LDA_Topic_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Product Categorization\n",
    "\n",
    "## Part 2: Topic modeling by LDA method\n",
    "\n",
    "The project concerns topic modeling by LDA method (Latent Dirichlet Allocation). I have used LDA method to extract the main topics from description of cosmetics from the dataset. \n",
    "\n",
    "Topic modeling is the process of using unsupervised learning techniques to extract the main topics that occur in a collection of documents. Latent Dirichlet Allocation (LDA) is an easy to use and efficient model for topic modeling. Each document is represented by the distribution of topics and each topic is represented by the distribution of words.\n",
    "\n",
    "### Dataset\n",
    "\n",
    "The dataset contains the details about makeup products such as brand, category, description, name, etc. It comes from http://makeup-api.herokuapp.com/.\n",
    "\n",
    "Attributes:\n",
    "\n",
    "* Brand (text: name of the brand)\n",
    "* Category (text: category of products)\n",
    "* Currency (text)\n",
    "* Description (text: product description)\n",
    "* Id (numeric)\n",
    "* Name (numeric: name of the product)\n",
    "* Price (numeric: product price)\n",
    "* Price sign (text)\n",
    "* Product type (text)\n",
    "* Rating (numeric)\n",
    "* Tag list (text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libriaries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "import gensim\n",
    "from gensim.models import LdaMulticore\n",
    "from gensim.models import CoherenceModel\n",
    "from gensim.corpora import Dictionary\n",
    "import gensim.corpora as corpora\n",
    "\n",
    "import pyLDAvis\n",
    "from pyLDAvis import gensim\n",
    "import pyLDAvis.sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
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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>brand</th>\n",
       "      <th>category</th>\n",
       "      <th>currency</th>\n",
       "      <th>description</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "      <th>price_sign</th>\n",
       "      <th>product_type</th>\n",
       "      <th>rating</th>\n",
       "      <th>tag_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>colourpop</td>\n",
       "      <td>pencil</td>\n",
       "      <td>CAD</td>\n",
       "      <td>Lippie Pencil A long-wearing and high-intensit...</td>\n",
       "      <td>1048</td>\n",
       "      <td>Lippie Pencil</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$</td>\n",
       "      <td>lip_liner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Vegan', 'cruelty free']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>colourpop</td>\n",
       "      <td>lipstick</td>\n",
       "      <td>CAD</td>\n",
       "      <td>Blotted Lip Sheer matte lipstick that creates ...</td>\n",
       "      <td>1047</td>\n",
       "      <td>Blotted Lip</td>\n",
       "      <td>5.5</td>\n",
       "      <td>$</td>\n",
       "      <td>lipstick</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Vegan', 'cruelty free']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>colourpop</td>\n",
       "      <td>lipstick</td>\n",
       "      <td>CAD</td>\n",
       "      <td>Lippie Stix Formula contains Vitamin E, Mango,...</td>\n",
       "      <td>1046</td>\n",
       "      <td>Lippie Stix</td>\n",
       "      <td>5.5</td>\n",
       "      <td>$</td>\n",
       "      <td>lipstick</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Vegan', 'cruelty free']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>colourpop</td>\n",
       "      <td>liquid</td>\n",
       "      <td>CAD</td>\n",
       "      <td>Developed for the Selfie Age, our buildable fu...</td>\n",
       "      <td>1045</td>\n",
       "      <td>No Filter Foundation</td>\n",
       "      <td>12.0</td>\n",
       "      <td>$</td>\n",
       "      <td>foundation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Vegan', 'cruelty free']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boosh</td>\n",
       "      <td>lipstick</td>\n",
       "      <td>CAD</td>\n",
       "      <td>All of our products are free from lead and hea...</td>\n",
       "      <td>1044</td>\n",
       "      <td>Lipstick</td>\n",
       "      <td>26.0</td>\n",
       "      <td>$</td>\n",
       "      <td>lipstick</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Organic', 'Chemical Free']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       brand  category currency  \\\n",
       "0  colourpop    pencil      CAD   \n",
       "1  colourpop  lipstick      CAD   \n",
       "2  colourpop  lipstick      CAD   \n",
       "3  colourpop    liquid      CAD   \n",
       "4      boosh  lipstick      CAD   \n",
       "\n",
       "                                         description    id  \\\n",
       "0  Lippie Pencil A long-wearing and high-intensit...  1048   \n",
       "1  Blotted Lip Sheer matte lipstick that creates ...  1047   \n",
       "2  Lippie Stix Formula contains Vitamin E, Mango,...  1046   \n",
       "3  Developed for the Selfie Age, our buildable fu...  1045   \n",
       "4  All of our products are free from lead and hea...  1044   \n",
       "\n",
       "                   name  price price_sign product_type  rating  \\\n",
       "0         Lippie Pencil    5.0          $    lip_liner     NaN   \n",
       "1           Blotted Lip    5.5          $     lipstick     NaN   \n",
       "2           Lippie Stix    5.5          $     lipstick     NaN   \n",
       "3  No Filter Foundation   12.0          $   foundation     NaN   \n",
       "4              Lipstick   26.0          $     lipstick     NaN   \n",
       "\n",
       "                       tag_list  \n",
       "0     ['Vegan', 'cruelty free']  \n",
       "1     ['Vegan', 'cruelty free']  \n",
       "2     ['Vegan', 'cruelty free']  \n",
       "3     ['Vegan', 'cruelty free']  \n",
       "4  ['Organic', 'Chemical Free']  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data\\products_final.csv', header=0,index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First observations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 931 entries, 0 to 930\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   brand         919 non-null    object \n",
