04B_Modeling_defined.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda install -c conda-forge pyldavis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in terminal\n",
    "# python -m spacy download en_core_web_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !conda install gensim -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "import pickle\n",
    "from emoji import UNICODE_EMOJI\n",
    "import pyLDAvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pickle.load(open(\"Data/clean_df.pickle\",\"rb\"))\n",
    "spaced_notes = pickle.load(open(\"Data/spaced_notes.pickle\",\"rb\"))\n",
    "demoji_notes = pickle.load(open(\"Data/demoji_notes.pickle\",\"rb\"))\n",
    "defined_notes = pickle.load(open(\"Data/defined_notes.pickle\",\"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a smaller set of notes to practice preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer \n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.decomposition import NMF\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import re\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_topics(model, feature_names, no_top_words, topic_names=None):\n",
    "    for ix, topic in enumerate(model.components_):\n",
    "        if not topic_names or not topic_names[ix]:\n",
    "            print(\"\\nTopic \", ix)\n",
    "        else:\n",
    "            print(\"\\nTopic: '\",topic_names[ix],\"'\")\n",
    "        print(\", \".join(feature_names[i] + ' ' + f'{topic[i]:9.2f}'\n",
    "                        for i in topic.argsort()[:-no_top_words - 1:-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## defined_notes\n",
    "### generate document-term matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://stackoverflow.com/questions/57333183/sk-learn-countvectorizer-keeping-emojis-as-words\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "s= ['👋 Welcome', '👋 Welcome']\n",
    "\n",
    "v = TfidfVectorizer(token_pattern=r'[^\\s]+')\n",
    "v.fit(s)\n",
    "v.get_feature_names()\n",
    "\n",
    "['welcome', '👋']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(286464, 756)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(token_pattern=r'[^\\s]+',stop_words='english',min_df=200)\n",
    "dtm = vectorizer.fit_transform(defined_notes)\n",
    "dtm.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have to convert `.toarray()` because the vectorizer returns a sparse matrix.\n",
    "# For a big corpus, we would skip the dataframe and keep the output sparse.\n",
    "dtm_lsa = pd.DataFrame(dtm.toarray(), columns=vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['3', 'admission', 'airbnb', 'airplane', 'alien', 'amazon', 'american',\n",
       "       'apple', 'april', 'arrow',\n",
       "       ...\n",
       "       '🥟', '🥤', '🥦', '🥩', '🥪', '🥯', '🥰', '🥳', '🦄', '🧀'],\n",
       "      dtype='object', length=756)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtm_lsa.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Latent Semantic Analysis (LSA) is just another name for Singular Value Decomposition (SVD) applied to Natural Language Processing (NLP)\n",
    "\n",
    "lsa = TruncatedSVD(150)\n",
    "doc_topic = lsa.fit(dtm[:100000])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8274167096138046"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(lsa.explained_variance_ratio_[0:150])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/lsa_defined_100k.pickle', 'wb') as f:\n",
    "    pickle.dump(doc_topic, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9220316310>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lsa.explained_variance_ratio_[0:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic  0\n",
      "skin      0.49, tone      0.49, light      0.29\n",
      "\n",
      "Topic  1\n",
      "hot      0.49, beverage      0.48, ☕      0.48\n",
      "\n",
      "Topic  2\n",
      "symbol      0.47, letter      0.47, regional      0.47\n",
      "\n",
      "Topic  3\n",
      "face      0.75, pizza      0.35, 🍕      0.34\n",
      "\n",
      "Topic  4\n",
      "pizza      0.63, 🍕      0.60, beverage      0.04\n",
      "\n",
      "Topic  5\n",
      "money      0.57, wings      0.48, 💸      0.48\n",
      "\n",
      "Topic  6\n",
      "cooking      0.33, 🍳      0.32, man      0.32\n",
      "\n",
      "Topic  7\n",
      "heart      0.65, red      0.47, ❤      0.45\n",
      "\n",
      "Topic  8\n",
      "football      0.61, american      0.56, 🏈      0.56\n",
      "\n",
      "Topic  9\n",
      "hamburger      0.70, 🍔      0.70, beer      0.07\n",
      "\n",
      "Topic  10\n",
      "party      0.49, 🎉      0.46, popper      0.46\n",
      "\n",
      "Topic  11\n",
      "taco      0.54, 🌮      0.52, burrito      0.33\n",
      "\n",
      "Topic  12\n",
      "sign      0.37, ‍      0.32, person      0.31\n",
      "\n",
      "Topic  13\n",
      "party      0.25, 🎉      0.23, popper      0.23\n",
      "\n",
      "Topic  14\n",
      "sushi      0.71, 🍣      0.70, rice      0.03\n",
      "\n",
      "Topic  15\n",
      "burrito      0.60, 🌯      0.59, beer      0.06\n",
      "\n",
      "Topic  16\n",
      "sweat      0.36, 💦      0.36, droplets      0.36\n",
      "\n",
      "Topic  17\n",
      "food      0.90, glass      0.16, wine      0.11\n",
      "\n",
      "Topic  18\n",
      "food      0.32, high      0.23, voltage      0.23\n",
      "\n",
      "Topic  19\n",
      "glass      0.52, wine      0.37, 🍷      0.36\n",
      "\n",
      "Topic  20\n",
      "🏻      0.38, light      0.22, sign      0.15\n",
      "\n",
      "Topic  21\n",
      "dollar      0.43, banknote      0.42, 💵      0.40\n",
      "\n",
      "Topic  22\n",
      "fuel      0.42, pump      0.42, ⛽      0.42\n",
      "\n",
      "Topic  23\n",
      "smiling      0.70, eyes      0.38, 😊      0.13\n",
      "\n",
      "Topic  24\n",
      "eggplant      0.38, 🍆      0.38, ️      0.28\n",
      "\n",
      "Topic  25\n",
      "money      0.24, person      0.14, medium      0.14\n",
      "\n",
      "Topic  26\n",
      "knife      0.54, fork      0.51, 🍴      0.41\n",
      "\n",
      "Topic  27\n",
      "gift      0.57, 🎁      0.46, wrapped      0.46\n",
      "\n",
      "Topic  28\n",
      "bottle      0.37, popping      0.34, cork      0.34\n",
      "\n",
      "Topic  29\n",
      "house      0.19, eggplant      0.17, 🍆      0.17\n",
      "\n",
      "Topic  30\n",
      "clinking      0.39, mugs      0.36, 🍻      0.36\n",
      "\n",
      "Topic  31\n",
      "basketball      0.67, 🏀      0.66, mug      0.06\n",
      "\n",
      "Topic  32\n",
      "flag      0.43, hole      0.42, ⛳      0.41\n",
      "\n",
      "Topic  33\n",
      "leg      0.35, poultry      0.35, 🍗      0.35\n",
      "\n",
      "Topic  34\n",
      "drink      0.44, tropical      0.43, 🍹      0.42\n",
      "\n",
      "Topic  35\n",
      "kiss      0.53, blowing      0.50, 😘      0.50\n",
      "\n",
      "Topic  36\n",
      "sandwich      0.65, 🥪      0.61, bag      0.20\n",
      "\n",
      "Topic  37\n",
      "sandwich      0.31, 🥪      0.29, wings      0.26\n",
      "\n",
      "Topic  38\n",
      "dancing      0.32, woman      0.26, man      0.23\n",
      "\n",
      "Topic  39\n",
      "hands      0.58, 🙌      0.27, raising      0.27\n",
      "\n",
      "Topic  40\n",
      "sign      0.26, dancing      0.25, horns      0.25\n",
      "\n",
      "Topic  41\n",
      "tickets      0.57, admission      0.47, 🎟      0.47\n",
      "\n",
      "Topic  42\n",
      "poo      0.42, pile      0.42, 💩      0.42\n",
      "\n",
      "Topic  43\n",
      "cookie      0.39, 🍪      0.38, dancing      0.26\n",
      "\n",
      "Topic  44\n",
      "automobile      0.63, 🚗      0.51, car      0.22\n",
      "\n",
      "Topic  45\n",
      "cookie      0.39, 🍪      0.37, sign      0.24\n",
      "\n",
      "Topic  46\n",
      "phone      0.51, mobile      0.37, 📱      0.34\n",
      "\n",
      "Topic  47\n",
      "t      0.56, shirt      0.50, 👕      0.44\n",
      "\n",
      "Topic  48\n",
      "dog      0.76, 🐶      0.40, t      0.17\n",
      "\n",
      "Topic  49\n",
      "bowl      0.34, 🍜      0.30, steaming      0.30\n",
      "\n",
      "Topic  50\n",
      "rent      0.77, bowl      0.33, 🍜      0.30\n",
      "\n",
      "Topic  51\n",
      "bowl      0.32, steaming      0.28, 🍜      0.28\n",
      "\n",
      "Topic  52\n",
      "bowl      0.14, tree      0.13, steaming      0.12\n",
      "\n",
      "Topic  53\n",
      "🔥      0.62, ball      0.25, electric      0.22\n",
