",
    "    texts_out = []\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent)) \n",
    "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
    "    return texts_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important Note:** All models are not automatically downloaded with spacy, so you will need to do a ```python -m spacy download en``` to use its preprocessing methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['look', 'product', 'portable', 'speaker', 'first', 'echo', 'replacement', 'second', 'big', 'reason', 'buy', 'standard', 'bluetooth', 'speaker', 'wifi', 'streaming', 'enable', 'home', 'stream', 'music', 'ease', 'voice', 'recognition', 'top', 'notch', 'really', 'shin', 'use', 'smart', 'home', 'accessory', 'problem', 'tap', 'microphone', 'button', 'listen', 'voice', 'want', 'big', 'sound', 'always', 'listen', 'feature', 'spend', 'extra', 'money', 'amazon', 'echo']\n"
     ]
    }
   ],
   "source": [
    "# call functions\n",
    "\n",
    "# remove stop words\n",
    "data_words_nostops = remove_stopwords(data_words)\n",
    "\n",
    "# initialize spacy 'en' model, use only tagger since we don't need parsing or NER \n",
    "# python3 -m spacy download en\n",
    "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
    "\n",
    "# lemmatization keeping only noun, adj, vb, adv\n",
    "data_lemmatized = lemmatization(data_words_nostops, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
    "\n",
    "print(data_lemmatized[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "look at this product as portable speaker first and an echo replacement second the big reason to buy this over standard bluetooth speakers is this has wifi streaming enabled when you are at home you can stream music with ease to it voice recognition is top notch and it really shines when using it with other smart home accessories the only problem is having to tap the microphone button for it to listen to your voice if you want bigger sound and always listening features spend the extra money on the amazon echo \n",
      "\n",
      "look product portable speaker first echo replacement second big reason buy standard bluetooth speaker wifi streaming enable home stream music ease voice recognition top notch really shin use smart home accessory problem tap microphone button listen voice want big sound always listen feature spend extra money amazon echo\n"
     ]
    }
   ],
   "source": [
    "# compare the nostop, lemmatised version with the original one\n",
    "# note that speakers is lemmatised to speaker; \n",
    "print(' '.join(data_words[3]), '\\n')\n",
    "print(' '.join(data_lemmatized[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Dictionary and Corpus\n",
    "\n",
    "Gensim's LDA requires the data in a certain format. Firstly, it needs the corpus as a dicionary of id-word mapping, where each word has a unique numeric ID. This is for computationally efficiency purposes. Secondly, it needs the corpus as a term-document frequency matrix which contains the frequency of each word in each document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(3, 7), (12, 2), (15, 2), (16, 3), (19, 1), (20, 2), (24, 4), (29, 3), (32, 1), (37, 1), (42, 4), (51, 2), (54, 1), (59, 1), (63, 5), (73, 1), (86, 1), (88, 1), (89, 8), (92, 1), (94, 2), (99, 1), (100, 1), (101, 2), (104, 1), (106, 1), (114, 2), (118, 1), (124, 1), (129, 1), (139, 2), (142, 1), (145, 1), (153, 1), (154, 1), (155, 2), (156, 1), (157, 1), (158, 1), (159, 1), (160, 1), (161, 1), (162, 1), (163, 1), (164, 1), (165, 1), (166, 1), (167, 2), (168, 1), (169, 2), (170, 1), (171, 1), (172, 1), (173, 1), (174, 1), (175, 1), (176, 1), (177, 1), (178, 2), (179, 1), (180, 1), (181, 1), (182, 1), (183, 1), (184, 1), (185, 1), (186, 1), (187, 1), (188, 1), (189, 1), (190, 1), (191, 1), (192, 1), (193, 1), (194, 2), (195, 1), (196, 1), (197, 2), (198, 2), (199, 5), (200, 1), (201, 3), (202, 2), (203, 1), (204, 3), (205, 1), (206, 1), (207, 1), (208, 1), (209, 6), (210, 1), (211, 1), (212, 2), (213, 1), (214, 2), (215, 1), (216, 1), (217, 1), (218, 1), (219, 1), (220, 1), (221, 2), (222, 1), (223, 2), (224, 1), (225, 1)]\n"
     ]
    }
   ],
   "source": [
    "# create dictionary and corpus\n",
    "# create dictionary\n",
    "id2word = corpora.Dictionary(data_lemmatized)\n",
    "\n",
    "# Create corpus\n",
    "corpus = [id2word.doc2bow(text) for text in data_lemmatized]\n",
    "\n",
    "# sample\n",
    "print(corpus[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The (3, 7) above represents the fact that the word with id=3 appears 7 times in the second document (review), word id 12 appears twice and so on. The nested list below shows the frequencies of words in the first document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('admit', 1),\n",
       "  ('alexa', 2),\n",
       "  ('allow', 1),\n",
       "  ('amazon', 3),\n",
       "  ('announce', 1),\n",
       "  ('answer', 1),\n",
       "  ('anyway', 1),\n",
       "  ('app', 2),\n",
       "  ('appeal', 1),\n",
       "  ('associate', 1),\n",
       "  ('avid', 1),\n",
       "  ('aware', 1),\n",
       "  ('battery', 3),\n",
       "  ('buy', 1),\n",
       "  ('choose', 2),\n",
       "  ('command', 1),\n",
       "  ('control', 1),\n",
       "  ('convenience', 1),\n",
       "  ('cook', 1),\n",
       "  ('could', 2),\n",
       "  ('day', 1),\n",
       "  ('decide', 2),\n",
       "  ('demand', 1),\n",
       "  ('development', 1),\n",
       "  ('device', 3),\n",
       "  ('dot', 1),\n",
       "  ('drain', 1),\n",
       "  ('early', 2),\n",
       "  ('easily', 2),\n",
       "  ('echo', 6),\n",
       "  ('echos', 1),\n",
       "  ('enable', 1),\n",
       "  ('especially', 1),\n",
       "  ('fabulous', 1),\n",
       "  ('february', 1),\n",
       "  ('financial', 1),\n",
       "  ('firmware', 1),\n",
       "  ('first', 1),\n",
       "  ('free', 6),\n",
       "  ('get', 1),\n",
       "  ('give', 1),\n",
       "  ('go', 1),\n",
       "  ('good', 1),\n",
       "  ('hand', 6),\n",
       "  ('high', 1),\n",
       "  ('hop', 1),\n",
       "  ('important', 1),\n",
       "  ('introduce', 1),\n",
       "  ('keep', 1),\n",
       "  ('kitchen', 1),\n",
       "  ('lack', 1),\n",
       "  ('life', 1),\n",
       "  ('listener', 1),\n",
       "  ('little', 2),\n",
       "  ('lot', 1),\n",
       "  ('love', 1),\n",
       "  ('major', 1),\n",
       "  ('make', 1),\n",
       "  ('maximum', 1),\n",
       "  ('microphone', 1),\n",
       "  ('mind', 1),\n",
       "  ('minimal', 1),\n",
       "  ('mode', 2),\n",
       "  ('music', 2),\n",
       "  ('new', 1),\n",
       "  ('operation', 1),\n",
       "  ('option', 2),\n",
       "  ('original', 1),\n",
       "  ('owner', 2),\n",
       "  ('pick', 1),\n",
       "  ('power', 1),\n",
       "  ('question', 1),\n",
       "  ('quickly', 2),\n",
       "  ('read', 2),\n",
       "  ('regular', 1),\n",
       "  ('require', 1),\n",
       "  ('respond', 1),\n",
       "  ('review', 1),\n",
       "  ('risk', 1),\n",
       "  ('sell', 1),\n",
       "  ('set', 1),\n",
       "  ('setting', 1),\n",
       "  ('shortcoming', 1),\n",
       "  ('skeptical', 1),\n",
       "  ('something', 1),\n",
       "  ('soon', 1),\n",
       "  ('sound', 1),\n",
       "  ('start', 1),\n",
       "  ('stream', 1),\n",
       "  ('tap', 7),\n",
       "  ('thank', 1),\n",