      " 1   category      507 non-null    object \n",
      " 2   currency      368 non-null    object \n",
      " 3   description   906 non-null    object \n",
      " 4   id            931 non-null    int64  \n",
      " 5   name          931 non-null    object \n",
      " 6   price         917 non-null    float64\n",
      " 7   price_sign    368 non-null    object \n",
      " 8   product_type  931 non-null    object \n",
      " 9   rating        340 non-null    float64\n",
      " 10  tag_list      931 non-null    object \n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 87.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(931, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation \n",
    "\n",
    "For apply topic modeling need to pre-process the data. Firstly I converted sentences to a list of tokens or words (i.e tokenizing), then removed stopwords and lemmatizing (reduces the inflectional forms of each word into a common base or root). Finally I converted text by using the bag of words model. Bag of words is a dictionary where the keys are words (or ngrams/tokens) and values are the number of times each word occurs in the corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = df.drop(['product_type', 'currency', 'id', 'price', 'price_sign', 'rating', 'brand', 'category', 'name', 'tag_list'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lippie Pencil A long-wearing and high-intensit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Blotted Lip Sheer matte lipstick that creates ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lippie Stix Formula contains Vitamin E, Mango,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Developed for the Selfie Age, our buildable fu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>All of our products are free from lead and hea...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                         description\n",
       "0  Lippie Pencil A long-wearing and high-intensit...\n",
       "1  Blotted Lip Sheer matte lipstick that creates ...\n",
       "2  Lippie Stix Formula contains Vitamin E, Mango,...\n",
       "3  Developed for the Selfie Age, our buildable fu...\n",
       "4  All of our products are free from lead and hea..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "text['description'] = text['description'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "description    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Text preprocessing\n",
    "\n",
    "I have removed non-alphabetic characters, the stopwords and applied lemmatization for each line of text.\n",
    "\n",
    "Extracting the stopwords from nltk library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\"]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lem = WordNetLemmatizer()\n",
    "\n",
    "# Show some stop words\n",
    "stop = stopwords.words('english')\n",
    "stop[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions to clean text, remove stopwords and apply lemmatizing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(words: str) -> str:\n",
    "    \"\"\"Clean text\"\"\"\n",
    "    words = re.sub(\"[^a-zA-Z]\",\" \", words)\n",
    "    text = words.lower().split()\n",
    "    return \" \".join(text)\n",
    "\n",
    "def remove_stopwords(text: str) -> str:\n",
    "    text = [word.lower() for word in text.split() if word.lower() not in stop]\n",
    "    return \" \".join(text)\n",
    "\n",
    "def word_lem(text: str) -> str:\n",
    "    lem_text = [lem.lemmatize(word) for word in text.split()]\n",
    "    return \" \".join(lem_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "text['description'] = text['description'].apply(clean_text)\n",
    "text['description'] = text['description'].apply(remove_stopwords)\n",
    "text['description'] = text['description'].apply(word_lem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lippie pencil long wearing high intensity lip ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blotted lip sheer matte lipstick creates perfe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lippie stix formula contains vitamin e mango a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>developed selfie age buildable full coverage n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>product free lead heavy metal parabens phthala...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         description\n",
       "0  lippie pencil long wearing high intensity lip ...\n",
       "1  blotted lip sheer matte lipstick creates perfe...\n",
       "2  lippie stix formula contains vitamin e mango a...\n",
       "3  developed selfie age buildable full coverage n...\n",
       "4  product free lead heavy metal parabens phthala..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "text.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'blotted lip sheer matte lipstick creates perfect popsicle pout formula lightweight matte buildable light medium coverage'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text['description'][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic modeling - LDA\n",
    "On the start I have created the list of list format of the custom corpus. I used for it gensim library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = [row.split() for row in text['description']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['lippie',\n",
       "  'pencil',\n",
       "  'long',\n",
       "  'wearing',\n",
       "  'high',\n",
       "  'intensity',\n",
       "  'lip',\n",
       "  'pencil',\n",
       "  'glide',\n",
       "  'easily',\n",
       "  'prevents',\n",
       "  'feathering',\n",
       "  'many',\n",
       "  'lippie',\n",
       "  'stix',\n",
       "  'coordinating',\n",
       "  'lippie',\n",
       "  'pencil',\n",
       "  'designed',\n",
       "  'compliment',\n",
       "  'perfectly',\n",
       "  'feel',\n",
       "  'free',\n",
       "  'mix',\n",