      "\n",
      "Topic  54\n",
      "female      0.36, ♀      0.35, sign      0.18\n",
      "\n",
      "Topic  55\n",
      "ball      0.45, soccer      0.33, ⚽      0.31\n",
      "\n",
      "Topic  56\n",
      "heart      0.43, eyes      0.20, female      0.18\n",
      "\n",
      "Topic  57\n",
      "tree      0.41, ball      0.39, soccer      0.29\n",
      "\n",
      "Topic  58\n",
      "bread      0.49, 🍞      0.38, meat      0.28\n",
      "\n",
      "Topic  59\n",
      "fries      0.40, french      0.40, 🍟      0.38\n",
      "\n",
      "Topic  60\n",
      "airplane      0.32, car      0.31, ✈      0.25\n",
      "\n",
      "Topic  61\n",
      "o      0.41, jack      0.40, lantern      0.39\n",
      "\n",
      "Topic  62\n",
      "eyes      0.22, airplane      0.18, car      0.18\n",
      "\n",
      "Topic  63\n",
      "hearts      0.47, 🥰      0.28, 3      0.28\n",
      "\n",
      "Topic  64\n",
      "ice      0.45, cream      0.34, soft      0.23\n",
      "\n",
      "Topic  65\n",
      "ice      0.35, cream      0.27, meat      0.26\n",
      "\n",
      "Topic  66\n",
      "biceps      0.27, 💪      0.27, flexed      0.27\n",
      "\n",
      "Topic  67\n",
      "camera      0.46, movie      0.46, 🎥      0.39\n",
      "\n",
      "Topic  68\n",
      "chicken      0.51, 🐔      0.42, computer      0.21\n",
      "\n",
      "Topic  69\n",
      "chicken      0.33, 🐔      0.28, strawberry      0.16\n",
      "\n",
      "Topic  70\n",
      "strawberry      0.55, banana      0.45, baby      0.17\n",
      "\n",
      "Topic  71\n",
      "joy      0.38, tears      0.38, 😂      0.38\n",
      "\n",
      "Topic  72\n",
      "joy      0.30, tears      0.30, 😂      0.30\n",
      "\n",
      "Topic  73\n",
      "thanks      0.77, strawberry      0.13, banana      0.10\n",
      "\n",
      "Topic  74\n",
      "🔥      0.49, high      0.26, voltage      0.23\n",
      "\n",
      "Topic  75\n",
      "sunglasses      0.42, 😎      0.42, green      0.32\n",
      "\n",
      "Topic  76\n",
      "running      0.51, shoe      0.40, 👟      0.34\n",
      "\n",
      "Topic  77\n",
      "green      0.48, salad      0.41, 🥗      0.35\n",
      "\n",
      "Topic  78\n",
      "airplane      0.59, ✈      0.44, 🔥      0.13\n",
      "\n",
      "Topic  79\n",
      "u      0.45, thank      0.32, s      0.26\n",
      "\n",
      "Topic  80\n",
      "u      0.39, thank      0.26, dark      0.24\n",
      "\n",
      "Topic  81\n",
      "tumbler      0.44, 🥃      0.44, glass      0.34\n",
      "\n",
      "Topic  82\n",
      "right      0.30, left      0.23, man      0.22\n",
      "\n",
      "Topic  83\n",
      "car      0.55, stuff      0.38, thumbs      0.20\n",
      "\n",
      "Topic  84\n",
      "dark      0.12, 🏿      0.12, right      0.12\n",
      "\n",
      "Topic  85\n",
      "stuff      0.40, 🏿      0.14, 💃      0.11\n",
      "\n",
      "Topic  86\n",
      "thumbs      0.47, 👍      0.44, utility      0.13\n",
      "\n",
      "Topic  87\n",
      "musical      0.56, notes      0.30, 🎶      0.29\n",
      "\n",
      "Topic  88\n",
      "cooking      0.20, 🍳      0.20, thumbs      0.17\n",
      "\n",
      "Topic  89\n",
      "baseball      0.55, ⚾      0.50, 🏠      0.18\n",
      "\n",
      "Topic  90\n",
      "garden      0.40, 🏡      0.39, baseball      0.23\n",
      "\n",
      "Topic  91\n",
      "volleyball      0.68, 🏐      0.66, 🏠      0.08\n",
      "\n",
      "Topic  92\n",
      "s      0.22, 🇺      0.19, u      0.18\n",
      "\n",
      "Topic  93\n",
      "cup      0.55, 🥤      0.52, straw      0.52\n",
      "\n",
      "Topic  94\n",
      "game      0.47, video      0.30, 🎮      0.29\n",
      "\n",
      "Topic  95\n",
      "raising      0.34, 🙌      0.33, ticket      0.26\n",
      "\n",
      "Topic  96\n",
      "game      0.44, video      0.28, 🎮      0.27\n",
      "\n",
      "Topic  97\n",
      "ticket      0.66, 🎫      0.43, uber      0.25\n",
      "\n",
      "Topic  98\n",
      "rice      0.65, cooked      0.28, 🍚      0.28\n",
      "\n",
      "Topic  99\n",
      "uber      0.93, rice      0.09, tree      0.09\n",
      "\n",
      "Topic  100\n",
      "peach      0.52, 🍑      0.51, claus      0.22\n",
      "\n",
      "Topic  101\n",
      "peach      0.43, 🍑      0.43, tree      0.20\n",
      "\n",
      "Topic  102\n",
      "pan      0.50, shallow      0.49, 🥘      0.49\n",
      "\n",
      "Topic  103\n",
      "button      0.56, p      0.50, 🅿      0.41\n",
      "\n",
      "Topic  104\n",
      "good      0.87, fantasy      0.17, button      0.15\n",
      "\n",
      "Topic  105\n",
      "bills      0.89, splitwisecom      0.36, fantasy      0.16\n",
      "\n",
      "Topic  106\n",
      "american      0.18, 🏈      0.18, good      0.18\n",
      "\n",
      "Topic  107\n",
      "heavy      0.45, mark      0.42, white      0.38\n",
      "\n",
      "Topic  108\n",
      "taxi      0.67, 🚕      0.56, oncoming      0.26\n",
      "\n",
      "Topic  109\n",
      "gas      0.73, mouth      0.27, money      0.21\n",
      "\n",
      "Topic  110\n",
      "clown      0.36, 🤡      0.36, woman      0.25\n",
      "\n",
      "Topic  111\n",
      "mask      0.39, medical      0.37, 😷      0.36\n",
      "\n",
      "Topic  112\n",
      "mask      0.37, medical      0.35, 😷      0.34\n",
      "\n",
      "Topic  113\n",
      "fish      0.64, love      0.40, 🐟      0.39\n",
      "\n",
      "Topic  114\n",
      "fish      0.46, 🐟      0.28, water      0.14\n",
      "\n",
      "Topic  115\n",
      "water      0.42, gas      0.37, wave      0.20\n",
      "\n",
      "Topic  116\n",
      "water      0.60, wave      0.28, 🌊      0.28\n",
      "\n",
      "Topic  117\n",
      "oncoming      0.62, 🚘      0.41, fist      0.18\n",
      "\n",
      "Topic  118\n",
      "partying      0.36, 🥳      0.36, love      0.13\n",
      "\n",
      "Topic  119\n",
      "arrow      0.36, hand      0.28, 🤙      0.27\n",
      "\n",
      "Topic  120\n",
      "venmo_dollar      0.71, partying      0.25, 🥳      0.25\n",
      "\n",
      "Topic  121\n",
      "venmo_dollar      0.60, telephone      0.33, ☎      0.25\n",
      "\n",
      "Topic  122\n",
      "telephone      0.50, ☎      0.38, partying      0.23\n",
      "\n",
      "Topic  123\n",
      "telephone      0.25, ☎      0.19, glasses      0.16\n",
      "\n",
      "Topic  124\n",
      "glasses      0.49, 🥂      0.48, clinking      0.32\n",
      "\n",
      "Topic  125\n",
      "snowflake      0.29, ❄      0.29, growing      0.16\n",
      "\n",
      "Topic  126\n",
      "🏾      0.35, bulb      0.18, 💡      0.18\n",
      "\n",
      "Topic  127\n",
      "balloon      0.44, 🎈      0.38, dinner      0.37\n",
      "\n",
      "Topic  128\n",
      "ear      0.39, corn      0.38, arrow      0.15\n",
      "\n",
      "Topic  129\n",
      "dinner      0.62, points      0.23, 💯      0.22\n",
      "\n",
      "Topic  130\n",
      "ear      0.34, corn      0.33, dinner      0.30\n",
      "\n",
      "Topic  131\n",
      "ear      0.39, corn      0.38, balloon      0.33\n",
      "\n",
      "Topic  132\n",
      "television      0.58, 📺      0.58, points      0.12\n",
      "\n",
      "Topic  133\n",
      "groceries      0.88, television      0.16, 📺      0.16\n",
      "\n",
      "Topic  134\n",
      "hotel      0.30, cat      0.29, 🍸      0.26\n",
      "\n",
      "Topic  135\n",
      "beach      0.32, umbrella      0.28, 🏖      0.25\n",
      "\n",
      "Topic  136\n",
      "beach      0.41, umbrella      0.37, 🏖      0.33\n",
      "\n",
      "Topic  137\n",
      "cut      0.27, 🍴      0.22, happy      0.18\n",
      "\n",
      "Topic  138\n",
      "day      0.31, happy      0.31, spaghetti      0.23\n",
      "\n",
      "Topic  139\n",
      "lunch      0.67, hotel      0.29, bone      0.21\n",
      "\n",
      "Topic  140\n",
      "away      0.32, 💨      0.31, dashing      0.31\n",
      "\n",
      "Topic  141\n",
      "plate      0.37, 🍽      0.36, day      0.19\n",
      "\n",
      "Topic  142\n",
      "hotel      0.30, happy      0.20, spaghetti      0.20\n",
      "\n",
      "Topic  143\n",
      "points      0.30, hotel      0.30, 💯      0.29\n",
      "\n",
      "Topic  144\n",
      "points      0.18, 💯      0.17, hotel      0.15\n",
      "\n",
      "Topic  145\n",
      "away      0.28, dashing      0.27, 💨      0.27\n",
      "\n",
      "Topic  146\n",
      "work      0.21, bone      0.20, 🍖      0.19\n",
      "\n",