       "  ('thing', 1),\n",
       "  ('think', 1),\n",
       "  ('thrilled', 1),\n",
       "  ('time', 1),\n",
       "  ('today', 1),\n",
       "  ('toggle', 1),\n",
       "  ('touch', 1),\n",
       "  ('try', 2),\n",
       "  ('update', 2),\n",
       "  ('use', 4),\n",
       "  ('voice', 2),\n",
       "  ('want', 1),\n",
       "  ('wanted', 1),\n",
       "  ('whole', 1),\n",
       "  ('wonderful', 1),\n",
       "  ('would', 1)]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# human-readable format of corpus (term-frequency)\n",
    "[[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the Topic Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now build the topic model. We'll define 10 topics to start with. The hyperparameter `alpha` affects sparsity of the document-topic\n",
    "(theta) distributions, whose default value is 1. Similarly, the hyperparameter `eta` can also be specified, which affects the topic-word distribution's sparsity.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(gensim.models.ldamodel.LdaModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build LDA model\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=10, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=100,\n",
    "                                           passes=10,\n",
    "                                           alpha='auto',\n",
    "                                           per_word_topics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now print the topics found in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.061*\"speaker\" + 0.049*\"alexa\" + 0.048*\"sound\" + 0.047*\"good\" + '\n",
      "  '0.038*\"tap\" + 0.034*\"echo\" + 0.030*\"great\" + 0.023*\"portable\" + '\n",
      "  '0.022*\"quality\" + 0.022*\"amazon\"'),\n",
      " (1,\n",
      "  '0.041*\"thing\" + 0.031*\"could\" + 0.027*\"find\" + 0.025*\"put\" + 0.025*\"music\" '\n",
      "  '+ 0.022*\"kid\" + 0.019*\"work\" + 0.018*\"year\" + 0.017*\"lot\" + 0.016*\"dot\"'),\n",
      " (2,\n",
      "  '0.044*\"great\" + 0.038*\"echo\" + 0.031*\"take\" + 0.029*\"button\" + '\n",
      "  '0.029*\"listen\" + 0.023*\"tap\" + 0.021*\"size\" + 0.020*\"love\" + 0.019*\"talk\" + '\n",
      "  '0.019*\"portable\"'),\n",
      " (3,\n",
      "  '0.036*\"hit\" + 0.026*\"small\" + 0.023*\"bedroom\" + 0.022*\"want\" + '\n",
      "  '0.020*\"version\" + 0.019*\"fantastic\" + 0.018*\"functionality\" + 0.015*\"part\" '\n",
      "  '+ 0.015*\"computer\" + 0.014*\"cell\"'),\n",
      " (4,\n",
      "  '0.064*\"buy\" + 0.042*\"love\" + 0.038*\"portability\" + 0.031*\"purchase\" + '\n",
      "  '0.028*\"great\" + 0.027*\"gift\" + 0.025*\"feature\" + 0.025*\"sound\" + '\n",
      "  '0.022*\"wish\" + 0.021*\"good\"'),\n",
      " (5,\n",
      "  '0.070*\"tap\" + 0.047*\"echo\" + 0.031*\"use\" + 0.029*\"get\" + 0.029*\"music\" + '\n",
      "  '0.024*\"phone\" + 0.019*\"work\" + 0.018*\"amazon\" + 0.017*\"dot\" + 0.016*\"play\"'),\n",
      " (6,\n",
      "  '0.056*\"use\" + 0.040*\"hand\" + 0.039*\"enjoy\" + 0.037*\"able\" + 0.024*\"free\" + '\n",
      "  '0.018*\"unit\" + 0.017*\"operate\" + 0.016*\"really\" + 0.015*\"excellent\" + '\n",
      "  '0.014*\"answer\"'),\n",
      " (7,\n",
      "  '0.037*\"better\" + 0.035*\"excellent\" + 0.029*\"add\" + 0.028*\"first\" + '\n",
      "  '0.020*\"able\" + 0.019*\"call\" + 0.019*\"thing\" + 0.018*\"list\" + 0.018*\"place\" '\n",
      "  '+ 0.018*\"alexa\"'),\n",
      " (8,\n",
      "  '0.044*\"want\" + 0.041*\"news\" + 0.023*\"carry\" + 0.022*\"house\" + 0.018*\"much\" '\n",
      "  '+ 0.016*\"week\" + 0.016*\"control\" + 0.015*\"certain\" + 0.014*\"may\" + '\n",
      "  '0.014*\"hear\"'),\n",
      " (9,\n",
      "  '0.081*\"use\" + 0.067*\"easy\" + 0.056*\"love\" + 0.055*\"great\" + 0.032*\"work\" + '\n",
      "  '0.032*\"ask\" + 0.029*\"time\" + 0.025*\"music\" + 0.025*\"fun\" + 0.023*\"sound\"')]\n"
     ]
    }
   ],
   "source": [
    "# print the 10 topics\n",
    "pprint.pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now evaluate the model using coherence score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coherence Score:  0.4024593193374404\n"
     ]
    }
   ],
   "source": [
    "# coherence score\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets visualise the topics. The `pyLDAvis` library comes with excellent interactive visualisation capabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el989346978855762408915970\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el989346978855762408915970_data = {\"mdsDat\": {\"Freq\": [28.988433837890625, 16.31891632080078, 11.436193466186523, 10.973428726196289, 9.703831672668457, 7.300774097442627, 4.386317253112793, 4.342255592346191, 3.8795135021209717, 2.670339584350586], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"x\": [-0.23692130106770737, -0.14150740582281415, -0.10659261164779174, -0.151817976629626, -0.0851430412333747, 0.08850914959325644, 0.1161997160647803, 0.15901494063272392, 0.1771536497719328, 0.18110488033862113], \"y\": [0.0882197714294874, 0.13173945118977778, -0.2605084973639571, 0.053533778566010735, -0.10749083964230945, 0.058125996674275794, 0.014097987550690767, -0.010179190519586385, 0.07412934640996759, -0.04166780429435707]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "0      28.988434        1       1 -0.236921  0.088220\n",
       "5      16.318916        1       2 -0.141507  0.131739\n",
       "4      11.436193        1       3 -0.106593 -0.260508\n",
       "2      10.973429        1       4 -0.151818  0.053534\n",
       "9       9.703832        1       5 -0.085143 -0.107491\n",
       "6       7.300774        1       6  0.088509  0.058126\n",
       "1       4.386317        1       7  0.116200  0.014098\n",
       "7       4.342256        1       8  0.159015 -0.010179\n",
       "8       3.879514        1       9  0.177154  0.074129\n",
       "3       2.670340        1      10  0.181105 -0.041668, topic_info=     Category        Freq         Term       Total  loglift  logprob\n",
       "term                                                                \n",
       "100   Default  170.000000          use  170.000000  30.0000  30.0000\n",
       "419   Default   62.000000         easy   62.000000  29.0000  29.0000\n",
       "181   Default  219.000000        great  219.000000  28.0000  28.0000\n",
       "13    Default   92.000000          buy   92.000000  27.0000  27.0000\n",
       "55    Default  148.000000         love  148.000000  26.0000  26.0000\n",
       "209   Default  194.000000      speaker  194.000000  25.0000  25.0000\n",
       "42    Default  159.000000         good  159.000000  24.0000  24.0000\n",
       "89    Default  271.000000          tap  271.000000  23.0000  23.0000\n",
       "86    Default  203.000000        sound  203.000000  22.0000  22.0000\n",
       "29    Default  226.000000         echo  226.000000  21.0000  21.0000\n",
       "1     Default  174.000000        alexa  174.000000  20.0000  20.0000\n",
       "63    Default  135.000000        music  135.000000  19.0000  19.0000\n",
       "305   Default   42.000000  portability   42.000000  18.0000  18.0000\n",
       "102   Default   44.000000         want   44.000000  17.0000  17.0000\n",
       "299   Default   34.000000         able   34.000000  16.0000  16.0000\n",