       "  'match']]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I created the bag of words model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic = corpora.Dictionary(corpus)\n",
    "\n",
    "bow_corpus = [dic.doc2bow(doc) for doc in corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 3), (12, 1), (13, 1), (14, 1), (15, 1), (16, 3), (17, 1), (18, 1), (19, 1), (20, 1)]]\n"
     ]
    }
   ],
   "source": [
    "# View\n",
    "print(bow_corpus[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'compliment'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Human readable format of corpus (term-frequency):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('compliment', 1),\n",
       "  ('coordinating', 1),\n",
       "  ('designed', 1),\n",
       "  ('easily', 1),\n",
       "  ('feathering', 1),\n",
       "  ('feel', 1),\n",
       "  ('free', 1),\n",
       "  ('glide', 1),\n",
       "  ('high', 1),\n",
       "  ('intensity', 1),\n",
       "  ('lip', 1),\n",
       "  ('lippie', 3),\n",
       "  ('long', 1),\n",
       "  ('many', 1),\n",
       "  ('match', 1),\n",
       "  ('mix', 1),\n",
       "  ('pencil', 3),\n",
       "  ('perfectly', 1),\n",
       "  ('prevents', 1),\n",
       "  ('stix', 1),\n",
       "  ('wearing', 1)]]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[(dic[id], freq) for id, freq in cp] for cp in bow_corpus[:1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the Topic Model\n",
    "First I chosed 20 topics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_model = LdaMulticore(bow_corpus, num_topics = 20, id2word = dic, passes = 10, workers = 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.028*\"skin\" + 0.019*\"shade\" + 0.017*\"oil\" + 0.016*\"coverage\" + 0.013*\"makeup\" + 0.013*\"natural\" + 0.012*\"free\" + 0.011*\"foundation\" + 0.011*\"formula\" + 0.010*\"look\"'),\n",
       " (1,\n",
       "  '0.065*\"lip\" + 0.021*\"lipstick\" + 0.019*\"colour\" + 0.016*\"liner\" + 0.016*\"look\" + 0.015*\"eye\" + 0.014*\"shade\" + 0.011*\"formula\" + 0.011*\"color\" + 0.010*\"smooth\"'),\n",
       " (2,\n",
       "  '0.048*\"nail\" + 0.020*\"gel\" + 0.019*\"coat\" + 0.018*\"shine\" + 0.015*\"polish\" + 0.015*\"top\" + 0.015*\"colour\" + 0.013*\"l\" + 0.010*\"one\" + 0.009*\"first\"'),\n",
       " (3,\n",
       "  '0.014*\"free\" + 0.013*\"lash\" + 0.012*\"eye\" + 0.012*\"ingredient\" + 0.011*\"alcohol\" + 0.010*\"formula\" + 0.008*\"mascara\" + 0.008*\"line\" + 0.008*\"copolymer\" + 0.007*\"acrylate\"'),\n",
       " (4,\n",
       "  '0.022*\"shade\" + 0.018*\"pencil\" + 0.013*\"free\" + 0.012*\"brow\" + 0.011*\"long\" + 0.010*\"glow\" + 0.008*\"powder\" + 0.008*\"feature\" + 0.008*\"healthy\" + 0.008*\"use\"'),\n",
       " (5,\n",
       "  '0.017*\"blush\" + 0.015*\"ci\" + 0.011*\"look\" + 0.010*\"skin\" + 0.010*\"powder\" + 0.010*\"feature\" + 0.010*\"shade\" + 0.009*\"use\" + 0.009*\"glow\" + 0.009*\"brush\"'),\n",
       " (6,\n",
       "  '0.050*\"lash\" + 0.019*\"mascara\" + 0.017*\"brush\" + 0.012*\"ingredient\" + 0.011*\"volume\" + 0.011*\"carnauba\" + 0.009*\"natural\" + 0.008*\"eye\" + 0.007*\"brow\" + 0.007*\"acid\"'),\n",
       " (7,\n",
       "  '0.026*\"skin\" + 0.021*\"formula\" + 0.018*\"finish\" + 0.017*\"foundation\" + 0.013*\"flawless\" + 0.013*\"free\" + 0.012*\"natural\" + 0.011*\"dior\" + 0.010*\"matte\" + 0.010*\"tone\"'),\n",
       " (8,\n",
       "  '0.015*\"shade\" + 0.013*\"nude\" + 0.013*\"skin\" + 0.010*\"imperfection\" + 0.009*\"dior\" + 0.009*\"matte\" + 0.008*\"eye\" + 0.008*\"tone\" + 0.007*\"four\" + 0.007*\"circle\"'),\n",
       " (9,\n",
       "  '0.020*\"ci\" + 0.015*\"ingredient\" + 0.012*\"extract\" + 0.011*\"oxide\" + 0.011*\"oil\" + 0.010*\"sodium\" + 0.010*\"dimethicone\" + 0.009*\"iron\" + 0.009*\"titanium\" + 0.009*\"glycol\"'),\n",
       " (10,\n",
       "  '0.024*\"oil\" + 0.019*\"extract\" + 0.015*\"color\" + 0.013*\"shade\" + 0.012*\"ci\" + 0.012*\"jojoba\" + 0.010*\"natural\" + 0.010*\"organic\" + 0.008*\"leaf\" + 0.008*\"wax\"'),\n",
       " (11,\n",
       "  '0.025*\"lip\" + 0.020*\"color\" + 0.011*\"colour\" + 0.010*\"glide\" + 0.009*\"soft\" + 0.009*\"lash\" + 0.008*\"look\" + 0.008*\"new\" + 0.008*\"natural\" + 0.008*\"upper\"'),\n",
       " (12,\n",
       "  '0.027*\"brow\" + 0.022*\"colour\" + 0.012*\"brush\" + 0.011*\"look\" + 0.011*\"kit\" + 0.011*\"eye\" + 0.010*\"lip\" + 0.010*\"make\" + 0.010*\"cheek\" + 0.010*\"powder\"'),\n",
       " (13,\n",
       "  '0.024*\"li\" + 0.015*\"nail\" + 0.013*\"lip\" + 0.013*\"color\" + 0.012*\"shade\" + 0.011*\"lipstick\" + 0.010*\"polish\" + 0.009*\"water\" + 0.008*\"natural\" + 0.007*\"oil\"'),\n",
       " (14,\n",
       "  '0.033*\"skin\" + 0.017*\"natural\" + 0.015*\"powder\" + 0.015*\"shade\" + 0.014*\"color\" + 0.013*\"free\" + 0.012*\"glow\" + 0.010*\"formula\" + 0.010*\"tone\" + 0.010*\"look\"'),\n",
       " (15,\n",
       "  '0.031*\"eye\" + 0.020*\"eyeliner\" + 0.018*\"line\" + 0.017*\"color\" + 0.015*\"tip\" + 0.015*\"formula\" + 0.011*\"long\" + 0.011*\"liner\" + 0.010*\"glide\" + 0.010*\"look\"'),\n",
       " (16,\n",
       "  '0.097*\"ci\" + 0.023*\"lake\" + 0.023*\"oxide\" + 0.020*\"ingredient\" + 0.016*\"red\" + 0.016*\"iron\" + 0.013*\"list\" + 0.010*\"time\" + 0.009*\"please\" + 0.008*\"yellow\"'),\n",
       " (17,\n",
       "  '0.028*\"oil\" + 0.017*\"seed\" + 0.016*\"extract\" + 0.014*\"oxide\" + 0.013*\"ci\" + 0.012*\"c\" + 0.012*\"ingredient\" + 0.010*\"coconut\" + 0.009*\"stearate\" + 0.009*\"natural\"'),\n",
       " (18,\n",
       "  '0.030*\"shadow\" + 0.030*\"eye\" + 0.023*\"shade\" + 0.014*\"matte\" + 0.014*\"palette\" + 0.012*\"color\" + 0.011*\"liner\" + 0.009*\"strong\" + 0.009*\"double\" + 0.009*\"step\"'),\n",
       " (19,\n",
       "  '0.030*\"oil\" + 0.023*\"lip\" + 0.016*\"seed\" + 0.013*\"pencil\" + 0.013*\"mineral\" + 0.012*\"butter\" + 0.011*\"color\" + 0.010*\"wear\" + 0.010*\"ci\" + 0.009*\"cerifera\"')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_model.print_topics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perplexity and Coherence Score\n",
    "\n",
    "In this step I computed Perplexity and Coherence Score which provide a convenient measure to judge how good a given topic model is.\n",