      "Topic  147\n",
      "🌲      0.16, evergreen      0.16, tree      0.16\n",
      "\n",
      "Topic  148\n",
      "work      0.23, apple      0.23, star      0.21\n",
      "\n",
      "Topic  149\n",
      "work      0.23, suit      0.16, ❤      0.14\n"
     ]
    }
   ],
   "source": [
    "display_topics(lsa, vectorizer.get_feature_names(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f92252d54f0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lsa.components_[15])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmf_model = NMF(20)\n",
    "nmf_doc_topic = nmf_model.fit_transform(dtm[:100000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/nmf_defined_100k.pickle', 'wb') as f:\n",
    "    pickle.dump(nmf_doc_topic, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic  0\n",
      "skin      7.16, tone      7.16, light      6.02\n",
      "\n",
      "Topic  1\n",
      "hot      5.89, beverage      5.76, ☕      5.75\n",
      "\n",
      "Topic  2\n",
      "symbol      5.54, letter      5.51, regional      5.51\n",
      "\n",
      "Topic  3\n",
      "face     11.20, smiling      3.20, eyes      1.51\n",
      "\n",
      "Topic  4\n",
      "pizza      7.32, 🍕      7.03, leg      0.03\n",
      "\n",
      "Topic  5\n",
      "money      6.17, wings      5.05, 💸      5.00\n",
      "\n",
      "Topic  6\n",
      "dark      3.50, man      3.45, skin      3.32\n",
      "\n",
      "Topic  7\n",
      "heart      6.18, red      4.38, ❤      4.21\n",
      "\n",
      "Topic  8\n",
      "football      5.44, american      5.00, 🏈      5.00\n",
      "\n",
      "Topic  9\n",
      "hamburger      6.28, 🍔      6.28, fries      0.35\n",
      "\n",
      "Topic  10\n",
      "party      5.90, popper      5.56, 🎉      5.56\n",
      "\n",
      "Topic  11\n",
      "taco      6.70, 🌮      6.50, tropical      1.09\n",
      "\n",
      "Topic  12\n",
      "sign      5.05, ‍      4.41, person      4.28\n",
      "\n",
      "Topic  13\n",
      "beer      5.90, mug      3.24, 🍺      3.23\n",
      "\n",
      "Topic  14\n",
      "sushi      5.83, 🍣      5.71, rice      0.28\n",
      "\n",
      "Topic  15\n",
      "burrito      5.27, 🌯      5.20, utility      0.09\n",
      "\n",
      "Topic  16\n",
      "high      3.25, voltage      3.16, ⚡      3.16\n",
      "\n",
      "Topic  17\n",
      "food      7.83, pan      0.79, shallow      0.78\n",
      "\n",
      "Topic  18\n",
      "eggplant      3.53, 🍆      3.53, sweat      3.29\n",
      "\n",
      "Topic  19\n",
      "glass      4.31, wine      3.03, 🍷      2.95\n"
     ]
    }
   ],
   "source": [
    "display_topics(nmf_model, vectorizer.get_feature_names(), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/allen/opt/anaconda3/envs/metis/lib/python3.8/site-packages/scipy/sparse/sparsetools.py:21: DeprecationWarning: `scipy.sparse.sparsetools` is deprecated!\n",
      "scipy.sparse.sparsetools is a private module for scipy.sparse, and should not be used.\n",
      "  _deprecated()\n"
     ]
    }
   ],
   "source": [
    "from gensim import matutils, models\n",
    "import scipy.sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tdm = dtm.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_counts = scipy.sparse.csr_matrix(tdm)\n",
    "corpus = matutils.Sparse2Corpus(sparse_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim.corpora as corpora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "id2word2 = corpora.Dictionary([[k for k,v in vectorizer.vocabulary_.items()]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('b', 17), ('food', 185), ('💇', 675), ('person', 352), ('getting', 203), ('haircut', 226), ('face', 164), ('horns', 248), ('🎳', 627), ('bowling', 64), ('🍵', 603), ('teacup', 469), ('handle', 230), ('good', 212), ('⛽', 553), ('fuel', 196), ('pump', 376), ('raised', 381), ('fist', 178), ('🏿', 645), ('dark', 135), ('skin', 425), ('tone', 487), ('💃', 671), ('woman', 528), ('dancing', 134), ('🌮', 568), ('taco', 463), ('🥑', 740), ('avocado', 14), ('know', 274), ('tickets', 481), ('🙏', 721), ('folded', 184), ('hands', 231), ('🏼', 642), ('medium', 309), ('light', 288), ('uber', 499), ('got', 214), ('right', 394), ('ya', 533), ('cash', 88), ('money', 315), ('🔥', 705), ('🍗', 585), ('poultry', 374), ('leg', 283), ('🐔', 646), ('chicken', 97), ('🎫', 625), ('ticket', 480), ('🍿', 612), ('popcorn', 369), ('airbnb', 2), ('🌶', 573), ('hot', 250), ('pepper', 351), ('car', 84), ('🏻', 641), ('💯', 693), ('points', 363), ('💦', 689), ('sweat', 458), ('droplets', 151), ('lunch', 296), ('\\u200d', 540), ('♀', 545), ('female', 172), ('sign', 423), ('🍸', 605), ('cocktail', 109), ('glass', 208), ('🥰', 752), ('smiling', 428), ('3', 0), ('hearts', 236), ('🎂', 614), ('birthday', 49), ('cake', 79), ('🎉', 619), ('party', 345), ('popper', 370), ('🌯', 569), ('burrito', 74), ('🍆', 576), ('eggplant', 158), ('🍺', 607), ('beer', 41), ('mug', 320), ('groceries', 220), ('🥳', 753), ('partying', 346), ('p', 336), ('🎮', 626), ('video', 512), ('game', 200), ('💕', 680), ('hit', 242), ('o', 331), ('s', 400), ('🤑', 727), ('mouth', 318), ('🍔', 582), ('hamburger', 228), ('🍝', 589), ('spaghetti', 437), ('🍕', 583), ('pizza', 357), ('hole', 245), ('️', 560), ('🏌', 634), ('golfing', 211), ('♂', 546), ('male', 300), ('crying', 126), ('clothes', 106), ('❤', 558), ('red', 384), ('heart', 235), ('👁', 653), ('eye', 162), ('😂', 709), ('tears', 470), ('joy', 265), ('i’m', 261), ('house', 252), ('💩', 691), ('pile', 355), ('poo', 367), ('🥤', 747), ('cup', 127), ('straw', 447), ('🐕', 647), ('dog', 142), ('tv', 496), ('phone', 353), ('💵', 696), ('dollar', 143), ('banknote', 31), ('💸', 697), ('wings', 526), ('📱', 701), ('mobile', 313), ('hi', 240), ('☕', 543), ('beverage', 45), ('⛳', 551), ('flag', 179), ('🏠', 638), ('umbrella', 500), ('🍴', 602), ('fork', 189), ('knife', 273), ('🍹', 606), ('tropical', 492), ('drink', 148), ('march', 302), ('rent', 387), ('kids', 271), ('january', 263), ('make', 299), ('gift', 205), ('happy', 232), ('bitch', 50), ('girl', 207), ('stuff', 450), ('thanks', 476), ('night', 328), ('hand', 229), ('🍱', 599), ('bento', 43), ('box', 65), ('things', 477), ('parking', 344), ('🍣', 592), ('sushi', 457), ('squares', 442), ('hotel', 251), ('🏨', 640), ('💍', 678), ('ring', 395), ('forgot', 188), ('lol', 293), ('thank', 475), ('massage', 306), ('test', 473), ('baseball', 33), ('🚙', 725), ('sport', 441), ('utility', 505), ('vehicle', 507), ('poker', 364), ('man', 301), ('refund', 385), ('july', 267), ('gas', 202), ('electric', 159), ('love', 295), ('late', 279), ('fees', 171), ('water', 515), ('school', 406), ('la', 275), ('wifi', 523), ('🤘', 729), ('present', 375), ('little', 291), ('moon', 317), ('x', 532), ('golf', 210), ('😋', 712), ('savoring', 405), ('🏃', 631), ('running', 399), ('g', 199), ('fantasy', 168), ('🏈', 633), ('american', 6), ('football', 187), ('💗', 682), ('growing', 222), ('dinner', 141), ('yo', 536), ('⚾', 550), ('👍', 655), ('thumbs', 478), ('class', 102), ('😘', 715), ('blowing', 53), ('kiss', 272), ('payment', 348), ('trophy', 491), ('new', 327), ('year', 534), ('🌭', 567), ('venmo_dollar', 510), ('n', 323), ('bbq', 35), ('🔙', 704), ('arrow', 9), ('fund', 198), ('drinks', 149), ('bar', 32), ('u', 498), ('christmas', 101), ('day', 137), ('raffle', 379), ('fun', 197), ('ty', 497), ('cookies', 116), ('🍰', 598), ('shortcake', 419), ('garden', 201), ('🎵', 628), ('musical', 322), ('note', 329), ('coffee', 110), ('table', 462), ('💪', 692), ('flexed', 181), ('biceps', 46), ('🇺', 564), ('regional', 386), ('indicator', 256), ('symbol', 460), ('letter', 286), ('🇸', 563), ('life', 287), ('🚕', 722), ('taxi', 467), ('✈', 554), ('airplane', 3), ('camera', 80), ('⭐', 559), ('white', 522), ('star', 444), ('bed', 40), ('🚗', 723), ('automobile', 13), ('🍻', 608), ('clinking', 105), ('mugs', 321), ('suit', 452), ('♥', 547), ('club', 108), ('🎨', 624), ('artist', 10), ('palette', 339), ('pool', 368), ('ball', 27), ('lessons', 285), ('best', 44), ('monkey', 316), ('💥', 688), ('collision', 111), ('sunglasses', 454), ('snow', 430), ('fish', 177), ('🌊', 566), ('wave', 516), ('🏆', 632), ('place', 358), ('hockey', 243), ('🍾', 611), ('bottle', 61), ('popping', 371), ('cork', 118), ('🧀', 755), ('cheese', 94), ('wedge', 519), ('🎶', 629), ('notes', 330), ('skull', 426), ('help', 238), ('bills', 48), ('dad', 133), ('🏐', 635), ('volleyball', 513), ('🐟', 649), ('bread', 67), ('oncoming', 334), ('woman’s', 529), ('hat', 233), ('⚡', 548), ('high', 241), ('voltage', 514), ('open', 335), ('shirts', 414), ('😊', 711), ('eyes', 163), ('🤙', 730), ('backhand', 20), ('index', 255), ('pointing', 362), ('👌', 654), ('ok', 