       "43    Default   28.000000         hand   28.000000  15.0000  15.0000\n",
       "190   Default   54.000000       listen   54.000000  14.0000  14.0000\n",
       "302   Default   32.000000        enjoy   32.000000  13.0000  13.0000\n",
       "167   Default   73.000000       button   73.000000  12.0000  12.0000\n",
       "91    Default   30.000000        thing   30.000000  11.0000  11.0000\n",
       "307   Default   43.000000         take   43.000000  10.0000  10.0000\n",
       "350   Default   94.000000         work   94.000000   9.0000   9.0000\n",
       "314   Default   35.000000     purchase   35.000000   8.0000   8.0000\n",
       "39    Default  110.000000          get  110.000000   7.0000   7.0000\n",
       "94    Default   57.000000         time   57.000000   6.0000   6.0000\n",
       "287   Default   37.000000          ask   37.000000   5.0000   5.0000\n",
       "207   Default   29.000000        small   29.000000   4.0000   4.0000\n",
       "125   Default   33.000000        house   33.000000   3.0000   3.0000\n",
       "271   Default   38.000000        phone   38.000000   2.0000   2.0000\n",
       "340   Default   25.000000    excellent   25.000000   1.0000   1.0000\n",
       "...       ...         ...          ...         ...      ...      ...\n",
       "954   Topic10    3.391064          low    4.155944   3.4196  -4.3202\n",
       "249   Topic10    3.333972        build    4.098870   3.4164  -4.3372\n",
       "826   Topic10    3.133518        ready    3.898326   3.4046  -4.3992\n",
       "1109  Topic10    3.119691  alternative    3.884329   3.4037  -4.4036\n",
       "650   Topic10    2.862119    gathering    3.626732   3.3862  -4.4898\n",
       "1439  Topic10    2.543551    worldwide    3.308232   3.3601  -4.6078\n",
       "1436  Topic10    2.543551     batterey    3.308232   3.3601  -4.6078\n",
       "1437  Topic10    2.543551      camping    3.308232   3.3601  -4.6078\n",
       "1438  Topic10    2.543551       weight    3.308232   3.3601  -4.6078\n",
       "1255  Topic10    2.384848         drop    3.152418   3.3439  -4.6722\n",
       "1069  Topic10    2.187955        cloud    2.952695   3.3232  -4.7584\n",
       "587   Topic10    2.166983        check    2.932043   3.3206  -4.7680\n",
       "652   Topic10    2.109094         yard    2.873729   3.3136  -4.7951\n",
       "981   Topic10    2.040123     response    2.804977   3.3046  -4.8283\n",
       "639   Topic10    1.820727         cord    2.585489   3.2723  -4.9421\n",
       "1076  Topic10    1.790512         fill    2.555236   3.2673  -4.9588\n",
       "320   Topic10    1.649919    versatile    2.414725   3.2421  -5.0406\n",
       "1360  Topic10    1.611800      college    2.376599   3.2346  -5.0640\n",
       "1227  Topic10    1.611559     cylinder    2.376702   3.2345  -5.0641\n",
       "1046  Topic10    1.413193        least    2.177830   3.1905  -5.1955\n",
       "589   Topic10    1.364537      exactly    2.129118   3.1781  -5.2305\n",
       "970   Topic10    1.265370       appear    2.030106   3.1502  -5.3060\n",
       "411   Topic10    3.473065      pleased    5.689944   3.1293  -4.2963\n",
       "138   Topic10    3.837363         part    6.687844   3.0675  -4.1965\n",
       "207   Topic10    6.732769        small   29.642344   2.1407  -3.6343\n",
       "102   Topic10    5.551739         want   44.022408   1.5524  -3.8272\n",
       "620   Topic10    2.222543       travel    5.162651   2.7802  -4.7427\n",
       "274   Topic10    3.138546         plug   13.111788   2.1932  -4.3976\n",
       "86    Topic10    2.864119        sound  203.298798  -0.6395  -4.4891\n",
       "7     Topic10    1.877892          app   26.926464   0.9600  -4.9112\n",
       "\n",
       "[512 rows x 6 columns], token_table=      Topic      Freq         Term\n",
       "term                              \n",
       "107       2  0.181276      ability\n",
       "107       3  0.815744      ability\n",
       "299       6  0.750945         able\n",
       "299       8  0.231060         able\n",
       "451       4  0.823261   absolutely\n",
       "155       1  0.927939     activate\n",
       "155       6  0.035690     activate\n",
       "640       4  0.882917   activation\n",
       "453       3  0.941526     actually\n",
       "336       2  0.250143          add\n",
       "336       6  0.208452          add\n",
       "336       8  0.500286          add\n",
       "430       4  0.945661     addition\n",
       "396       6  0.726966   additional\n",
       "526       7  0.663484    advertise\n",
       "108       8  0.955898          ago\n",
       "285       2  0.935844        alarm\n",
       "1         1  0.777222        alexa\n",
       "1         2  0.045719        alexa\n",
       "1         4  0.080008        alexa\n",
       "1         6  0.040004        alexa\n",
       "1         8  0.040004        alexa\n",
       "1         9  0.011430        alexa\n",
       "2         6  0.845521        allow\n",
       "338       7  0.625359       almost\n",
       "157       1  0.517688         also\n",
       "157       2  0.298666         also\n",
       "157       5  0.159289         also\n",
       "1109     10  0.772334  alternative\n",
       "109       5  0.886064      amazing\n",
       "...     ...       ...          ...\n",
       "1422      5  0.919157        watch\n",
       "1067      8  0.621022   waterproof\n",
       "317       2  0.961044      weather\n",
       "151       9  0.883914         week\n",
       "1438     10  0.906829       weight\n",
       "558       4  0.830525     wherever\n",
       "225       1  0.956102           wi\n",
       "521       4  0.295918         wife\n",
       "521       7  0.591835         wife\n",
       "244       1  0.751075         wifi\n",
       "244       4  0.225322         wifi\n",
       "321       1  0.968281     wireless\n",
       "448       3  0.961368         wish\n",
       "105       6  0.936321    wonderful\n",
       "350       2  0.317525         work\n",
       "350       3  0.158762         work\n",
       "350       4  0.063505         work\n",
       "350       5  0.317525         work\n",
       "350       6  0.063505         work\n",
       "350       7  0.084673         work\n",
       "1439     10  0.906829    worldwide\n",
       "331       1  0.965180        worth\n",
       "106       1  0.589831        would\n",
       "106       2  0.026810        would\n",
       "106       3  0.107242        would\n",
       "106       5  0.160863        would\n",
       "106       6  0.107242        would\n",
       "652      10  0.695960         yard\n",
       "153       7  0.946861         year\n",
       "735       9  0.942148          yet\n",
       "\n",
       "[628 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 6, 5, 3, 10, 7, 2, 8, 9, 4])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize the topics\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning - Number of Topics and Alpha\n",
    "\n",
    "Let's now tune the two main hyperparameters - number of topics and alpha. The strategy typically used is to tune these parameters such that the coherence score is maximised."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute coherence value at various values of alpha and num_topics\n",
    "def compute_coherence_values(dictionary, corpus, texts, num_topics_range, alpha_range):\n",
    "    \n",
    "    coherence_values = []\n",