    "\n",
    "Compute Perplexity - lower is the better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perplexity: -7.1364033649727485\n"
     ]
    }
   ],
   "source": [
    "perplexity = lda_model.log_perplexity(bow_corpus)\n",
    "print('\\nPerplexity:', perplexity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute Coherence Score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coherence Score:  0.4478213675260944\n"
     ]
    }
   ],
   "source": [
    "coherence_lda = CoherenceModel(model=lda_model, texts=corpus, dictionary=dic, coherence='c_v')\n",
    "coherence = coherence_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model visualization\n",
    "\n",
    "For visualization of received model I have used pyLDAvis library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\pyLDAvis\\_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
    },
    {
     "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_el3292417829132844006939518937\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el3292417829132844006939518937_data = {\"mdsDat\": {\"x\": [-0.24944056409299606, -0.18762494458474435, -0.13482621253145705, 0.052617557076611615, 0.11065964632263811, -0.17855303116791144, -0.06462520234774544, 0.13456480389645983, 0.0909669061918731, 0.052965500403253804, -0.0695227964623735, 0.11497976129308288, -0.049552958768167545, 0.08776852528303664, 0.04687697896805968, 0.005491510197834413, -0.0529888373967307, 0.10747543107466283, 0.0813738508716397, 0.10139407577297373], \"y\": [-0.0165967750827245, 0.0488664132289305, -0.1349998571454359, 0.09213607639558205, -0.19439424476884112, 0.027460867099003788, 0.0403084667420864, -0.010293908631685047, 0.07191294522743424, 0.03603477110075177, -0.13282771954031664, -0.09571614280150173, 0.07745603643118536, -0.08664870098356987, -0.009617983681689497, 0.10562535105448854, 0.00947148421074951, 0.07231275338285614, 0.01703265968469054, 0.08247750807800573], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 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\"thicker\", \"thickness\", \"thickness\", \"thin\", \"thin\", \"thin\", \"thin\", \"thin\", \"thin\", \"thin\", \"thin\", \"thin\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"tin\", \"tin\", \"tin\", \"tinctorius\", \"tinctorius\", \"tinctorius\", \"tinctorius\", \"tinctorius\", \"tinted\", \"tinted\", \"tinted\", \"tinted\", \"tip\", \"tip\", \"tip\", \"tip\", \"tip\", \"tip\", \"tip\", \"tip\", \"tip\", \"tip\", \"tip\", \"tip\", \"tip\", \"tip\", \"titanium\", \"titanium\", \"titanium\", \"titanium\", \"titanium\", \"titanium\", \"titanium\", \"titanium\", \"titanium\", \"titanium\", \"titanium\", \"titanium\", \"titanium\", \"tiwst\", \"tocopherol\", \"tocopherol\", \"tocopherol\", \"tocopherol\", \"tocopherol\", \"tocopherol\", \"tocopherol\", \"tocopherol\", \"tocopheryl\", \"tocopheryl\", \"tocopheryl\", \"tocopheryl\", \"tocopheryl\", \"tocopheryl\", \"ton\", \"tone\", \"tone\", \"tone\", \"tone\", \"tone\", \"tone\", \"tone\", \"tone\", \"tone\", \"tone\", \"tone\", \"tone\", \"tone\", \"toned\", \"toned\", \"toned\", \"toned\", \"toned\", \"toned\", \"tonemimic\", \"toneuse\", \"tool\", \"tool\", \"tool\", \"tool\", \"top\", \"top\", \"top\", \"top\", \"top\", \"top\", \"top\", \"top\", \"top\", \"top\", \"top\", \"top\", \"tos\", \"touched\", \"toxic\", \"toxic\", \"toxic\", \"toxic\", \"toxic\", \"toxickit\", \"transferring\", \"transformative\", \"trehalose\", \"trend\", \"trend\", \"trend\", \"triacontanyl\", \"triacontanyl\", \"triacontanyl\", \"tributyl\", \"tributyl\", \"tricaprylin\", \"triethanolamine\", \"triethanolamine\", \"triethoxycaprylylsilane\", \"triethoxycaprylylsilane\", \"triethoxycaprylylsilane\", \"triglyceride\", \"triglyceride\", \"triglyceride\", \"triglyceride\", \"triglyceride\", \"triglyceride\", \"triglyceride\", \"triisostearate\", \"triisostearate\", \"triisostearate\", \"trimethicone\", \"trimethicone\", \"trioctyldodecyl\", \"trublend\", \"truffle\", \"trunaked\", \"trunaked\", \"tube\", \"tube\", \"tube\", \"tube\", \"tube\", \"twelve\", \"twin\", \"twisted\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"ul\", \"ultimate\", \"ultimate\", \"ultimate\", \"ultimate\", \"ultimate\", \"ultimate\", \"ultimate\", \"ultimate\", \"ultimate\", \"ultimate\", \"ultimate\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultra\", \"ultramarine\", \"ultramarine\", \"ultramarine\", \"ultramarine\", \"ultramarine\", \"ultramarine\", \"ultramarine\", \"ultramarine\", \"ultramarine\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unison\", \"universally\", \"universally\", \"universally\", \"universally\", \"unwelcome\", \"upper\", \"upper\", \"upper\", \"upper\", \"upper\", \"upper\", \"upper\", \"upper\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"usher\", \"various\", \"vary\", \"vary\", \"vary\", \"vary\", \"vegetable\", \"vegetable\", \"vegetable\", \"vegetable\", \"velvet\", \"velvet\", \"velvet\", \"velvety\", \"velvety\", \"velvety\", \"velvety\", \"velvety\", \"velvety\", \"velvety\", \"velvety\", \"velvety\", \"velvety\", \"velvety\", \"vengeance\", \"veritable\", \"vernis\", \"vernis\", \"vernis\", \"vibe\", \"villain\", \"violet\", \"violet\", \"violet\", \"violet\", \"violet\", \"violet\", \"violet\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vitamin\", \"vivacious\", \"vivid\", \"vivid\", \"vivid\", \"vivid\", \"vivid\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"voluminous\", \"voluminous\", \"vulneraria\", \"wantmedium\", \"warm\", \"warm\", \"warm\", \"warm\", \"warm\", \"warm\", \"warm\", \"warm\", \"warm\", \"warm\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"waterproof\", \"waterproof\", \"waterproof\", \"waterproof\", \"waterproof\", \"waterproof\", \"waterproof\", \"waterproof\", \"waterproof\", \"wattage\", \"wax\", \"wax\", \"wax\", \"wax\", \"wax\", \"wax\", \"wax\", \"wax\", \"wax\", \"wax\", \"wax\", \"wax\", \"wax\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wear\", \"wearer\", \"wearer\", \"wearer\", \"wearer\", \"wearerdirections\", \"wearhypoallergenicparaben\", \"wearophthalmologist\", \"weight\", \"weight\", \"weight\", \"wet\", \"wet\", \"wet\", \"wet\", \"wet\", \"wet\", \"wet\", \"wet\", \"wet\", \"whenever\", \"whole\", \"wider\", \"wiggle\", \"witch\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"wonder\", \"wonder\", \"wonder\", \"wonder\", \"workout\", \"worth\", \"wowder\", \"wrinkleseven\", \"xpression\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yellow\", \"yellow\", \"yellow\", \"yellow\", \"yellow\", \"yellow\", \"yellowing\", \"yet\", \"yet\", \"yet\", \"yet\", \"yet\", \"yet\", \"young\", \"youtube\", \"zone\", \"zone\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [17, 