332), ('time', 482), ('basketball', 34), ('🏀', 630), ('favorite', 169), ('c', 78), ('bus', 75), ('service', 409), ('🍞', 590), ('🥪', 750), ('sandwich', 403), ('black', 51), ('rice', 392), ('candy', 82), ('hey', 239), ('da', 132), ('jack', 262), ('🍷', 604), ('wine', 525), ('chinese', 98), ('fuck', 195), ('🐶', 650), ('🙃', 719), ('upside', 502), ('🍫', 597), ('chocolate', 100), ('🍦', 594), ('soft', 434), ('ice', 254), ('cream', 123), ('🏡', 639), ('👀', 652), ('🍓', 581), ('strawberry', 448), ('👕', 657), ('t', 461), ('shirt', 413), ('costco', 120), ('june', 268), ('utilities', 504), ('room', 398), ('cleaning', 104), ('supplies', 456), ('paper', 343), ('milk', 312), ('movie', 319), ('cat', 89), ('great', 217), ('way', 517), ('internet', 258), ('work', 530), ('corn', 119), ('flowers', 183), ('🌼', 575), ('blossom', 52), ('💰', 694), ('bag', 22), ('island', 259), ('🎟', 621), ('admission', 1), ('brunch', 71), ('weekend', 521), ('bank', 30), ('today', 486), ('juice', 266), ('vegas', 506), ('🎅', 617), ('santa', 404), ('claus', 103), ('🎄', 616), ('tree', 489), ('babysitting', 19), ('🎥', 623), ('plus', 361), ('🍎', 579), ('apple', 7), ('za', 538), ('👶', 668), ('baby', 18), ('week', 520), ('🚿', 726), ('shower', 420), ('😎', 714), ('finger', 176), ('🏽', 643), ('super', 455), ('goods', 213), ('🔌', 703), ('plug', 360), ('festival_floaty', 175), ('🐝', 648), ('honeybee', 247), ('wind', 524), ('💡', 687), ('bulb', 72), ('home', 246), ('boy', 66), ('need', 325), ('💳', 695), ('credit', 124), ('card', 85), ('boat', 56), ('festival', 173), ('🥯', 751), ('bagel', 23), ('🎁', 613), ('wrapped', 531), ('👻', 669), ('ghost', 204), ('🍩', 595), ('doughnut', 146), ('🍤', 593), ('fried', 192), ('shrimp', 421), ('🙌', 720), ('raising', 382), ('meat', 307), ('wedding', 518), ('🍑', 580), ('peach', 349), ('bone', 57), ('📚', 699), ('books', 59), ('chick', 96), ('chipotle', 99), ('sex', 410), ('it’s', 260), ('🐷', 651), ('pig', 354), ('😉', 710), ('winking', 527), ('august', 12), ('tip', 483), ('big', 47), ('alien', 4), ('🤡', 732), ('clown', 107), ('🍟', 591), ('french', 190), ('fries', 194), ('dues', 152), ('pay', 347), ('👨', 664), ('🍳', 601), ('cooking', 117), ('tongue', 488), ('like', 289), ('🎤', 622), ('microphone', 311), ('👦', 662), ('board', 55), ('🤤', 733), ('drooling', 150), ('👩', 665), ('hair', 225), ('book', 58), ('🥂', 738), ('glasses', 209), ('green', 218), ('🍚', 586), ('cooked', 114), ('stuffed', 451), ('flatbread', 180), ('rest', 389), ('insurance', 257), ('sitting', 424), ('👯', 666), ('people', 350), ('bunny', 73), ('ears', 156), ('april', 8), ('potato', 373), ('🚘', 724), ('tax', 466), ('return', 390), ('grinning', 219), ('sweet', 459), ('head', 234), ('🤷', 737), ('shrugging', 422), ('shit', 415), ('half', 227), ('blue', 54), ('🍜', 588), ('steaming', 446), ('bowl', 63), ('fee', 170), ('☺', 544), ('donation', 144), ('rolling', 397), ('💆', 674), ('leaf', 281), ('😷', 718), ('medical', 308), ('mask', 304), ('🥘', 744), ('shallow', 411), ('pan', 341), ('foods', 186), ('shoe', 416), ('heavy', 237), ('check', 93), ('mark', 303), ('🦄', 754), ('unicorn', 501), ('old', 333), ('beach', 37), ('crab', 122), ('cause', 90), ('deposit', 138), ('venmo', 509), ('balance', 26), ('you’re', 537), ('🇷', 562), ('r', 378), ('🥩', 749), ('cut', 129), ('amazon', 5), ('sorry', 436), ('🍖', 584), ('🍲', 600), ('pot', 372), ('cards', 86), ('🎃', 615), ('lantern', 276), ('guitar', 223), ('kale', 270), ('salad', 402), ('send', 408), ('🍽', 610), ('plate', 359), ('🎈', 618), ('balloon', 28), ('holding', 244), ('🏕', 636), ('camping', 81), ('thai', 474), ('💋', 677), ('📞', 700), ('telephone', 471), ('receiver', 383), ('sun', 453), ('bear', 39), ('📺', 702), ('television', 472), ('paid', 338), ('⛵', 552), ('sailboat', 401), ('❄', 557), ('snowflake', 431), ('👰', 667), ('bride', 69), ('veil', 508), ('🏖', 637), ('target', 465), ('gifts', 206), ('☀', 541), ('💐', 679), ('bouquet', 62), ('👟', 661), ('👖', 658), ('jeans', 264), ('💙', 683), ('💊', 676), ('pill', 356), ('ear', 155), ('shoes', 417), ('⚽', 549), ('soccer', 433), ('merry', 310), ('💛', 685), ('yellow', 535), ('💜', 686), ('purple', 377), ('desert', 139), ('shipping', 412), ('🤩', 735), ('struck', 449), ('🌴', 572), ('palm', 340), ('just', 269), ('loan', 292), ('ass', 11), ('ur', 503), ('bday', 36), ('lyft', 297), ('extra', 161), ('thx', 479), ('🍼', 609), ('🍇', 577), ('grapes', 216), ('🥓', 741), ('bacon', 21), ('button', 76), ('🤠', 731), ('cowboy', 121), ('pole', 365), ('🥃', 739), ('tumbler', 493), ('🤪', 736), ('zany', 539), ('pancakes', 342), ('mom', 314), ('ride', 393), ('change', 92), ('egg', 157), ('👗', 659), ('dress', 147), ('🥟', 746), ('dumpling', 153), ('splitwisecom', 440), ('🥖', 742), ('baguette', 25), ('✌', 555), ('victory', 511), ('🌱', 570), ('seedling', 407), ('trip', 490), ('🍛', 587), ('curry', 128), ('🍪', 596), ('cookie', 115), ('slightly', 427), ('turkey', 494), ('🥦', 748), ('broccoli', 70), ('🥗', 743), ('tix', 485), ('breakfast', 68), ('🎊', 620), ('confetti', 113), ('don’t', 145), ('🕺', 706), ('festival_beer', 174), ('m', 298), ('lawn', 280), ('🌲', 571), ('evergreen', 160), ('care', 87), ('nerd', 326), ('e', 154), ('💻', 698), ('laptop', 277), ('computer', 112), ('h', 224), ('cell', 91), ('😭', 717), ('loudly', 294), ('rental', 388), ('😍', 713), ('buy', 77), ('rocket_popsicle', 396), ('awesome', 16), ('✨', 556), ('sparkles', 438), ('🏾', 644), ('package', 337), ('🍌', 578), ('banana', 29), ('☎', 542), ('🅿', 561), ('starbucks', 445), ('🥞', 745), ('tacos', 464), ('din', 140), ('friend', 193), ('bell', 42), ('cap', 83), ('🤦', 734), ('facepalming', 165), ('💅', 673), ('nail', 324), ('polish', 366), ('soap', 432), ('👧', 663), ('🌸', 574), ('cherry', 95), ('d', 131), ('🤗', 728), ('hugging', 253), ('family', 167), ('squinting', 443), ('😜', 716), ('💁', 670), ('tipping', 484), ('masks', 305), ('snacks', 429), ('tea', 468), ('graduation', 215), ('horse', 249), ('👑', 656), ('crown', 125), ('💄', 672), ('lipstick', 290), ('😁', 708), ('beaming', 38), ('💨', 690), ('dashing', 136), ('away', 15), ('shopping', 418), ('friday', 191), ('ribbon', 391), ('💚', 684), ('🌈', 565), ('rainbow', 380), ('👚', 660), ('booze', 60), ('left', 282), ('💖', 681), ('sparkling', 439), ('facing', 166), ('cuz', 130), ('softball', 435), ('lashes', 278), ('floor', 182), ('grocery', 221), ('tutoring', 495), ('bags', 24), ('lesson', 284), ('🖤', 707)])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.vocabulary_.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "id2word = dict((v, k) for k, v in vectorizer.vocabulary_.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20:09:30\n",
      "20:23:35\n"
     ]
    }
   ],
   "source": [
    "print(datetime.now().strftime(\"%H:%M:%S\"))\n",
    "lda = models.LdaMulticore(corpus=corpus, id2word=id2word, num_topics=20, passes=50, workers=8)\n",
    "lda.print_topics()\n",
    "print(datetime.now().strftime(\"%H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/lda_defined_286k.pickle', 'wb') as f:\n",
    "    pickle.dump(lda, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.113*\"thank\" + 0.060*\"uber\" + 0.059*\"dollar\" + 0.054*\"banknote\" + 0.050*\"💵\" + 0.047*\"christmas\" + 0.042*\"chicken\" + 0.039*\"tree\" + 0.032*\"rice\" + 0.030*\"🐔\"'),\n",
       " (1,\n",
       "  '0.095*\"hot\" + 0.067*\"beverage\" + 0.065*\"☕\" + 0.051*\"baby\" + 0.050*\"knife\" + 0.046*\"fork\" + 0.046*\"🍴\" + 0.035*\"groceries\" + 0.035*\"bills\" + 0.030*\"bag\"'),\n",
       " (2,\n",
       "  '0.239*\"heart\" + 0.141*\"red\" + 0.123*\"❤\" + 0.071*\"️\" + 0.031*\"bowl\" + 0.024*\"🍜\" + 0.024*\"steaming\" + 0.020*\"mouth\" + 0.019*\"suit\" + 0.017*\"blue\"'),\n",
       " (3,\n",
       "  '0.327*\"face\" + 0.109*\"smiling\" + 0.064*\"eyes\" + 0.035*\"fuel\" + 0.034*\"pump\" + 0.034*\"⛽\" + 0.017*\"😊\" + 0.016*\"grinning\" + 0.015*\"cat\" + 0.015*\"musical\"'),\n",
       " (4,\n",
       "  '0.072*\"car\" + 0.057*\"cooking\" + 0.056*\"🍳\" + 0.050*\"hearts\" + 0.049*\"good\" + 0.049*\"3\" + 0.049*\"🥰\" + 0.045*\"basketball\" + 0.043*\"🏀\" + 0.042*\"arrow\"'),\n",
       " (5,\n",
       "  '0.147*\"taco\" + 0.133*\"🌮\" + 0.110*\"thanks\" + 0.074*\"bottle\" + 0.072*\"🔥\" + 0.057*\"cork\" + 0.057*\"popping\" + 0.057*\"🍾\" + 0.023*\"doughnut\" + 0.023*\"🍩\"'),\n",
       " (6,\n",
       "  '0.068*\"hearts\" + 0.066*\"cookie\" + 0.063*\"💕\" + 0.058*\"🍪\" + 0.054*\"ticket\" + 0.035*\"ball\" + 0.032*\"🎫\" + 0.032*\"oncoming\" + 0.024*\"tumbler\" + 0.023*\"soccer\"'),\n",
       " (7,\n",
       "  '0.075*\"leg\" + 0.072*\"lunch\" + 0.072*\"poultry\" + 0.072*\"🍗\" + 0.065*\"gas\" + 0.039*\"meat\" + 0.039*\"clothes\" + 0.039*\"deposit\" + 0.035*\"woman’s\" + 0.033*\"new\"'),\n",