    "    model_list = []\n",
    "    for alpha in alpha_range:\n",
    "        for num_topics in num_topics_range:\n",
    "            lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                               id2word=dictionary,\n",
    "                                               num_topics=num_topics, \n",
    "                                               alpha=alpha,\n",
    "                                               per_word_topics=True)\n",
    "            model_list.append(lda_model)\n",
    "            coherencemodel = CoherenceModel(model=lda_model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "            coherence_values.append((alpha, num_topics, coherencemodel.get_coherence()))\n",
    "        \n",
    "\n",
    "    return model_list, coherence_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build models across a range of num_topics and alpha\n",
    "num_topics_range = [2, 6, 10, 15, 20]\n",
    "alpha_range = [0.01, 0.1, 1]\n",
    "model_list, coherence_values = compute_coherence_values(dictionary=id2word, \n",
    "                                                        corpus=corpus, \n",
    "                                                        texts=data_lemmatized, \n",
    "                                                        num_topics_range=num_topics_range, \n",
    "                                                        alpha_range=alpha_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>num_topics</th>\n",
       "      <th>coherence_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>2</td>\n",
       "      <td>0.465229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>6</td>\n",
       "      <td>0.454546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>10</td>\n",
       "      <td>0.406792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01</td>\n",
       "      <td>15</td>\n",
       "      <td>0.431168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>20</td>\n",
       "      <td>0.402189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.459691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.10</td>\n",
       "      <td>6</td>\n",
       "      <td>0.419994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.438603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.10</td>\n",
       "      <td>15</td>\n",
       "      <td>0.435700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.10</td>\n",
       "      <td>20</td>\n",
       "      <td>0.439248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.449877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.00</td>\n",
       "      <td>6</td>\n",
       "      <td>0.473140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.00</td>\n",
       "      <td>10</td>\n",
       "      <td>0.469619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.00</td>\n",
       "      <td>15</td>\n",
       "      <td>0.469497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.00</td>\n",
       "      <td>20</td>\n",
       "      <td>0.470033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    alpha  num_topics  coherence_value\n",
       "0    0.01           2         0.465229\n",
       "1    0.01           6         0.454546\n",
       "2    0.01          10         0.406792\n",
       "3    0.01          15         0.431168\n",
       "4    0.01          20         0.402189\n",
       "5    0.10           2         0.459691\n",
       "6    0.10           6         0.419994\n",
       "7    0.10          10         0.438603\n",
       "8    0.10          15         0.435700\n",
       "9    0.10          20         0.439248\n",
       "10   1.00           2         0.449877\n",
       "11   1.00           6         0.473140\n",
       "12   1.00          10         0.469619\n",
       "13   1.00          15         0.469497\n",
       "14   1.00          20         0.470033"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coherence_df = pd.DataFrame(coherence_values, columns=['alpha', 'num_topics', 'coherence_value'])\n",
    "coherence_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "def plot_coherence(coherence_df, alpha_range, num_topics_range):\n",
    "    plt.figure(figsize=(16,6))\n",
    "\n",
    "    for i, val in enumerate(alpha_range):\n",
    "\n",
    "        # subplot 1/3/i\n",
    "        plt.subplot(1, 3, i+1)\n",
    "        alpha_subset = coherence_df[coherence_df['alpha']==val]\n",
    "\n",
    "        plt.plot(alpha_subset[\"num_topics\"], alpha_subset[\"coherence_value\"])\n",
    "        plt.xlabel('num_topics')\n",
    "        plt.ylabel('Coherence Value')\n",
    "        plt.title(\"alpha={0}\".format(val))\n",
    "        plt.ylim([0.30, 1])\n",
    "        plt.legend('coherence value', loc='upper left')\n",
    "        plt.xticks(num_topics_range)\n",
    "\n",
    "plot_coherence(coherence_df, alpha_range, num_topics_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demonetisation Tweets\n",
    "\n",
    "Let's now try identifying topics in tweets related to a specific topic - demonetisation. The overall pipeline is the same, apart from someextra preprocessing steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14940, 16)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>text</th>\n",
       "      <th>favorited</th>\n",
       "      <th>favoriteCount</th>\n",
       "      <th>replyToSN</th>\n",
       "      <th>created</th>\n",
       "      <th>truncated</th>\n",
       "      <th>replyToSID</th>\n",
       "      <th>id</th>\n",
       "      <th>replyToUID</th>\n",
       "      <th>statusSource</th>\n",
       "      <th>screenName</th>\n",
       "      <th>retweetCount</th>\n",
       "      <th>isRetweet</th>\n",
       "      <th>retweeted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>RT @rssurjewala: Critical question: Was PayTM ...</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-23 18:40:30</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.014957e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>HASHTAGFARZIWAL</td>\n",
       "      <td>331</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>RT @Hemant_80: Did you vote on #Demonetization...</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-23 18:40:29</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.014957e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>PRAMODKAUSHIK9</td>\n",
       "      <td>66</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>RT @roshankar: Former FinSec, RBI Dy Governor,...</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-23 18:40:03</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.014955e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>rahulja13034944</td>\n",
       "      <td>12</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>RT @ANI_news: Gurugram (Haryana): Post office ...</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-23 18:39:59</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.014955e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>deeptiyvd</td>\n",
       "      <td>338</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>RT @satishacharya: Reddy Wedding! @mail_today ...</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-23 18:39:39</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.014954e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://cpimharyana.com\" rel=\"nofollow...</td>\n",