10, 7, 15, 16, 18, 6, 2, 1, 5, 4, 3, 11, 12, 19, 14, 20, 8, 13, 9]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el3292417829132844006939518937\", ldavis_el3292417829132844006939518937_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el3292417829132844006939518937\", ldavis_el3292417829132844006939518937_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el3292417829132844006939518937\", ldavis_el3292417829132844006939518937_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "16    -0.249441 -0.016597       1        1  22.661810\n",
       "9     -0.187625  0.048866       2        1  11.228264\n",
       "6     -0.134826 -0.135000       3        1   8.822572\n",
       "14     0.052618  0.092136       4        1   7.389819\n",
       "15     0.110660 -0.194394       5        1   6.086630\n",
       "17    -0.178553  0.027461       6        1   5.397391\n",
       "5     -0.064625  0.040308       7        1   4.794846\n",
       "1      0.134565 -0.010294       8        1   4.383132\n",
       "0      0.090967  0.071913       9        1   3.464938\n",
       "4      0.052966  0.036035      10        1   3.270230\n",
       "3     -0.069523 -0.132828      11        1   2.947644\n",
       "2      0.114980 -0.095716      12        1   2.587211\n",
       "10    -0.049553  0.077456      13        1   2.579350\n",
       "11     0.087769 -0.086649      14        1   2.393081\n",
       "18     0.046877 -0.009618      15        1   2.354388\n",
       "13     0.005492  0.105625      16        1   2.347759\n",
       "19    -0.052989  0.009471      17        1   2.264284\n",
       "7      0.107475  0.072313      18        1   2.176631\n",
       "12     0.081374  0.017033      19        1   1.878339\n",
       "8      0.101394  0.082478      20        1   0.971675, topic_info=     Category         Freq         Term        Total  loglift  logprob\n",
       "term                                                                  \n",
       "2020  Default  1649.000000           ci  1649.000000  30.0000  30.0000\n",
       "10    Default   404.000000          lip   404.000000  29.0000  29.0000\n",
       "259   Default   328.000000         lash   328.000000  28.0000  28.0000\n",
       "82    Default   417.000000         skin   417.000000  27.0000  27.0000\n",
       "77    Default   470.000000          oil   470.000000  26.0000  26.0000\n",
       "257   Default   448.000000          eye   448.000000  25.0000  25.0000\n",
       "1786  Default   154.000000         nail   154.000000  24.0000  24.0000\n",
       "130   Default   480.000000        shade   480.000000  23.0000  23.0000\n",
       "353   Default   160.000000         brow   160.000000  22.0000  22.0000\n",
       "429   Default   251.000000      extract   251.000000  21.0000  21.0000\n",
       "443   Default   364.000000         lake   364.000000  20.0000  20.0000\n",
       "57    Default   452.000000        color   452.000000  19.0000  19.0000\n",
       "404   Default   229.000000       colour   229.000000  18.0000  18.0000\n",
       "266   Default   142.000000       shadow   142.000000  17.0000  17.0000\n",
       "2039  Default   517.000000        oxide   517.000000  16.0000  16.0000\n",
       "525   Default   152.000000         seed   152.000000  15.0000  15.0000\n",
       "25    Default   369.000000      formula   369.000000  14.0000  14.0000\n",
       "68    Default   134.000000   foundation   134.000000  13.0000  13.0000\n",
       "489   Default   126.000000      mascara   126.000000  12.0000  12.0000\n",
       "359   Default   182.000000        liner   182.000000  11.0000  11.0000\n",
       "75    Default   352.000000      natural   352.000000  10.0000  10.0000\n",
       "29    Default   231.000000        matte   231.000000   9.0000   9.0000\n",
       "28    Default   137.000000     lipstick   137.000000   8.0000   8.0000\n",
       "6     Default   296.000000         free   296.000000   7.0000   7.0000\n",
       "2032  Default   385.000000         iron   385.000000   6.0000   6.0000\n",
       "16    Default   122.000000       pencil   122.000000   5.0000   5.0000\n",
       "120   Default   348.000000         look   348.000000   4.0000   4.0000\n",
       "550   Default   143.000000         glow   143.000000   3.0000   3.0000\n",
       "296   Default   130.000000        blush   130.000000   2.0000   2.0000\n",
       "469   Default   277.000000        brush   277.000000   1.0000   1.0000\n",
       "...       ...          ...          ...          ...      ...      ...\n",
       "468   Topic20     2.884680         away    16.294653   2.9025  -5.3239\n",
       "1911  Topic20     1.449317     diorskin     5.160435   3.3640  -6.0123\n",
       "1214  Topic20     3.635799      neutral    28.564714   2.5726  -5.0925\n",
       "1129  Topic20     1.389851     original     5.049348   3.3438  -6.0542\n",
       "439   Topic20     2.091802    including    11.467085   2.9324  -5.6453\n",
       "1909  Topic20     1.384688      breathe     5.157844   3.3189  -6.0579\n",
       "901   Topic20     1.474536  universally     5.954711   3.2381  -5.9950\n",
       "369   Topic20     1.429108      colored     6.074261   3.1869  -6.0263\n",
       "666   Topic20     2.857715    concealer    27.811817   2.3585  -5.3333\n",
       "541   Topic20     3.516300      created    44.384029   2.0984  -5.1260\n",
       "130   Topic20     9.133621        shade   480.659790   0.6707  -4.1714\n",
       "127   Topic20     4.131299        range    77.118126   1.7072  -4.9648\n",
       "82    Topic20     7.505239         skin   417.193726   0.6160  -4.3678\n",