       " (8,\n",
       "  '0.089*\"symbol\" + 0.087*\"letter\" + 0.085*\"regional\" + 0.085*\"indicator\" + 0.055*\"love\" + 0.055*\"automobile\" + 0.055*\"🚗\" + 0.038*\"u\" + 0.033*\"nail\" + 0.032*\"polish\"'),\n",
       " (9,\n",
       "  '0.125*\"beer\" + 0.060*\"drink\" + 0.059*\"mug\" + 0.058*\"🍺\" + 0.058*\"clinking\" + 0.056*\"tropical\" + 0.055*\"mugs\" + 0.054*\"🍻\" + 0.050*\"🍹\" + 0.042*\"tickets\"'),\n",
       " (10,\n",
       "  '0.159*\"money\" + 0.122*\"wings\" + 0.117*\"💸\" + 0.101*\"house\" + 0.085*\"🏠\" + 0.029*\"high\" + 0.025*\"camera\" + 0.024*\"movie\" + 0.022*\"voltage\" + 0.022*\"⚡\"'),\n",
       " (11,\n",
       "  '0.111*\"skin\" + 0.110*\"tone\" + 0.086*\"light\" + 0.063*\"medium\" + 0.053*\"\\u200d\" + 0.048*\"sign\" + 0.044*\"🏻\" + 0.040*\"🏼\" + 0.036*\"person\" + 0.030*\"female\"'),\n",
       " (12,\n",
       "  '0.113*\"phone\" + 0.071*\"mobile\" + 0.065*\"📱\" + 0.050*\"hands\" + 0.042*\"glasses\" + 0.039*\"hotel\" + 0.039*\"🥂\" + 0.037*\"girl\" + 0.036*\"folded\" + 0.036*\"🙏\"'),\n",
       " (13,\n",
       "  '0.118*\"party\" + 0.094*\"🎉\" + 0.094*\"popper\" + 0.092*\"birthday\" + 0.054*\"cake\" + 0.049*\"hamburger\" + 0.049*\"🍔\" + 0.045*\"happy\" + 0.044*\"🎂\" + 0.032*\"venmo_dollar\"'),\n",
       " (14,\n",
       "  '0.228*\"food\" + 0.109*\"gift\" + 0.074*\"wrapped\" + 0.074*\"🎁\" + 0.046*\"face\" + 0.041*\"dog\" + 0.031*\"🐶\" + 0.022*\"night\" + 0.020*\"pan\" + 0.020*\"savoring\"'),\n",
       " (15,\n",
       "  '0.066*\"stuff\" + 0.052*\"ice\" + 0.048*\"dinner\" + 0.038*\"cream\" + 0.035*\"cheese\" + 0.030*\"white\" + 0.029*\"wedge\" + 0.029*\"🧀\" + 0.028*\"cleaning\" + 0.027*\"cup\"'),\n",
       " (16,\n",
       "  '0.065*\"getting\" + 0.056*\"person\" + 0.056*\"haircut\" + 0.055*\"fries\" + 0.055*\"french\" + 0.052*\"🍟\" + 0.048*\"💇\" + 0.039*\"face\" + 0.031*\"taxi\" + 0.028*\"partying\"'),\n",
       " (17,\n",
       "  '0.109*\"glass\" + 0.084*\"wine\" + 0.074*\"🍷\" + 0.070*\"football\" + 0.058*\"american\" + 0.057*\"🏈\" + 0.054*\"sweat\" + 0.049*\"💦\" + 0.049*\"droplets\" + 0.035*\"electric\"'),\n",
       " (18,\n",
       "  '0.218*\"pizza\" + 0.183*\"🍕\" + 0.091*\"kiss\" + 0.077*\"face\" + 0.074*\"blowing\" + 0.074*\"😘\" + 0.034*\"bread\" + 0.030*\"🍞\" + 0.025*\"tip\" + 0.022*\"mark\"'),\n",
       " (19,\n",
       "  '0.107*\"rent\" + 0.061*\"sushi\" + 0.052*\"🍣\" + 0.049*\"burrito\" + 0.045*\"🌯\" + 0.042*\"day\" + 0.032*\"baseball\" + 0.030*\"people\" + 0.029*\"eggplant\" + 0.029*\"🍆\"')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.print_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import CoherenceModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.005676299249983"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.log_perplexity(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coherence Score:  -10.474913260542657\n"
     ]
    }
   ],
   "source": [
    "coherence_model_lda = CoherenceModel(model=lda, corpus=corpus, dictionary=id2word, coherence='u_mass')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis.gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{17: 'b',\n",
       " 185: 'food',\n",
       " 675: '💇',\n",
       " 352: 'person',\n",
       " 203: 'getting',\n",
       " 226: 'haircut',\n",
       " 164: 'face',\n",
       " 248: 'horns',\n",
       " 627: '🎳',\n",
       " 64: 'bowling',\n",
       " 603: '🍵',\n",
       " 469: 'teacup',\n",
       " 230: 'handle',\n",
       " 212: 'good',\n",
       " 553: '⛽',\n",
       " 196: 'fuel',\n",
       " 376: 'pump',\n",
       " 381: 'raised',\n",
       " 178: 'fist',\n",
       " 645: '🏿',\n",
       " 135: 'dark',\n",
       " 425: 'skin',\n",
       " 487: 'tone',\n",
       " 671: '💃',\n",
       " 528: 'woman',\n",
       " 134: 'dancing',\n",
       " 568: '🌮',\n",
       " 463: 'taco',\n",
       " 740: '🥑',\n",
       " 14: 'avocado',\n",
       " 274: 'know',\n",
       " 481: 'tickets',\n",
       " 721: '🙏',\n",
       " 184: 'folded',\n",
       " 231: 'hands',\n",
       " 642: '🏼',\n",
       " 309: 'medium',\n",
       " 288: 'light',\n",
       " 499: 'uber',\n",
       " 214: 'got',\n",
       " 394: 'right',\n",
       " 533: 'ya',\n",
       " 88: 'cash',\n",
       " 315: 'money',\n",
       " 705: '🔥',\n",
       " 585: '🍗',\n",
       " 374: 'poultry',\n",
       " 283: 'leg',\n",
       " 646: '🐔',\n",
       " 97: 'chicken',\n",
       " 625: '🎫',\n",
       " 480: 'ticket',\n",
       " 612: '🍿',\n",
       " 369: 'popcorn',\n",
       " 2: 'airbnb',\n",
       " 573: '🌶',\n",
       " 250: 'hot',\n",
       " 351: 'pepper',\n",
       " 84: 'car',\n",
       " 641: '🏻',\n",
       " 693: '💯',\n",
       " 363: 'points',\n",
       " 689: '💦',\n",
       " 458: 'sweat',\n",
       " 151: 'droplets',\n",
       " 296: 'lunch',\n",
       " 540: '\\u200d',\n",
       " 545: '♀',\n",
       " 172: 'female',\n",
       " 423: 'sign',\n",
       " 605: '🍸',\n",
       " 109: 'cocktail',\n",
       " 208: 'glass',\n",
       " 752: '🥰',\n",
       " 428: 'smiling',\n",
       " 0: '3',\n",
       " 236: 'hearts',\n",
       " 614: '🎂',\n",
       " 49: 'birthday',\n",
       " 79: 'cake',\n",
       " 619: '🎉',\n",
       " 345: 'party',\n",
       " 370: 'popper',\n",
       " 569: '🌯',\n",
       " 74: 'burrito',\n",
       " 576: '🍆',\n",
       " 158: 'eggplant',\n",
       " 607: '🍺',\n",
       " 41: 'beer',\n",
       " 320: 'mug',\n",
       " 220: 'groceries',\n",
       " 753: '🥳',\n",
       " 346: 'partying',\n",
       " 336: 'p',\n",
       " 626: '🎮',\n",
       " 512: 'video',\n",
       " 200: 'game',\n",
       " 680: '💕',\n",
       " 242: 'hit',\n",
       " 331: 'o',\n",
       " 400: 's',\n",
       " 727: '🤑',\n",
       " 318: 'mouth',\n",
       " 582: '🍔',\n",
       " 228: 'hamburger',\n",
       " 589: '🍝',\n",
       " 437: 'spaghetti',\n",
       " 583: '🍕',\n",
       " 357: 'pizza',\n",
       " 245: 'hole',\n",
       " 560: '️',\n",
       " 634: '🏌',\n",
       " 211: 'golfing',\n",
       " 546: '♂',\n",
       " 300: 'male',\n",
       " 126: 'crying',\n",
       " 106: 'clothes',\n",
       " 558: '❤',\n",
       " 384: 'red',\n",
       " 235: 'heart',\n",
       " 653: '👁',\n",
       " 162: 'eye',\n",
       " 709: '😂',\n",
       " 470: 'tears',\n",
       " 265: 'joy',\n",
       " 261: 'i’m',\n",
       " 252: 'house',\n",
       " 691: '💩',\n",
       " 355: 'pile',\n",
       " 367: 'poo',\n",
       " 747: '🥤',\n",
       " 127: 'cup',\n",
       " 447: 'straw',\n",
       " 647: '🐕',\n",
       " 142: 'dog',\n",
       " 496: 'tv',\n",
       " 353: 'phone',\n",
       " 696: '💵',\n",
       " 143: 'dollar',\n",
       " 31: 'banknote',\n",
       " 697: '💸',\n",
       " 526: 'wings',\n",
       " 701: '📱',\n",
       " 313: 'mobile',\n",
       " 240: 'hi',\n",
       " 543: '☕',\n",
       " 45: 'beverage',\n",
       " 551: '⛳',\n",
       " 179: 'flag',\n",
       " 638: '🏠',\n",
       " 500: 'umbrella',\n",
       " 602: '🍴',\n",
       " 189: 'fork',\n",
       " 273: 'knife',\n",
       " 606: '🍹',\n",
       " 492: 'tropical',\n",
       " 148: 'drink',\n",
       " 302: 'march',\n",
       " 387: 'rent',\n",
       " 271: 'kids',\n",
       " 263: 'january',\n",
       " 299: 'make',\n",
       " 205: 'gift',\n",
       " 232: 'happy',\n",
       " 50: 'bitch',\n",
       " 207: 'girl',\n",
       " 450: 'stuff',\n",
       " 476: 'thanks',\n",
       " 328: 'night',\n",
       " 229: 'hand',\n",
       " 599: '🍱',\n",
       " 43: 'bento',\n",
       " 65: 'box',\n",
       " 477: 'things',\n",
       " 344: 'parking',\n",
       " 592: '🍣',\n",
       " 457: 'sushi',\n",
       " 442: 'squares',\n",
       " 251: 'hotel',\n",
       " 640: '🏨',\n",
       " 678: '💍',\n",
       " 395: 'ring',\n",
       " 188: 'forgot',\n",
       " 293: 'lol',\n",
       " 475: 'thank',\n",
       " 306: 'massage',\n",
       " 473: 'test',\n",
       " 33: 'baseball',\n",
       " 725: '🚙',\n",
       " 441: 'sport',\n",
       " 505: 'utility',\n",
       " 507: 'vehicle',\n",
       " 364: 'poker',\n",
       " 301: 'man',\n",
       " 385: 'refund',\n",
       " 267: 'july',\n",
       " 202: 'gas',\n",
       " 159: 'electric',\n",
       " 295: 'love',\n",
       " 279: 'late',\n",
       " 171: 'fees',\n",
       " 515: 'water',\n",
       " 406: 'school',\n",
       " 275: 'la',\n",
       " 523: 'wifi',\n",
       " 729: '🤘',\n",
       " 375: 'present',\n",
       " 291: 'little',\n",
       " 317: 'moon',\n",
       " 532: 'x',\n",
       " 210: 'golf',\n",
       " 712: '😋',\n",
       " 405: 'savoring',\n",