       "      <td>CPIMBadli</td>\n",
       "      <td>120</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  X                                               text  \\\n",
       "0           1  1  RT @rssurjewala: Critical question: Was PayTM ...   \n",
       "1           2  2  RT @Hemant_80: Did you vote on #Demonetization...   \n",
       "2           3  3  RT @roshankar: Former FinSec, RBI Dy Governor,...   \n",
       "3           4  4  RT @ANI_news: Gurugram (Haryana): Post office ...   \n",
       "4           5  5  RT @satishacharya: Reddy Wedding! @mail_today ...   \n",
       "\n",
       "   favorited  favoriteCount replyToSN              created  truncated  \\\n",
       "0      False              0       NaN  2016-11-23 18:40:30      False   \n",
       "1      False              0       NaN  2016-11-23 18:40:29      False   \n",
       "2      False              0       NaN  2016-11-23 18:40:03      False   \n",
       "3      False              0       NaN  2016-11-23 18:39:59      False   \n",
       "4      False              0       NaN  2016-11-23 18:39:39      False   \n",
       "\n",
       "   replyToSID            id  replyToUID  \\\n",
       "0         NaN  8.014957e+17         NaN   \n",
       "1         NaN  8.014957e+17         NaN   \n",
       "2         NaN  8.014955e+17         NaN   \n",
       "3         NaN  8.014955e+17         NaN   \n",
       "4         NaN  8.014954e+17         NaN   \n",
       "\n",
       "                                        statusSource       screenName  \\\n",
       "0  <a href=\"http://twitter.com/download/android\" ...  HASHTAGFARZIWAL   \n",
       "1  <a href=\"http://twitter.com/download/android\" ...   PRAMODKAUSHIK9   \n",
       "2  <a href=\"http://twitter.com/download/android\" ...  rahulja13034944   \n",
       "3  <a href=\"http://twitter.com/download/android\" ...        deeptiyvd   \n",
       "4  <a href=\"http://cpimharyana.com\" rel=\"nofollow...        CPIMBadli   \n",
       "\n",
       "   retweetCount  isRetweet  retweeted  \n",
       "0           331       True      False  \n",
       "1            66       True      False  \n",
       "2            12       True      False  \n",
       "3           338       True      False  \n",
       "4           120       True      False  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"demonetization_tweets.csv\", encoding = \"ISO-8859-1\")\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'RT @centerofright: #DeMonetization - Ppl are ready to give @narendramodi the 50 Days he asked for in the war against corruption and black m\\x85'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see randomly chosen sample tweets\n",
    "df.text[random.randrange(len(df.text))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    RT @rssurjewala: Critical question: Was PayTM ...\n",
       "1    RT @Hemant_80: Did you vote on #Demonetization...\n",
       "2    RT @roshankar: Former FinSec, RBI Dy Governor,...\n",
       "3    RT @ANI_news: Gurugram (Haryana): Post office ...\n",
       "4    RT @satishacharya: Reddy Wedding! @mail_today ...\n",
       "5    @DerekScissors1: Indias #demonetization: #Bla...\n",
       "6    RT @gauravcsawant: Rs 40 lakh looted from a ba...\n",
       "7    RT @Joydeep_911: Calling all Nationalists to j...\n",
       "8    RT @sumitbhati2002: Many opposition leaders ar...\n",
       "9    National reform now destroyed even the essence...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.text[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that many tweets have strings such as RT, @xyz, etc. Some have URLs, punctuation marks, smileys etc. The following code cleans the data to handle many of these issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rt : critical question: was paytm informed abo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rt _80: did you vote on #demonetization on mod...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rt : former finsec rbi dy governor cbdt chair ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rt _news: gurugram haryana: post office employ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rt : reddy wedding! _today cartoon #demonetiza...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>: indias #demonetization: #blackmoney a sympt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>rt : rs 40 lakh looted from a bank in kishtwar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>rt _911: calling all nationalists to join\\rwal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>rt : many opposition leaders are with  on the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>national reform now destroyed even the essence...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet\n",
       "0  rt : critical question: was paytm informed abo...\n",
       "1  rt _80: did you vote on #demonetization on mod...\n",
       "2  rt : former finsec rbi dy governor cbdt chair ...\n",
       "3  rt _news: gurugram haryana: post office employ...\n",
       "4  rt : reddy wedding! _today cartoon #demonetiza...\n",
       "5  : indias #demonetization: #blackmoney a sympt...\n",
       "6  rt : rs 40 lakh looted from a bank in kishtwar...\n",
       "7  rt _911: calling all nationalists to join\\rwal...\n",
       "8  rt : many opposition leaders are with  on the ...\n",
       "9  national reform now destroyed even the essence..."
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove URLs\n",
    "def remove_URL(x):\n",
    "            return x.replace(r'https[a-zA-Z0-9]*', \"\", regex = True)\n",
    "\n",
    "\n",
    "# clean tweet text\n",
    "def clean_tweets(tweet_col):\n",
    "    \n",
    "    df = pd.DataFrame({'tweet':tweet_col})\n",
    "    \n",
    "    df['tweet'] = df['tweet'].replace(r'\\'|\\\"|\\,|\\.|\\?|\\+|\\-|\\/|\\=|\\(|\\)|\\n|\"', '', regex=True)\n",
    "    df['tweet'] = df['tweet'].replace(\"  \", \" \")\n",
    "    df['tweet'] = df['tweet'].replace(r'@[a-zA-Z0-9]*', '', regex=True)\n",
    "    df['tweet'] = remove_URL(df['tweet'])\n",
    "    df['tweet'] = df['tweet'].str.lower() \n",
    "    \n",
    "    return(df)\n",
    "\n",
    "cleaned_tweets = clean_tweets(df.text)\n",
    "cleaned_tweets[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since tweets often contain slang words such as *wat, rt, lol* etc, we can append the stopwords with a list of such custom words and remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "words_remove = [\"ax\",\"i\",\"you\",\"edu\",\"s\",\"t\",\"m\",\"subject\",\"can\",\"lines\",\"re\",\"what\", \"there\",\n",
    "                    \"all\",\"we\",\"one\",\"the\",\"a\",\"an\",\"of\",\"or\",\"in\",\"for\",\"by\",\"on\",\"but\",\"is\",\"in\",\n",
    "                    \"a\",\"not\",\"with\",\"as\",\"was\",\"if\",\"they\",\"are\",\"this\",\"and\",\"it\",\"have\",\"from\",\"at\",\n",
    "                    \"my\",\"be\",\"by\",\"not\",\"that\",\"to\",\"from\",\"com\",\"org\",\"like\",\"likes\",\"so\",\"said\",\"from\",\n",
    "                    \"what\",\"told\",\"over\",\"more\",\"other\",\"have\",\"last\",\"with\",\"this\",\"that\",\"such\",\"when\",\n",
    "                    \"been\",\"says\",\"will\",\"also\",\"where\",\"why\",\"would\",\"today\", \"in\", \"on\", \"you\", \"r\", \"d\", \n",