       "400   Topic20     4.541875         tone   124.083282   1.3263  -4.8700\n",
       "234   Topic20     3.345669          set    51.493378   1.9001  -5.1757\n",
       "73    Topic20     3.938594      looking    86.447929   1.5452  -5.0125\n",
       "172   Topic20     2.671282     radiance    26.822023   2.3272  -5.4008\n",
       "29    Topic20     5.081303        matte   231.968933   0.8129  -4.7578\n",
       "114   Topic20     3.494914    available    69.891838   1.6383  -5.1321\n",
       "504   Topic20     2.881036      instant    37.776211   2.0604  -5.3252\n",
       "550   Topic20     3.842669         glow   143.894699   1.0110  -5.0372\n",
       "257   Topic20     4.926632          eye   448.666138   0.1223  -4.7887\n",
       "1052  Topic20     2.889165       beauty    45.803593   1.8705  -5.3224\n",
       "799   Topic20     2.849771          pro    48.692024   1.7956  -5.3361\n",
       "668   Topic20     2.493362         dark    33.680084   2.0306  -5.4697\n",
       "25    Topic20     3.458944      formula   369.954529  -0.0385  -5.1424\n",
       "144   Topic20     2.964913       smooth   161.361313   0.6371  -5.2965\n",
       "278   Topic20     2.804492         even   125.177811   0.8354  -5.3521\n",
       "31    Topic20     2.556898      perfect   159.565750   0.5002  -5.4446\n",
       "75    Topic20     2.539998      natural   352.833801  -0.2999  -5.4512\n",
       "\n",
       "[1456 rows x 6 columns], token_table=      Topic      Freq          Term\n",
       "term                               \n",
       "3505     12  0.853218           abc\n",
       "2481      3  0.872298        acacia\n",
       "289       3  0.727951          acai\n",
       "289      11  0.121325          acai\n",
       "1779      4  0.191171   accentuates\n",
       "1779     17  0.573514   accentuates\n",
       "3206      7  0.731665        access\n",
       "1908      8  0.703803     accessory\n",
       "1316     20  0.654001   accommodate\n",
       "1343     18  0.577339  accommodates\n",
       "147      15  0.755993     according\n",
       "2255      1  0.443638       acetate\n",
       "2255      2  0.260396       acetate\n",
       "2255      3  0.048222       acetate\n",
       "2255      4  0.009644       acetate\n",
       "2255      6  0.106087       acetate\n",
       "2255      7  0.038577       acetate\n",
       "2255     10  0.009644       acetate\n",
       "2255     11  0.028933       acetate\n",
       "2255     12  0.019289       acetate\n",
       "2255     15  0.009644       acetate\n",
       "2255     18  0.009644       acetate\n",
       "2089      1  0.235506          acid\n",
       "2089      2  0.350239          acid\n",
       "2089      3  0.223428          acid\n",
       "2089      4  0.006039          acid\n",
       "2089      5  0.012077          acid\n",
       "2089      6  0.036232          acid\n",
       "2089      7  0.042270          acid\n",
       "2089      9  0.006039          acid\n",
       "...     ...       ...           ...\n",
       "1302      2  0.279650        wonder\n",
       "1302     14  0.186433        wonder\n",
       "1302     17  0.279650        wonder\n",
       "1741     19  0.793039       workout\n",
       "865      18  0.776477         worth\n",
       "732       4  0.872869        wowder\n",
       "3448     20  0.621128  wrinkleseven\n",
       "4671      6  0.722963     xpression\n",
       "1235      1  0.333293          year\n",
       "1235      4  0.055549          year\n",
       "1235      9  0.055549          year\n",
       "1235     10  0.499940          year\n",
       "1235     19  0.055549          year\n",
       "391       1  0.843544        yellow\n",
       "391       2  0.050904        yellow\n",
       "391       3  0.007272        yellow\n",
       "391       4  0.014544        yellow\n",
       "391       6  0.014544        yellow\n",
       "391       7  0.058175        yellow\n",
       "4446      4  0.731224     yellowing\n",
       "804       2  0.146909           yet\n",
       "804       3  0.110182           yet\n",
       "804       8  0.404001           yet\n",
       "804       9  0.183637           yet\n",
       "804      14  0.036727           yet\n",
       "804      18  0.036727           yet\n",
       "595      17  0.695447         young\n",
       "2754     12  0.757683       youtube\n",
       "1132     10  0.697011          zone\n",
       "1132     16  0.232337          zone\n",
       "\n",
       "[4507 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[17, 10, 7, 15, 16, 18, 6, 2, 1, 5, 4, 3, 11, 12, 19, 14, 20, 8, 13, 9])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "\n",
    "lda_vis = pyLDAvis.gensim.prepare(lda_model, bow_corpus, dic)\n",
    "lda_vis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of the LDA model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"lda_gensim.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A good topic model will have fairly big, non-overlapping bubbles scattered throughout the chart instead of being clustered in one quadrant.\n",
    "In this case is not a good enough topic model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA with Scikit-Learn\n",
    "\n",
    "In this approach I have used Scikit-Learn library to visualize the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer(analyzer='word',       \n",
    "                             min_df=3,                       \n",
    "                             stop_words='english',             \n",
    "                             lowercase=True,                   \n",
    "                             token_pattern='[a-zA-Z0-9]{3,}',  \n",
    "                             max_features=5000,          \n",
    "                            )\n",
    "\n",
    "vectorized = vectorizer.fit_transform(text['description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lda = LatentDirichletAllocation(n_components=20, # Number of topics\n",
    "                                      learning_method='online',\n",
    "                                      random_state=0,       \n",
    "                                      n_jobs = -1 \n",