       " 631: '🏃',\n",
       " 399: 'running',\n",
       " 199: 'g',\n",
       " 168: 'fantasy',\n",
       " 633: '🏈',\n",
       " 6: 'american',\n",
       " 187: 'football',\n",
       " 682: '💗',\n",
       " 222: 'growing',\n",
       " 141: 'dinner',\n",
       " 536: 'yo',\n",
       " 550: '⚾',\n",
       " 655: '👍',\n",
       " 478: 'thumbs',\n",
       " 102: 'class',\n",
       " 715: '😘',\n",
       " 53: 'blowing',\n",
       " 272: 'kiss',\n",
       " 348: 'payment',\n",
       " 491: 'trophy',\n",
       " 327: 'new',\n",
       " 534: 'year',\n",
       " 567: '🌭',\n",
       " 510: 'venmo_dollar',\n",
       " 323: 'n',\n",
       " 35: 'bbq',\n",
       " 704: '🔙',\n",
       " 9: 'arrow',\n",
       " 198: 'fund',\n",
       " 149: 'drinks',\n",
       " 32: 'bar',\n",
       " 498: 'u',\n",
       " 101: 'christmas',\n",
       " 137: 'day',\n",
       " 379: 'raffle',\n",
       " 197: 'fun',\n",
       " 497: 'ty',\n",
       " 116: 'cookies',\n",
       " 598: '🍰',\n",
       " 419: 'shortcake',\n",
       " 201: 'garden',\n",
       " 628: '🎵',\n",
       " 322: 'musical',\n",
       " 329: 'note',\n",
       " 110: 'coffee',\n",
       " 462: 'table',\n",
       " 692: '💪',\n",
       " 181: 'flexed',\n",
       " 46: 'biceps',\n",
       " 564: '🇺',\n",
       " 386: 'regional',\n",
       " 256: 'indicator',\n",
       " 460: 'symbol',\n",
       " 286: 'letter',\n",
       " 563: '🇸',\n",
       " 287: 'life',\n",
       " 722: '🚕',\n",
       " 467: 'taxi',\n",
       " 554: '✈',\n",
       " 3: 'airplane',\n",
       " 80: 'camera',\n",
       " 559: '⭐',\n",
       " 522: 'white',\n",
       " 444: 'star',\n",
       " 40: 'bed',\n",
       " 723: '🚗',\n",
       " 13: 'automobile',\n",
       " 608: '🍻',\n",
       " 105: 'clinking',\n",
       " 321: 'mugs',\n",
       " 452: 'suit',\n",
       " 547: '♥',\n",
       " 108: 'club',\n",
       " 624: '🎨',\n",
       " 10: 'artist',\n",
       " 339: 'palette',\n",
       " 368: 'pool',\n",
       " 27: 'ball',\n",
       " 285: 'lessons',\n",
       " 44: 'best',\n",
       " 316: 'monkey',\n",
       " 688: '💥',\n",
       " 111: 'collision',\n",
       " 454: 'sunglasses',\n",
       " 430: 'snow',\n",
       " 177: 'fish',\n",
       " 566: '🌊',\n",
       " 516: 'wave',\n",
       " 632: '🏆',\n",
       " 358: 'place',\n",
       " 243: 'hockey',\n",
       " 611: '🍾',\n",
       " 61: 'bottle',\n",
       " 371: 'popping',\n",
       " 118: 'cork',\n",
       " 755: '🧀',\n",
       " 94: 'cheese',\n",
       " 519: 'wedge',\n",
       " 629: '🎶',\n",
       " 330: 'notes',\n",
       " 426: 'skull',\n",
       " 238: 'help',\n",
       " 48: 'bills',\n",
       " 133: 'dad',\n",
       " 635: '🏐',\n",
       " 513: 'volleyball',\n",
       " 649: '🐟',\n",
       " 67: 'bread',\n",
       " 334: 'oncoming',\n",
       " 529: 'woman’s',\n",
       " 233: 'hat',\n",
       " 548: '⚡',\n",
       " 241: 'high',\n",
       " 514: 'voltage',\n",
       " 335: 'open',\n",
       " 414: 'shirts',\n",
       " 711: '😊',\n",
       " 163: 'eyes',\n",
       " 730: '🤙',\n",
       " 20: 'backhand',\n",
       " 255: 'index',\n",
       " 362: 'pointing',\n",
       " 654: '👌',\n",
       " 332: 'ok',\n",
       " 482: 'time',\n",
       " 34: 'basketball',\n",
       " 630: '🏀',\n",
       " 169: 'favorite',\n",
       " 78: 'c',\n",
       " 75: 'bus',\n",
       " 409: 'service',\n",
       " 590: '🍞',\n",
       " 750: '🥪',\n",
       " 403: 'sandwich',\n",
       " 51: 'black',\n",
       " 392: 'rice',\n",
       " 82: 'candy',\n",
       " 239: 'hey',\n",
       " 132: 'da',\n",
       " 262: 'jack',\n",
       " 604: '🍷',\n",
       " 525: 'wine',\n",
       " 98: 'chinese',\n",
       " 195: 'fuck',\n",
       " 650: '🐶',\n",
       " 719: '🙃',\n",
       " 502: 'upside',\n",
       " 597: '🍫',\n",
       " 100: 'chocolate',\n",
       " 594: '🍦',\n",
       " 434: 'soft',\n",
       " 254: 'ice',\n",
       " 123: 'cream',\n",
       " 639: '🏡',\n",
       " 652: '👀',\n",
       " 581: '🍓',\n",
       " 448: 'strawberry',\n",
       " 657: '👕',\n",
       " 461: 't',\n",
       " 413: 'shirt',\n",
       " 120: 'costco',\n",
       " 268: 'june',\n",
       " 504: 'utilities',\n",
       " 398: 'room',\n",
       " 104: 'cleaning',\n",
       " 456: 'supplies',\n",
       " 343: 'paper',\n",
       " 312: 'milk',\n",
       " 319: 'movie',\n",
       " 89: 'cat',\n",
       " 217: 'great',\n",
       " 517: 'way',\n",
       " 258: 'internet',\n",
       " 530: 'work',\n",
       " 119: 'corn',\n",
       " 183: 'flowers',\n",
       " 575: '🌼',\n",
       " 52: 'blossom',\n",
       " 694: '💰',\n",
       " 22: 'bag',\n",
       " 259: 'island',\n",
       " 621: '🎟',\n",
       " 1: 'admission',\n",
       " 71: 'brunch',\n",
       " 521: 'weekend',\n",
       " 30: 'bank',\n",
       " 486: 'today',\n",
       " 266: 'juice',\n",
       " 506: 'vegas',\n",
       " 617: '🎅',\n",
       " 404: 'santa',\n",
       " 103: 'claus',\n",
       " 616: '🎄',\n",
       " 489: 'tree',\n",
       " 19: 'babysitting',\n",
       " 623: '🎥',\n",
       " 361: 'plus',\n",
       " 579: '🍎',\n",
       " 7: 'apple',\n",
       " 538: 'za',\n",
       " 668: '👶',\n",
       " 18: 'baby',\n",
       " 520: 'week',\n",
       " 726: '🚿',\n",
       " 420: 'shower',\n",
       " 714: '😎',\n",
       " 176: 'finger',\n",
       " 643: '🏽',\n",
       " 455: 'super',\n",
       " 213: 'goods',\n",
       " 703: '🔌',\n",
       " 360: 'plug',\n",
       " 175: 'festival_floaty',\n",
       " 648: '🐝',\n",
       " 247: 'honeybee',\n",
       " 524: 'wind',\n",
       " 687: '💡',\n",
       " 72: 'bulb',\n",
       " 246: 'home',\n",
       " 66: 'boy',\n",
       " 325: 'need',\n",
       " 695: '💳',\n",
       " 124: 'credit',\n",
       " 85: 'card',\n",
       " 56: 'boat',\n",
       " 173: 'festival',\n",
       " 751: '🥯',\n",
       " 23: 'bagel',\n",
       " 613: '🎁',\n",
       " 531: 'wrapped',\n",
       " 669: '👻',\n",
       " 204: 'ghost',\n",
       " 595: '🍩',\n",
       " 146: 'doughnut',\n",
       " 593: '🍤',\n",
       " 192: 'fried',\n",
       " 421: 'shrimp',\n",
       " 720: '🙌',\n",
       " 382: 'raising',\n",
       " 307: 'meat',\n",
       " 518: 'wedding',\n",
       " 580: '🍑',\n",
       " 349: 'peach',\n",
       " 57: 'bone',\n",
       " 699: '📚',\n",
       " 59: 'books',\n",
       " 96: 'chick',\n",
       " 99: 'chipotle',\n",
       " 410: 'sex',\n",
       " 260: 'it’s',\n",
       " 651: '🐷',\n",
       " 354: 'pig',\n",
       " 710: '😉',\n",
       " 527: 'winking',\n",
       " 12: 'august',\n",
       " 483: 'tip',\n",
       " 47: 'big',\n",
       " 4: 'alien',\n",
       " 732: '🤡',\n",
       " 107: 'clown',\n",
       " 591: '🍟',\n",
       " 190: 'french',\n",
       " 194: 'fries',\n",
       " 152: 'dues',\n",
       " 347: 'pay',\n",
       " 664: '👨',\n",
       " 601: '🍳',\n",
       " 117: 'cooking',\n",
       " 488: 'tongue',\n",
       " 289: 'like',\n",
       " 622: '🎤',\n",
       " 311: 'microphone',\n",
       " 662: '👦',\n",
       " 55: 'board',\n",
       " 733: '🤤',\n",
       " 150: 'drooling',\n",
       " 665: '👩',\n",
       " 225: 'hair',\n",
       " 58: 'book',\n",
       " 738: '🥂',\n",
       " 209: 'glasses',\n",
       " 218: 'green',\n",
       " 586: '🍚',\n",
       " 114: 'cooked',\n",
       " 451: 'stuffed',\n",
       " 180: 'flatbread',\n",
       " 389: 'rest',\n",
       " 257: 'insurance',\n",
       " 424: 'sitting',\n",
       " 666: '👯',\n",
       " 350: 'people',\n",
       " 73: 'bunny',\n",
       " 156: 'ears',\n",
       " 8: 'april',\n",
       " 373: 'potato',\n",
       " 724: '🚘',\n",
       " 466: 'tax',\n",
       " 390: 'return',\n",
       " 219: 'grinning',\n",
       " 459: 'sweet',\n",
       " 234: 'head',\n",
       " 737: '🤷',\n",
       " 422: 'shrugging',\n",
       " 415: 'shit',\n",
       " 227: 'half',\n",
       " 54: 'blue',\n",
       " 588: '🍜',\n",
       " 446: 'steaming',\n",
       " 63: 'bowl',\n",
       " 170: 'fee',\n",
       " 544: '☺',\n",
       " 144: 'donation',\n",
       " 397: 'rolling',\n",
       " 674: '💆',\n",
       " 281: 'leaf',\n",
       " 718: '😷',\n",
       " 308: 'medical',\n",
       " 304: 'mask',\n",
       " 744: '🥘',\n",
       " 411: 'shallow',\n",
       " 341: 'pan',\n",
       " 186: 'foods',\n",
       " 416: 'shoe',\n",
       " 237: 'heavy',\n",
       " 93: 'check',\n",
       " 303: 'mark',\n",
       " 754: '🦄',\n",
       " 501: 'unicorn',\n",
       " 333: 'old',\n",
       " 37: 'beach',\n",