    "                    \"u\", \"hw\",\"wat\", \"oly\", \"s\", \"b\", \"ht\", \"rt\", \"p\",\"the\",\"th\", \"lol\", ':']\n",
    "\n",
    "\n",
    "# remove stop words, punctuations \n",
    "stop_words = set(list(stopwords.words('english') + \n",
    "                      list(string.punctuation) +\n",
    "                      words_remove)) \n",
    "\n",
    "data_words = list(sent_to_words(cleaned_tweets.tweet.values.tolist(), deacc=False))\n",
    "\n",
    "\n",
    "# remove stopwords\n",
    "def remove_stopwords(texts, stop_words=stop_words):\n",
    "    return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
    "\n",
    "data_words_nostops = remove_stopwords(data_words)\n",
    "\n",
    "\n",
    "# spacy for lemmatization\n",
    "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
    "data_lemmatized = lemmatization(data_words_nostops, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['critical',\n",
       "  'question',\n",
       "  'paytm',\n",
       "  'inform',\n",
       "  'demonetization',\n",
       "  'edict',\n",
       "  'pm',\n",
       "  'clearly',\n",
       "  'fishy',\n",
       "  'require',\n",
       "  'full',\n",
       "  'disclosure',\n",
       "  'amp'],\n",
       " ['vote', 'demonetization', 'modi', 'survey', 'app'],\n",
       " ['former',\n",
       "  'finsec',\n",
       "  'rbi',\n",
       "  'governor',\n",
       "  'cbdt',\n",
       "  'chair',\n",
       "  'harvard',\n",
       "  'professor',\n",
       "  'lambaste',\n",
       "  'demonetization',\n",
       "  'aadmi',\n",
       "  'listen']]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample lemmatized tweets\n",
    "data_lemmatized[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dictionary and corpus\n",
    "id2word = corpora.Dictionary(data_lemmatized)\n",
    "texts = data_lemmatized\n",
    "corpus = [id2word.doc2bow(text) for text in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build models across a range of num_topics and alpha\n",
    "num_topics_range = [2, 6, 10, 15]\n",
    "alpha_range = [0.01, 0.1, 1]\n",
    "model_list, coherence_values = compute_coherence_values(dictionary=id2word, \n",
    "                                                        corpus=corpus, \n",
    "                                                        texts=data_lemmatized, \n",
    "                                                        num_topics_range=num_topics_range, \n",
    "                                                        alpha_range=alpha_range)\n",
    "\n",
    "coherence_df = pd.DataFrame(coherence_values, columns=['alpha', 'num_topics', 'coherence_value'])\n",
    "coherence_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plot_coherence(coherence_df, alpha_range, num_topics_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build LDA model with alpha=0.1 and 10 topics\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=10, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=100,\n",
    "                                           passes=10,\n",
    "                                           alpha=0.1,\n",
    "                                           per_word_topics=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.098*\"modi\" + 0.088*\"demonetization\" + 0.066*\"poor\" + 0.062*\"love\" + '\n",
      "  '0.062*\"quiet\" + 0.061*\"men\" + 0.061*\"prohibition\" + 0.061*\"tobaccogutkha\" + '\n",
      "  '0.061*\"engli\" + 0.050*\"implement\"'),\n",
      " (1,\n",
      "  '0.084*\"demonetization\" + 0.062*\"youtube\" + 0.059*\"people\" + 0.049*\"economy\" '\n",
      "  '+ 0.027*\"indian\" + 0.026*\"flag\" + 0.026*\"reinstate\" + 0.025*\"thus\" + '\n",
      "  '0.025*\"ge\" + 0.018*\"back\"'),\n",
      " (2,\n",
      "  '0.185*\"ed\" + 0.081*\"demonetization\" + 0.063*\"keep\" + 0.038*\"bc\" + '\n",
      "  '0.036*\"bf\" + 0.036*\"headline\" + 0.036*\"yearend\" + 0.033*\"wave\" + '\n",
      "  '0.028*\"index\" + 0.028*\"ad\"'),\n",
      " (3,\n",
      "  '0.112*\"ban\" + 0.091*\"find\" + 0.081*\"demonetization\" + 0.057*\"economic\" + '\n",
      "  '0.045*\"effect\" + 0.043*\"tcoe\" + 0.028*\"less\" + 0.027*\"system\" + '\n",
      "  '0.025*\"double\" + 0.024*\"already\"'),\n",
      " (4,\n",
      "  '0.085*\"demonetization\" + 0.077*\"pm\" + 0.052*\"due\" + 0.050*\"shortage\" + '\n",
      "  '0.048*\"ufe\" + 0.038*\"question\" + 0.036*\"end\" + 0.034*\"parliament\" + '\n",
      "  '0.033*\"coast\" + 0.033*\"fisherman\"'),\n",
      " (5,\n",
      "  '0.077*\"cash\" + 0.059*\"demonetization\" + 0.050*\"report\" + 0.050*\"supply\" + '\n",
      "  '0.049*\"blame\" + 0.046*\"lack\" + 0.018*\"atms\" + 0.017*\"post\" + 0.016*\"still\" '\n",
      "  '+ 0.013*\"payment\"'),\n",
      " (6,\n",
      "  '0.085*\"demonetization\" + 0.062*\"s\" + 0.059*\"staff\" + 0.057*\"yogi\" + '\n",
      "  '0.057*\"adityanath\" + 0.043*\"amp\" + 0.042*\"youtube\" + 0.026*\"price\" + '\n",
      "  '0.021*\"slip\" + 0.021*\"appear\"'),\n",
      " (7,\n",
      "  '0.140*\"india\" + 0.109*\"demonetization\" + 0.066*\"will\" + 0.053*\"dear\" + '\n",
      "  '0.050*\"actually\" + 0.041*\"make\" + 0.038*\"video\" + 0.035*\"there\" + '\n",
      "  '0.034*\"bypoll\" + 0.029*\"believe\"'),\n",
      " (8,\n",
      "  '0.107*\"good\" + 0.066*\"tco\" + 0.060*\"successful\" + 0.059*\"demonetization\" + '\n",
      "  '0.046*\"politicsit\" + 0.046*\"transparent\" + 0.042*\"get\" + 0.024*\"day\" + '\n",
      "  '0.012*\"rakshaksetc\" + 0.012*\"swachata\"'),\n",
      " (9,\n",
      "  '0.115*\"rich\" + 0.079*\"demonetization\" + 0.075*\"result\" + 0.028*\"cag\" + '\n",
      "  '0.028*\"audit\" + 0.028*\"royal\" + 0.028*\"goalpost\" + 0.020*\"corruption\" + '\n",
      "  '0.018*\"take\" + 0.016*\"lot\"')]\n"
     ]
    }
   ],
   "source": [
    "# print keywords \n",
    "pprint.pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coherence Score:  0.4645754680906517\n"
     ]
    }
   ],
   "source": [
    "# coherence score\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('Coherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el989347172343603124293891\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el989347172343603124293891_data = {\"mdsDat\": {\"Freq\": [14.642657279968262, 13.439915657043457, 10.127768516540527, 9.871559143066406, 9.829673767089844, 9.50656509399414, 9.334724426269531, 8.173550605773926, 7.688958168029785, 7.384620666503906], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"x\": [-0.053407754532371086, 0.06622017823473811, -0.12550885934303133, -0.11766072047319572, 0.27193158775011206, -0.09017605123437918, 0.2283249292743274, -0.07137344106681003, -0.026028318811465966, -0.0823215497979244], \"y\": [-0.009415978431527562, 0.0048471108273569884, -0.0033424136689939265, 0.016893814436681674, -0.27333408785306507, -0.014362076696438962, 0.3154798575131, -0.0057704751008925935, -0.021636045461061224, -0.009359705565159828]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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0.9931229948997498, 0.9933065176010132, 0.026148797944188118, 0.0784463956952095, 0.7321663498878479, 0.13074399530887604, 0.9940059185028076, 0.9969909191131592, 0.00199633720330894, 0.0059890118427574635, 0.9921796321868896, 0.9921878576278687, 0.9984533190727234, 0.9953473210334778, 0.41167572140693665, 0.5881081819534302, 