    "                                     )\n",
    "\n",
    "lda_output = model_lda.fit_transform(vectorized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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_el3292417829141109521403616573\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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      "text/plain": [
       "PreparedData(topic_coordinates=                x          y  topics  cluster       Freq\n",
       "topic                                                   \n",
       "5       42.131062 -78.749969       1        1  15.493109\n",
       "7       -9.934859 -89.483253       2        1  14.509088\n",
       "6        6.507674 -41.602722       3        1  12.239941\n",
       "16     -29.774748  46.241222       4        1  10.083952\n",
       "4       74.004898  61.442486       5        1   8.443662\n",
       "13     -50.668072   6.508484       6        1   8.441146\n",
       "8       85.439751 -45.613400       7        1   7.997524\n",
       "14      41.855190  23.003712       8        1   6.627451\n",
       "17      12.901948  54.534599       9        1   5.515937\n",
       "9      -67.115273  92.572136      10        1   3.726297\n",
       "0      -60.571545 -78.992073      11        1   2.444179\n",
       "15     -17.020338  96.351402      12        1   1.404671\n",
       "12      91.827042   9.675216      13        1   1.302956\n",
       "19      43.782440 -20.290585      14        1   0.527856\n",
       "2       -2.770097   5.427330      15        1   0.348045\n",
       "3      -79.530136  45.705872      16        1   0.341974\n",
       "10     -85.697350 -34.795536      17        1   0.227230\n",
       "18    -110.059319   8.055429      18        1   0.125703\n",
       "11      35.379772  98.571175      19        1   0.113670\n",
       "1      -35.439091 -34.610035      20        1   0.085610, topic_info=     Category        Freq               Term       Total  loglift  logprob\n",
       "term                                                                      \n",
       "1010  Default  327.000000               lash  327.000000  30.0000  30.0000\n",
       "1006  Default  378.000000               lake  378.000000  29.0000  29.0000\n",
       "1514  Default  298.000000                red  298.000000  28.0000  28.0000\n",
       "1201  Default  168.000000               nail  168.000000  27.0000  27.0000\n",
       "939   Default  572.000000         ingredient  572.000000  26.0000  26.0000\n",
       "1060  Default  397.000000                lip  397.000000  25.0000  25.0000\n",
       "1206  Default  402.000000            natural  402.000000  24.0000  24.0000\n",
       "237   Default  199.000000               brow  199.000000  23.0000  23.0000\n",
       "1613  Default  491.000000              shade  491.000000  22.0000  22.0000\n",
       "644   Default  272.000000            extract  272.000000  21.0000  21.0000\n",
       "1283  Default  526.000000              oxide  526.000000  20.0000  20.0000\n",
       "1129  Default  237.000000              matte  237.000000  19.0000  19.0000\n",
       "1269  Default  103.000000            organic  103.000000  18.0000  18.0000\n",
       "182   Default  143.000000              blend  143.000000  17.0000  17.0000\n",
       "1984  Default  230.000000                wax  230.000000  16.0000  16.0000\n",
       "1333  Default  126.000000             pencil  126.000000  15.0000  15.0000\n",
       "1122  Default  119.000000            mascara  119.000000  14.0000  14.0000\n",
       "1660  Default  406.000000               skin  406.000000  13.0000  13.0000\n",
       "239   Default  302.000000              brush  302.000000  12.0000  12.0000\n",
       "743   Default  345.000000               free  345.000000  11.0000  11.0000\n",
       "695   Default  200.000000             finish  200.000000  10.0000  10.0000\n",
       "353   Default  458.000000              color  458.000000   9.0000   9.0000\n",
       "1069  Default  244.000000               list  244.000000   8.0000   8.0000\n",
       "357   Default  230.000000             colour  230.000000   7.0000   7.0000\n",
       "649   Default  481.000000                eye  481.000000   6.0000   6.0000\n",
       "790   Default  151.000000               glow  151.000000   5.0000   5.0000\n",
       "281   Default  153.000000           carnauba  153.000000   4.0000   4.0000\n",
       "960   Default  401.000000               iron  401.000000   3.0000   3.0000\n",
       "294   Default  123.000000           cerifera  123.000000   2.0000   2.0000\n",
       "653   Default  104.000000           eyeliner  104.000000   1.0000   1.0000\n",
       "...       ...         ...                ...         ...      ...      ...\n",
       "1863  Topic20    0.022327            topcoat    9.514598   1.0083  -7.5855\n",
       "1973  Topic20    0.022178               wait    7.585198   1.2283  -7.5921\n",
       "2028  Topic20    0.021902               zone    4.440934   1.7511  -7.6047\n",
       "23    Topic20    0.021845  actioningredients    3.914250   1.8747  -7.6073\n",
       "1386  Topic20    0.021820            pointed    3.722433   1.9238  -7.6084\n",
       "825   Topic20    0.021803              grown    3.616692   1.9518  -7.6092\n",
       "649   Topic20    0.023594                eye  481.426955  -2.8604  -7.5303\n",
       "1067  Topic20    0.022958             liquid   93.772600  -1.2518  -7.5576\n",
       "1780  Topic20    0.022776             subtle   47.641743  -0.5826  -7.5656\n",
       "1505  Topic20    0.022374               real   14.535446   0.5867  -7.5834\n",
       "512   Topic20    0.022518            desired   32.493456  -0.2113  -7.5769\n",
       "1615  Topic20    0.022758             shadow  143.981811  -1.6894  -7.5663\n",
       "992   Topic20    0.022098               kind    8.092619   1.1599  -7.5958\n",
       "1136  Topic20    0.022062               mean    7.688935   1.2094  -7.5974\n",
       "839   Topic20    0.022027              hazel    6.570295   1.3651  -7.5990\n",
       "60    Topic20    0.022013             almond    6.276029   1.4102  -7.5997\n",