       " 122: 'crab',\n",
       " 90: 'cause',\n",
       " 138: 'deposit',\n",
       " 509: 'venmo',\n",
       " 26: 'balance',\n",
       " 537: 'you’re',\n",
       " 562: '🇷',\n",
       " 378: 'r',\n",
       " 749: '🥩',\n",
       " 129: 'cut',\n",
       " 5: 'amazon',\n",
       " 436: 'sorry',\n",
       " 584: '🍖',\n",
       " 600: '🍲',\n",
       " 372: 'pot',\n",
       " 86: 'cards',\n",
       " 615: '🎃',\n",
       " 276: 'lantern',\n",
       " 223: 'guitar',\n",
       " 270: 'kale',\n",
       " 402: 'salad',\n",
       " 408: 'send',\n",
       " 610: '🍽',\n",
       " 359: 'plate',\n",
       " 618: '🎈',\n",
       " 28: 'balloon',\n",
       " 244: 'holding',\n",
       " 636: '🏕',\n",
       " 81: 'camping',\n",
       " 474: 'thai',\n",
       " 677: '💋',\n",
       " 700: '📞',\n",
       " 471: 'telephone',\n",
       " 383: 'receiver',\n",
       " 453: 'sun',\n",
       " 39: 'bear',\n",
       " 702: '📺',\n",
       " 472: 'television',\n",
       " 338: 'paid',\n",
       " 552: '⛵',\n",
       " 401: 'sailboat',\n",
       " 557: '❄',\n",
       " 431: 'snowflake',\n",
       " 667: '👰',\n",
       " 69: 'bride',\n",
       " 508: 'veil',\n",
       " 637: '🏖',\n",
       " 465: 'target',\n",
       " 206: 'gifts',\n",
       " 541: '☀',\n",
       " 679: '💐',\n",
       " 62: 'bouquet',\n",
       " 661: '👟',\n",
       " 658: '👖',\n",
       " 264: 'jeans',\n",
       " 683: '💙',\n",
       " 676: '💊',\n",
       " 356: 'pill',\n",
       " 155: 'ear',\n",
       " 417: 'shoes',\n",
       " 549: '⚽',\n",
       " 433: 'soccer',\n",
       " 310: 'merry',\n",
       " 685: '💛',\n",
       " 535: 'yellow',\n",
       " 686: '💜',\n",
       " 377: 'purple',\n",
       " 139: 'desert',\n",
       " 412: 'shipping',\n",
       " 735: '🤩',\n",
       " 449: 'struck',\n",
       " 572: '🌴',\n",
       " 340: 'palm',\n",
       " 269: 'just',\n",
       " 292: 'loan',\n",
       " 11: 'ass',\n",
       " 503: 'ur',\n",
       " 36: 'bday',\n",
       " 297: 'lyft',\n",
       " 161: 'extra',\n",
       " 479: 'thx',\n",
       " 609: '🍼',\n",
       " 577: '🍇',\n",
       " 216: 'grapes',\n",
       " 741: '🥓',\n",
       " 21: 'bacon',\n",
       " 76: 'button',\n",
       " 731: '🤠',\n",
       " 121: 'cowboy',\n",
       " 365: 'pole',\n",
       " 739: '🥃',\n",
       " 493: 'tumbler',\n",
       " 736: '🤪',\n",
       " 539: 'zany',\n",
       " 342: 'pancakes',\n",
       " 314: 'mom',\n",
       " 393: 'ride',\n",
       " 92: 'change',\n",
       " 157: 'egg',\n",
       " 659: '👗',\n",
       " 147: 'dress',\n",
       " 746: '🥟',\n",
       " 153: 'dumpling',\n",
       " 440: 'splitwisecom',\n",
       " 742: '🥖',\n",
       " 25: 'baguette',\n",
       " 555: '✌',\n",
       " 511: 'victory',\n",
       " 570: '🌱',\n",
       " 407: 'seedling',\n",
       " 490: 'trip',\n",
       " 587: '🍛',\n",
       " 128: 'curry',\n",
       " 596: '🍪',\n",
       " 115: 'cookie',\n",
       " 427: 'slightly',\n",
       " 494: 'turkey',\n",
       " 748: '🥦',\n",
       " 70: 'broccoli',\n",
       " 743: '🥗',\n",
       " 485: 'tix',\n",
       " 68: 'breakfast',\n",
       " 620: '🎊',\n",
       " 113: 'confetti',\n",
       " 145: 'don’t',\n",
       " 706: '🕺',\n",
       " 174: 'festival_beer',\n",
       " 298: 'm',\n",
       " 280: 'lawn',\n",
       " 571: '🌲',\n",
       " 160: 'evergreen',\n",
       " 87: 'care',\n",
       " 326: 'nerd',\n",
       " 154: 'e',\n",
       " 698: '💻',\n",
       " 277: 'laptop',\n",
       " 112: 'computer',\n",
       " 224: 'h',\n",
       " 91: 'cell',\n",
       " 717: '😭',\n",
       " 294: 'loudly',\n",
       " 388: 'rental',\n",
       " 713: '😍',\n",
       " 77: 'buy',\n",
       " 396: 'rocket_popsicle',\n",
       " 16: 'awesome',\n",
       " 556: '✨',\n",
       " 438: 'sparkles',\n",
       " 644: '🏾',\n",
       " 337: 'package',\n",
       " 578: '🍌',\n",
       " 29: 'banana',\n",
       " 542: '☎',\n",
       " 561: '🅿',\n",
       " 445: 'starbucks',\n",
       " 745: '🥞',\n",
       " 464: 'tacos',\n",
       " 140: 'din',\n",
       " 193: 'friend',\n",
       " 42: 'bell',\n",
       " 83: 'cap',\n",
       " 734: '🤦',\n",
       " 165: 'facepalming',\n",
       " 673: '💅',\n",
       " 324: 'nail',\n",
       " 366: 'polish',\n",
       " 432: 'soap',\n",
       " 663: '👧',\n",
       " 574: '🌸',\n",
       " 95: 'cherry',\n",
       " 131: 'd',\n",
       " 728: '🤗',\n",
       " 253: 'hugging',\n",
       " 167: 'family',\n",
       " 443: 'squinting',\n",
       " 716: '😜',\n",
       " 670: '💁',\n",
       " 484: 'tipping',\n",
       " 305: 'masks',\n",
       " 429: 'snacks',\n",
       " 468: 'tea',\n",
       " 215: 'graduation',\n",
       " 249: 'horse',\n",
       " 656: '👑',\n",
       " 125: 'crown',\n",
       " 672: '💄',\n",
       " 290: 'lipstick',\n",
       " 708: '😁',\n",
       " 38: 'beaming',\n",
       " 690: '💨',\n",
       " 136: 'dashing',\n",
       " 15: 'away',\n",
       " 418: 'shopping',\n",
       " 191: 'friday',\n",
       " 391: 'ribbon',\n",
       " 684: '💚',\n",
       " 565: '🌈',\n",
       " 380: 'rainbow',\n",
       " 660: '👚',\n",
       " 60: 'booze',\n",
       " 282: 'left',\n",
       " 681: '💖',\n",
       " 439: 'sparkling',\n",
       " 166: 'facing',\n",
       " 130: 'cuz',\n",
       " 435: 'softball',\n",
       " 278: 'lashes',\n",
       " 182: 'floor',\n",
       " 221: 'grocery',\n",
       " 495: 'tutoring',\n",
       " 24: 'bags',\n",
       " 284: 'lesson',\n",
       " 707: '🖤'}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "\n",
       "<div id=\"ldavis_el17431402629258577286740539579\"></div>\n",
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0.9983074069023132, 0.9977020025253296, 0.999656081199646, 0.9982702732086182, 0.9976896047592163, 0.9986777305603027, 0.9993528723716736, 0.9990695118904114, 0.9994587898254395, 0.9990158081054688, 0.9991891980171204, 0.9986245632171631, 0.9984322786331177, 0.99721360206604, 0.9972221851348877, 0.9979507923126221, 0.9976310729980469, 0.9987617135047913, 0.9983502626419067, 0.9978065490722656, 0.8032650947570801, 0.1963973343372345, 0.9957156777381897, 0.9981542825698853, 0.9989053010940552, 0.9966990947723389, 0.9982020258903503, 0.9981406331062317, 0.9983988404273987, 0.9991872906684875, 0.9989469647407532, 0.9986611604690552, 0.9963172078132629, 0.9991181492805481], \"Term\": [\"3\", \"admission\", \"airbnb\", \"airplane\", \"amazon\", \"american\", \"apple\", \"april\", \"arrow\", \"artist\", \"ass\", \"august\", \"automobile\", \"automobile\", \"avocado\", \"away\", \"b\", \"b\", \"baby\", \"baby\", \"babysitting\", \"backhand\", \"bacon\", \"bacon\", \"bag\", \"bagel\", \"bags\", \"baguette\", \"balance\", \"ball\", \"ball\", \"balloon\", \"banana\", \"bank\", \"banknote\", \"bar\", \"baseball\", \"basketball\", \"bday\", \"beach\", \"beaming\", \"bed\", \"beer\", \"bell\", \"bento\", \"best\", \"beverage\", \"biceps\", \"big\", \"big\", \"bills\", \"birthday\", \"bitch\", \"black\", \"blossom\", \"blossom\", \"blowing\", \"blue\", \"board\", \"bone\", \"book\", \"books\", \"booze\", \"bottle\", \"bouquet\", \"bowl\", \"bowling\", \"box\", \"boy\", \"bread\", \"bread\", \"breakfast\", \"bride\", \"broccoli\", \"brunch\", \"bulb\", \"bunny\", \"burrito\", \"button\", \"buy\", \"c\", \"cake\", \"camera\", \"camping\", \"candy\", \"candy\", \"cap\", \"car\", \"card\", \"card\", \"cash\", \"cat\", \"cell\", \"change\", \"cheese\", \"cherry\", \"chick\", \"chicken\", \"chinese\", \"chipotle\", \"chocolate\", \"christmas\", \"class\", \"claus\", \"cleaning\", \"clinking\", \"clinking\", \"clothes\", \"clown\", \"cocktail\", \"computer\", \"confetti\", \"cooked\", \"cookie\", \"cookies\", \"cooking\", \"cork\", \"corn\", \"costco\", \"crab\", \"cream\", \"crying\", \"cup\", \"cut\", \"cuz\", \"d\", \"da\", \"dad\", \"dancing\", \"dark\", \"dashing\", \"day\", \"deposit\", \"din\", \"dinner\", \"dog\", \"dog\", \"dollar\", \"donation\", \"don\\u2019t\", \"doughnut\", \"dress\", \"drink\", \"drinks\", \"droplets\", \"dues\", \"dumpling\", \"e\", \"ears\", \"egg\", \"eggplant\", \"electric\", \"electric\", \"extra\", \"eye\", \"eyes\", \"face\", \"face\", \"face\", \"face\", \"facing\", \"family\", \"fantasy\", \"favorite\", \"fee\", \"female\", \"festival_beer\", \"festival_floaty\", \"finger\", \"fish\", \"fist\", \"flag\", \"flatbread\", \"flexed\", \"floor\", \"flowers\", \"folded\", \"food\", \"football\", \"forgot\", \"fork\", \"fork\", \"french\", \"friday\", \"fried\", \"friend\", \"fries\", \"fuel\", \"fun\", \"fun\", \"fund\", \"g\", \"game