0.003331382991746068, 0.9860894083976746, 0.003331382991746068, 0.006662765983492136, 0.9889571070671082], \"Term\": [\"aadhaar\", \"abhijit\", \"action\", \"actually\", \"ad\", \"adityanath\", \"adsensegate\", \"already\", \"amp\", \"amp\", \"amp\", \"amp\", \"analysis\", \"appear\", \"article\", \"atms\", \"audit\", \"back\", \"ban\", \"banerjee\", \"bank\", \"banker\", \"banking\", \"baseless\", \"baseless\", \"baseless\", \"bc\", \"be\", \"begin\", \"believe\", \"benefit\", \"bf\", \"bill\", \"bjp\", \"black\", \"blame\", \"buy\", \"bypoll\", \"cag\", \"call\", \"can\", \"cash\", \"change\", \"charge\", \"cia\", \"cia\", \"claim\", \"close\", \"coast\", \"coast\", \"coast\", \"coast\", \"come\", \"concern\", \"consumer\", \"contract\", \"contrary\", \"corruption\", \"cost\", \"could\", \"country\", \"cpi\", \"crisis\", \"crore\", \"cross\", \"cross\", \"crunch\", \"currency\", \"daily\", \"datum\", \"day\", \"dear\", \"demon\", \"demonetisation\", \"demonetization\", \"demonetization\", \"demonetization\", \"demonetization\", \"demonetization\", \"demonetization\", \"demonetization\", \"demonetization\", \"demonetization\", \"demonetization\", \"digital\", \"discuss\", \"diwasyoga\", \"do\", \"doomsday\", \"double\", \"drama\", \"drivesgau\", \"dry\", \"due\", \"earlier\", \"economic\", \"economy\", \"ed\", \"effect\", \"end\", \"engli\", \"enough\", \"estate\", \"evanspiegel\", \"even\", \"evm\", \"example\", \"exchange\", \"exclusive\", \"export\", \"face\", \"failure\", \"fast\", \"fast\", \"feat\", \"feel\", \"fight\", \"find\", \"finger\", \"finger\", \"fintech\", \"fire\", \"first\", \"fisherman\", \"fisherman\", \"fisherman\", \"fisherman\", \"fix\", \"flag\", \"formation\", \"formation\", \"formation\", \"formation\", \"free\", \"fstd\", \"ge\", \"get\", \"give\", \"give\", \"global\", \"go\", \"goalpost\", \"goalpost\", \"goalpost\", \"goalpost\", \"goalpost\", \"good\", \"governance\", \"govt\", \"grand\", \"great\", \"grow\", \"growth\", \"happen\", \"hawaiian\", \"hawaiian\", \"hawaiian\", \"hawaiian\", \"headline\", \"hear\", \"help\", \"hope\", \"hopefully\", \"idea\", \"iip\", \"impact\", \"implement\", \"include\", \"income\", \"index\", \"india\", \"indian\", \"industrial\", \"informal\", \"inspire\", \"issue\", \"japan\", \"japan\", \"japan\", \"japan\", \"jnu\", \"join\", \"keep\", \"kerala\", \"know\", \"lack\", \"late\", \"less\", \"level\", \"like\", \"look\", \"loot\", \"lose\", \"lot\", \"love\", \"lpfv\", \"make\", \"mamata\", \"mandate\", \"market\", \"may\", \"mean\", \"medium\", \"men\", \"minister\", \"mla\", \"mobile\", \"modi\", \"modis\", \"money\", \"month\", \"morgan\", \"morgan\", \"motilal\", \"motilal\", \"move\", \"much\", \"must\", \"national\", \"need\", \"news\", \"news\", \"night\", \"normalcy\", \"not\", \"note\", \"ntil\", \"offensive\", \"offensive\", \"offensive\", \"offensive\", \"oil\", \"old\", \"opportunity\", \"oswal\", \"oswal\", \"overall\", \"parliament\", \"partner\", \"patel\", \"pathetic\", \"payment\", \"pelter\", \"people\", \"percent\", \"period\", \"pm\", \"pm\", \"politicsit\", \"politicsit\", \"politicsit\", \"politicsit\", \"politicsit\", \"poo\", \"poor\", \"post\", \"poverty\", \"predict\", \"prediction\", \"price\", \"print\", \"product\", \"production\", \"prohibition\", \"prove\", \"puthanpanam\", \"puthanpanam\", \"puthanpanam\", \"puthanpanam\", \"question\", \"quiet\", \"racism\", \"rakshaksetc\", \"rbi\", \"read\", \"real\", \"reflect\", \"reform\", \"reinstate\", \"report\", \"require\", \"research\", \"responsible\", \"restriction\", \"result\", \"return\", \"revert\", \"revert\", \"rich\", \"riffin\", \"right\", \"rise\", \"royal\", \"royal\", \"royal\", \"royal\", \"royal\", \"rs\", \"rule\", \"run\", \"s\", \"sanghis\", \"sanghis\", \"sanghis\", \"sanghis\", \"sanghis\", \"sanghis\", \"scam\", \"scot\", \"scot\", \"scot\", \"security\", \"see\", \"service\", \"set\", \"shopping\", \"shortage\", \"silence\", \"sir\", \"slip\", \"soon\", \"source\", \"speech\", \"staff\", \"stand\", \"still\", \"stone\", \"strike\", \"stunt\", \"subverting\", \"subverting\", \"subverting\", \"subverting\", \"success\", \"successful\", \"supply\", \"support\", \"surgical\", \"swachata\", \"system\", \"take\", \"talk\", \"tax\", \"tco\", \"tcoe\", \"tcolffzzfokf\", \"tcon\", \"tcorlpk\", \"tcotaeysn\", \"tcotyriiekziu\", \"team\", \"thank\", \"there\", \"think\", \"thug\", \"thus\", \"time\", \"tobaccogutkha\", \"transparent\", \"transparent\", \"transparent\", \"transparent\", \"transparent\", \"true\", \"turk\", \"ufe\", \"update\", \"use\", \"video\", \"vote\", \"wake\", \"want\", \"warming\", \"warming\", \"warming\", \"warming\", \"waroncash\", \"watch\", \"wave\", \"way\", \"well\", \"wgdeaa\", \"wgdeaa\", \"whammy\", \"whole\", \"will\", \"withdrawal\", \"would\", \"wwii\", \"wwii\", \"wwii\", \"wwii\", \"ybf\", \"year\", \"yearend\", \"yearend\", \"yearend\", \"yet\", \"yogi\", \"young\", \"youtube\", \"youtube\", \"youtubeisdead\", \"youtubeisdead\", \"youtubeisdead\", \"youtubeisdead\", \"youtuber\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [5, 8, 6, 7, 1, 2, 3, 10, 4, 9]};\n",
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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "4      14.642657        1       1 -0.053408 -0.009416\n",
       "7      13.439916        1       2  0.066220  0.004847\n",
       "5      10.127769        1       3 -0.125509 -0.003342\n",
       "6       9.871559        1       4 -0.117661  0.016894\n",
       "0       9.829674        1       5  0.271932 -0.273334\n",
       "1       9.506565        1       6 -0.090176 -0.014362\n",
       "2       9.334724        1       7  0.228325  0.315480\n",
       "9       8.173551        1       8 -0.071373 -0.005770\n",
       "3       7.688958        1       9 -0.026028 -0.021636\n",
       "8       7.384621        1      10 -0.082322 -0.009360, topic_info=     Category         Freq            Term         Total  loglift  logprob\n",
       "term                                                                      \n",
       "97    Default  2581.000000              ed   2581.000000  30.0000  30.0000\n",
       "47    Default  2813.000000           india   2813.000000  29.0000  29.0000\n",
       "593   Default  1397.000000            rich   1397.000000  28.0000  28.0000\n",
       "14    Default  1437.000000            modi   1437.000000  27.0000  27.0000\n",
       "214   Default  1286.000000             ban   1286.000000  26.0000  26.0000\n",
       "10    Default  1678.000000              pm   1678.000000  25.0000  25.0000\n",
       "123   Default  1179.000000            good   1179.000000  24.0000  24.0000\n",
       "1146  Default  1049.000000            find   1049.000000  23.0000  23.0000\n",
       "28    Default  1158.000000            cash   1158.000000  22.0000  22.0000\n",
       "947   Default  1323.000000            will   1323.000000  21.0000  21.0000\n",
       "2212  Default  1496.000000         youtube   1496.000000  20.0000  20.0000\n",
       "206   Default   908.000000          result    908.000000  19.0000  19.0000\n",
       "170   Default   961.000000            poor    961.000000  18.0000  18.0000\n",
       "356   Default  1145.000000             due   1145.000000  17.0000  17.0000\n",
       "1129  Default  1061.000000            dear   1061.000000  16.0000  16.0000\n",