       "504   Topic20    0.022104              depth   10.761109   0.8752  -7.5955\n",
       "1206  Topic20    0.022919            natural  402.647833  -2.7107  -7.5593\n",
       "1061  Topic20    0.022103           lipcolor   11.806202   0.7825  -7.5955\n",
       "1027  Topic20    0.022013               left    8.648202   1.0897  -7.5996\n",
       "1339  Topic20    0.022334            perfect  163.286164  -1.8340  -7.5851\n",
       "560   Topic20    0.022285                dry   83.688746  -1.1678  -7.5873\n",
       "1080  Topic20    0.022213               look  380.396873  -2.6851  -7.5906\n",
       "1602  Topic20    0.022155          sensitive   88.875382  -1.2338  -7.5932\n",
       "566   Topic20    0.022130                duo   15.167955   0.5331  -7.5943\n",
       "317   Topic20    0.022071             choose   18.008847   0.3588  -7.5970\n",
       "185   Topic20    0.022055           blending   18.086479   0.3537  -7.5977\n",
       "1986  Topic20    0.022046               wear  156.863139  -1.8069  -7.5982\n",
       "233   Topic20    0.022014            bronzed   12.639008   0.7102  -7.5996\n",
       "1846  Topic20    0.021988               time  237.307164  -2.2235  -7.6008\n",
       "\n",
       "[1162 rows x 6 columns], token_table=      Topic      Freq               Term\n",
       "term                                    \n",
       "0         5  0.828607            abeille\n",
       "0         8  0.097483            abeille\n",
       "3         7  0.886136           absolute\n",
       "5         1  0.505540          absorbing\n",
       "5        16  0.252770          absorbing\n",
       "7         5  0.891224             acacia\n",
       "17        1  0.111484               acid\n",
       "17        4  0.275775               acid\n",
       "17        5  0.492875               acid\n",
       "17        8  0.023470               acid\n",
       "17        9  0.088013               acid\n",
       "17       13  0.005868               acid\n",
       "18        4  0.133898           acrylate\n",
       "18        5  0.765133           acrylate\n",
       "18        8  0.076513           acrylate\n",
       "21        2  0.657280                act\n",
       "21       15  0.219093                act\n",
       "23        9  0.766430  actioningredients\n",
       "24        1  0.212743             active\n",
       "24        2  0.070914             active\n",
       "24        4  0.567315             active\n",
       "24        8  0.070914             active\n",
       "24       16  0.070914             active\n",
       "32        6  0.404938            adheres\n",
       "32       14  0.404938            adheres\n",
       "35       11  0.926212             adipic\n",
       "43       11  0.890483              agent\n",
       "46        1  0.936776                air\n",
       "50        4  0.074829               alba\n",
       "50        5  0.848066               alba\n",
       "...     ...       ...                ...\n",
       "2000     17  0.070308              white\n",
       "2003     10  0.913639             wiggle\n",
       "2006      5  0.378393             winged\n",
       "2006     10  0.378393             winged\n",
       "2007      8  0.132516            winning\n",
       "2007      9  0.132516            winning\n",
       "2007     12  0.662581            winning\n",
       "2009      2  0.927231               wipe\n",
       "2017      1  0.084521                wow\n",
       "2017      6  0.845208                wow\n",
       "2020      4  0.821311            xanthan\n",
       "2020      5  0.156440            xanthan\n",
       "2021      2  0.050322               year\n",
       "2021      9  0.905793               year\n",
       "2022      1  0.056674             yellow\n",
       "2022      4  0.042506             yellow\n",
       "2022      5  0.028337             yellow\n",
       "2022      6  0.184192             yellow\n",
       "2022      7  0.014169             yellow\n",
       "2022      8  0.630502             yellow\n",
       "2022      9  0.028337             yellow\n",
       "2025      1  0.072987                zea\n",
       "2025      4  0.072987                zea\n",
       "2025      9  0.802856                zea\n",
       "2027      1  0.250233               zinc\n",
       "2027      4  0.368765               zinc\n",
       "2027      6  0.039511               zinc\n",
       "2027      9  0.329255               zinc\n",
       "2028      2  0.225178               zone\n",
       "2028     10  0.450356               zone\n",
       "\n",
       "[2403 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[6, 8, 7, 17, 5, 14, 9, 15, 18, 10, 1, 16, 13, 20, 3, 4, 11, 19, 12, 2])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "pyLDAvis.sklearn.prepare(model_lda, vectorized, vectorizer, mds='tsne')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of LDA model by scikit-learn library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"lda_sklearn.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By the using this method I have successfully created a good looking topic model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this project I used LDA method to extract the main topics from description of cosmetics products from the dataset. Before I have started topic modeling I had to pre-process the data. I have applied process in which sentences are converted to a list of tokens or words (i.e tokenizing), removed stopwords and applied lemmatizing (reduces the inflectional forms of each word into a common base or root). Finally I converted text by using the bag of words model. Bag of words is a dictionary where the keys are words (or ngrams/tokens) and values are the number of times each word occurs in the corpus. \n",
    "I have built a topic model using Gensim’s LDA and visualized the topics using pyLDAvis library. Then I created LDA implementation by Scikit-learn and visualized it. By the using first method I have received a not good enough topic model. I decided to applied second method by using Scikit-learn library. Thank's it I got a good looking topic model visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}