       "57    Default   915.000000               s    915.000000  15.0000  15.0000\n",
       "2241  Default   911.000000            love    911.000000  14.0000  14.0000\n",
       "2616  Default   902.000000           quiet    902.000000  13.0000  13.0000\n",
       "907   Default   881.000000            keep    881.000000  12.0000  12.0000\n",
       "330   Default  1089.000000        shortage   1089.000000  11.0000  11.0000\n",
       "9475  Default   902.000000   tobaccogutkha    902.000000  10.0000  10.0000\n",
       "9474  Default   902.000000     prohibition    902.000000   9.0000   9.0000\n",
       "9473  Default   902.000000             men    902.000000   8.0000   8.0000\n",
       "9472  Default   902.000000           engli    902.000000   7.0000   7.0000\n",
       "604   Default   864.000000           staff    864.000000   6.0000   6.0000\n",
       "6839  Default  1041.000000             ufe   1041.000000   5.0000   5.0000\n",
       "2156  Default   995.000000        actually    995.000000   4.0000   4.0000\n",
       "89    Default   837.000000          people    837.000000   3.0000   3.0000\n",
       "6929  Default   843.000000            yogi    843.000000   2.0000   2.0000\n",
       "8678  Default   833.000000      adityanath    833.000000   1.0000   1.0000\n",
       "...       ...          ...             ...           ...      ...      ...\n",
       "327   Topic10   265.359924             day    266.236145   2.6025  -3.7251\n",
       "7896  Topic10   133.650345       diwasyoga    134.530945   2.5992  -4.4110\n",
       "7897  Topic10   133.650345       drivesgau    134.530945   2.5992  -4.4110\n",
       "7898  Topic10   133.650345     rakshaksetc    134.530945   2.5992  -4.4110\n",
       "7899  Topic10   133.650345           stunt    134.530945   2.5992  -4.4110\n",
       "7900  Topic10   133.650345        swachata    134.530945   2.5992  -4.4110\n",
       "27    Topic10   119.601585             rbi    120.477890   2.5985  -4.5221\n",
       "1832  Topic10    89.840126          update     90.716438   2.5961  -4.8082\n",
       "663   Topic10    90.031380        surgical     90.914536   2.5960  -4.8061\n",
       "9387  Topic10   505.797363      politicsit    511.246979   2.5951  -3.0801\n",
       "9388  Topic10   505.797363     transparent    511.246979   2.5951  -3.0801\n",
       "193   Topic10    74.352966            need     75.229218   2.5941  -4.9974\n",
       "120   Topic10    70.026405         banking     70.904404   2.5933  -5.0573\n",
       "115   Topic10    69.163513            hear     70.039734   2.5932  -5.0697\n",
       "3523  Topic10    66.724701         concern     67.601913   2.5927  -5.1056\n",
       "1462  Topic10    61.246063           datum     62.122265   2.5916  -5.1913\n",
       "7067  Topic10    62.934059     evanspiegel     63.839497   2.5915  -5.1641\n",
       "189   Topic10    58.147156          enough     59.023354   2.5908  -5.2432\n",
       "931   Topic10    56.172852            rule     57.049141   2.5903  -5.2778\n",
       "3647  Topic10    54.269768        analysis     55.146034   2.5898  -5.3123\n",
       "2510  Topic10    55.236095         partner     56.145206   2.5894  -5.2946\n",
       "117   Topic10    49.471489           stand     50.347771   2.5882  -5.4048\n",
       "3055  Topic10    48.947506        informal     49.823761   2.5880  -5.4155\n",
       "6468  Topic10    47.782833            fstd     48.659191   2.5876  -5.4396\n",
       "6469  Topic10    47.782833            ntil     48.659191   2.5876  -5.4396\n",
       "1838  Topic10    46.330379          mamata     47.206615   2.5870  -5.4704\n",
       "8206  Topic10    50.993073          morgan     52.961025   2.5679  -5.3745\n",
       "8208  Topic10    50.993073          wgdeaa     52.961025   2.5679  -5.3745\n",
       "8207  Topic10    50.993073          revert     52.961025   2.5679  -5.3745\n",
       "3     Topic10   651.099304  demonetization  12319.785156  -0.3345  -2.8276\n",
       "\n",
       "[365 rows x 6 columns], token_table=      Topic      Freq           Term\n",
       "term                                \n",
       "4284      3  0.990521        aadhaar\n",
       "7806      9  0.982065        abhijit\n",
       "257       7  0.996938         action\n",
       "2156      2  0.999422       actually\n",
       "664       7  0.998512             ad\n",
       "8678      4  0.997956     adityanath\n",
       "7268      4  0.983787    adsensegate\n",
       "447       9  0.996382        already\n",
       "0         1  0.282494            amp\n",
       "0         3  0.158302            amp\n",
       "0         4  0.550121            amp\n",
       "0         8  0.007871            amp\n",
       "3647     10  0.979218       analysis\n",
       "2798      4  0.998320         appear\n",
       "2605      7  0.988746        article\n",
       "347       3  0.997259           atms\n",
       "8198      8  0.998518          audit\n",
       "1583      6  0.998672           back\n",
       "214       9  0.998969            ban\n",
       "1553      9  0.991890       banerjee\n",
       "51        4  0.992916           bank\n",
       "7122      8  0.994028         banker\n",
       "120      10  0.987245        banking\n",
       "4383      2  0.005231       baseless\n",
       "4383      6  0.983489       baseless\n",
       "4383     10  0.005231       baseless\n",
       "920       7  0.997398             bc\n",
       "582       8  0.992984             be\n",
       "1756      6  0.994292          begin\n",
       "2968      2  0.998235        believe\n",
       "...     ...       ...            ...\n",
       "381       1  0.999112          watch\n",
       "804       7  0.998658           wave\n",
       "500       1  0.989299            way\n",
       "862       9  0.995407           well\n",
       "8208      9  0.018882         wgdeaa\n",
       "8208     10  0.962972         wgdeaa\n",
       "7217      7  0.996277         whammy\n",
       "1459      6  0.980827          whole\n",
       "947       2  0.999618           will\n",
       "1207      2  0.993123     withdrawal\n",
       "875       2  0.993307          would\n",
       "9462      2  0.026149           wwii\n",
       "9462      3  0.078446           wwii\n",
       "9462      5  0.732166           wwii\n",
       "9462      6  0.130744           wwii\n",
       "9586      1  0.994006            ybf\n",
       "1529      2  0.996991           year\n",
       "9236      1  0.001996        yearend\n",
       "9236      3  0.005989        yearend\n",
       "9236      7  0.992180        yearend\n",
       "2468      9  0.992188            yet\n",
       "6929      4  0.998453           yogi\n",
       "5561      6  0.995347          young\n",
       "2212      4  0.411676        youtube\n",
       "2212      6  0.588108        youtube\n",
       "9793      2  0.003331  youtubeisdead\n",
       "9793      4  0.986089  youtubeisdead\n",
       "9793      7  0.003331  youtubeisdead\n",
       "9793      9  0.006663  youtubeisdead\n",
       "6062      6  0.988957       youtuber\n",
       "\n",
       "[407 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[5, 8, 6, 7, 1, 2, 3, 10, 4, 9])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the topics\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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