\", \"garden\", \"gas\", \"getting\", \"gift\", \"gifts\", \"girl\", \"girl\", \"glass\", \"glass\", \"glasses\", \"good\", \"got\", \"graduation\", \"great\", \"green\", \"green\", \"grinning\", \"groceries\", \"growing\", \"hair\", \"hair\", \"hair\", \"haircut\", \"hamburger\", \"hamburger\", \"hand\", \"hand\", \"handle\", \"hands\", \"hands\", \"happy\", \"hat\", \"head\", \"heart\", \"heart\", \"hearts\", \"hearts\", \"heavy\", \"heavy\", \"help\", \"high\", \"hit\", \"hockey\", \"holding\", \"hole\", \"home\", \"honeybee\", \"horns\", \"horns\", \"horse\", \"hot\", \"hotel\", \"house\", \"house\", \"hugging\", \"ice\", \"index\", \"indicator\", \"insurance\", \"internet\", \"i\\u2019m\", \"jack\", \"jeans\", \"joy\", \"juice\", \"july\", \"july\", \"june\", \"just\", \"just\", \"just\", \"kale\", \"kids\", \"kiss\", \"knife\", \"knife\", \"know\", \"la\", \"lantern\", \"laptop\", \"lashes\", \"lawn\", \"leaf\", \"leaf\", \"left\", \"leg\", \"lesson\", \"letter\", \"life\", \"light\", \"light\", \"like\", \"lipstick\", \"lipstick\", \"little\", \"loan\", \"lol\", \"love\", \"love\", \"lunch\", \"lyft\", \"m\", \"male\", \"man\", \"march\", \"mark\", \"mark\", \"mask\", \"masks\", \"massage\", \"meat\", \"medical\", \"medium\", \"medium\", \"microphone\", \"milk\", \"mobile\", \"mom\", \"money\", \"money\", \"monkey\", \"mouth\", \"movie\", \"mug\", \"mugs\", \"musical\", \"musical\", \"n\", \"n\", \"nail\", \"need\", \"new\", \"night\", \"note\", \"notes\", \"o\", \"o\", \"ok\", \"oncoming\", \"open\", \"palette\", \"palm\", \"pan\", \"pancakes\", \"paper\", \"parking\", \"party\", \"partying\", \"pay\", \"pay\", \"payment\", \"peach\", \"people\", \"pepper\", \"person\", \"person\", \"phone\", \"pig\", \"pig\", \"pile\", \"pill\", \"pizza\", \"place\", \"place\", \"plate\", \"plug\", \"plug\", \"pointing\", \"points\", \"polish\", \"poo\", \"pool\", \"popcorn\", \"popper\", \"popping\", \"pot\", \"potato\", \"poultry\", \"pump\", \"purple\", \"r\", \"raffle\", \"raffle\", \"rainbow\", \"raising\", \"receiver\", \"red\", \"refund\", \"regional\", \"rent\", \"rental\", \"rest\", \"return\", \"rice\", \"right\", \"ring\", \"ring\", \"rocket_popsicle\", \"rolling\", \"rolling\", \"room\", \"s\", \"s\", \"s\", \"sailboat\", \"salad\", \"sandwich\", \"santa\", \"savoring\", \"school\", \"seedling\", \"service\", \"shallow\", \"shipping\", \"shirt\", \"shirts\", \"shit\", \"shoe\", \"shoes\", \"shopping\", \"shortcake\", \"shrimp\", \"shrugging\", \"sign\", \"sitting\", \"skin\", \"slightly\", \"smiling\", \"snowflake\", \"soccer\", \"soft\", \"sorry\", \"spaghetti\", \"sparkles\", \"sparkling\", \"splitwisecom\", \"sport\", \"squares\", \"squares\", \"star\", \"star\", \"starbucks\", \"steaming\", \"straw\", \"strawberry\", \"struck\", \"stuff\", \"stuffed\", \"suit\", \"sun\", \"sunglasses\", \"supplies\", \"sushi\", \"sweat\", \"symbol\", \"t\", \"t\", \"table\", \"taco\", \"target\", \"tax\", \"taxi\", \"tea\", \"teacup\", \"tears\", \"telephone\", \"telephone\", \"television\", \"television\", \"test\", \"thank\", \"thanks\", \"things\", \"thumbs\", \"thx\", \"ticket\", \"tickets\", \"time\", \"tip\", \"tone\", \"tongue\", \"tree\", \"tree\", \"tree\", \"trip\", \"trophy\", \"tropical\", \"tumbler\", \"tv\", \"u\", \"uber\", \"umbrella\", \"unicorn\", \"upside\", \"ur\", \"utilities\", \"utility\", \"vegas\", \"vehicle\", \"veil\", \"venmo\", \"venmo_dollar\", \"video\", \"volleyball\", \"voltage\", \"water\", \"water\", \"wave\", \"way\", \"wedding\", \"wedge\", \"week\", \"weekend\", \"white\", \"wifi\", \"wine\", \"wings\", \"winking\", \"woman\", \"woman\\u2019s\", \"work\", \"wrapped\", \"ya\", \"year\", \"yellow\", \"yo\", \"you\\u2019re\", \"you\\u2019re\", \"zany\", \"\\u200d\", \"\\u2600\", \"\\u260e\", \"\\u2615\", \"\\u2640\", \"\\u2642\", \"\\u2665\", \"\\u26a1\", \"\\u26bd\", \"\\u26be\", \"\\u26f3\", \"\\u26f5\", \"\\u26fd\", \"\\u2708\", \"\\u2728\", \"\\u2744\", \"\\u2764\", \"\\ufe0f\", \"\\ufe0f\", \"\\ufe0f\", \"\\ufe0f\", \"\\ufe0f\", \"\\ufe0f\", \"\\ud83c\\uddf7\", \"\\ud83c\\uddf8\", 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\"\\ud83c\\udf88\", \"\\ud83c\\udf89\", \"\\ud83c\\udf8a\", \"\\ud83c\\udf9f\", \"\\ud83c\\udfa4\", \"\\ud83c\\udfa5\", \"\\ud83c\\udfa8\", \"\\ud83c\\udfab\", \"\\ud83c\\udfae\", \"\\ud83c\\udfb3\", \"\\ud83c\\udfb5\", \"\\ud83c\\udfb6\", \"\\ud83c\\udfc0\", \"\\ud83c\\udfc6\", \"\\ud83c\\udfc8\", \"\\ud83c\\udfcc\", \"\\ud83c\\udfd0\", \"\\ud83c\\udfd6\", \"\\ud83c\\udfe0\", \"\\ud83c\\udfe1\", \"\\ud83c\\udfe8\", \"\\ud83c\\udffb\", \"\\ud83c\\udffc\", \"\\ud83c\\udffd\", \"\\ud83c\\udffe\", \"\\ud83d\\udc14\", \"\\ud83d\\udc15\", \"\\ud83d\\udc1d\", \"\\ud83d\\udc1f\", \"\\ud83d\\udc36\", \"\\ud83d\\udc37\", \"\\ud83d\\udc37\", \"\\ud83d\\udc40\", \"\\ud83d\\udc41\", \"\\ud83d\\udc4c\", \"\\ud83d\\udc4d\", \"\\ud83d\\udc55\", \"\\ud83d\\udc56\", \"\\ud83d\\udc57\", \"\\ud83d\\udc5a\", \"\\ud83d\\udc66\", \"\\ud83d\\udc67\", \"\\ud83d\\udc68\", \"\\ud83d\\udc69\", \"\\ud83d\\udc6f\", \"\\ud83d\\udc70\", \"\\ud83d\\udc76\", \"\\ud83d\\udc76\", \"\\ud83d\\udc83\", \"\\ud83d\\udc84\", \"\\ud83d\\udc84\", \"\\ud83d\\udc85\", \"\\ud83d\\udc86\", \"\\ud83d\\udc87\", \"\\ud83d\\udc8a\", \"\\ud83d\\udc8b\", \"\\ud83d\\udc8d\", \"\\ud83d\\udc8d\", \"\\ud83d\\udc90\", \"\\ud83d\\udc95\", \"\\ud83d\\udc96\", \"\\ud83d\\udc97\", \"\\ud83d\\udc99\", \"\\ud83d\\udc9a\", \"\\ud83d\\udc9b\", \"\\ud83d\\udc9c\", \"\\ud83d\\udca1\", \"\\ud83d\\udca6\", \"\\ud83d\\udca8\", \"\\ud83d\\udca9\", \"\\ud83d\\udcaa\", \"\\ud83d\\udcaf\", \"\\ud83d\\udcb0\", \"\\ud83d\\udcb5\", \"\\ud83d\\udcb8\", \"\\ud83d\\udcbb\", \"\\ud83d\\udcda\", \"\\ud83d\\udcde\", \"\\ud83d\\udcf1\", \"\\ud83d\\udcfa\", \"\\ud83d\\udcfa\", \"\\ud83d\\udd0c\", \"\\ud83d\\udd0c\", \"\\ud83d\\udd19\", \"\\ud83d\\udd25\", \"\\ud83d\\udd25\", \"\\ud83d\\udda4\", \"\\ud83d\\ude01\", \"\\ud83d\\ude02\", \"\\ud83d\\ude09\", \"\\ud83d\\ude0a\", \"\\ud83d\\ude0b\", \"\\ud83d\\ude0d\", \"\\ud83d\\ude0e\", \"\\ud83d\\ude18\", \"\\ud83d\\ude37\", \"\\ud83d\\ude43\", \"\\ud83d\\ude4c\", \"\\ud83d\\ude4f\", \"\\ud83d\\ude95\", \"\\ud83d\\ude97\", \"\\ud83d\\ude98\", \"\\ud83d\\ude99\", \"\\ud83e\\udd11\", \"\\ud83e\\udd17\", \"\\ud83e\\udd21\", \"\\ud83e\\udd29\", \"\\ud83e\\udd2a\", \"\\ud83e\\udd37\", \"\\ud83e\\udd42\", \"\\ud83e\\udd43\", \"\\ud83e\\udd51\", \"\\ud83e\\udd53\", \"\\ud83e\\udd53\", \"\\ud83e\\udd56\", \"\\ud83e\\udd57\", \"\\ud83e\\udd58\", \"\\ud83e\\udd5e\", \"\\ud83e\\udd5f\", \"\\ud83e\\udd64\", \"\\ud83e\\udd69\", \"\\ud83e\\udd6a\", \"\\ud83e\\udd70\", \"\\ud83e\\udd73\", \"\\ud83e\\udd84\", \"\\ud83e\\uddc0\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [12, 4, 11, 3, 2, 10, 15, 9, 18, 19, 1, 14, 16, 17, 20, 7, 13, 6, 5, 8]};\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_el17431402629258577286740539579\", ldavis_el17431402629258577286740539579_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_el17431402629258577286740539579\", ldavis_el17431402629258577286740539579_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_el17431402629258577286740539579\", ldavis_el17431402629258577286740539579_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "11     0.052020 -0.201081       1        1  14.264187\n",
       "3     -0.314033 -0.042591       2        1   7.507633\n",
       "10     0.131235 -0.152563       3        1   6.572530\n",
       "2     -0.000135 -0.144577       4        1   5.539789\n",
       "1      0.095323 -0.244240       5        1   5.107083\n",
       "9      0.091887 -0.030660       6        1   5.084039\n",
       "14    -0.206679  0.010409       7        1   4.863949\n",
       "8      0.042121  0.086974       8        1   4.719287\n",
       "17     0.104874  0.078368       9        1   4.631519\n",
       "18    -0.286136  0.013148      10        1   4.364753\n",
       "0      0.039468  0.067771      11        1   4.305724\n",
       "13     0.000365  0.120246      12        1   4.171197\n",
       "15     0.046219 -0.104360      13        1   4.023326\n",
       "16    -0.182418  0.020988      14        1   3.993521\n",
       "19     0.037636 -0.062210      15        1   3.874284\n",
       "6      0.091476  0.218852      16        1   3.599184\n",
       "12     0.074584  0.039121      17        1   3.594886\n",
       "5      0.050326  0.091156      18        1   3.470204\n",
       "4      0.083497  0.187553      19        1   3.246427\n",
       "7      0.048371  0.047694      20        1   3.066475, topic_info=       Term          Freq         Total Category  logprob  loglift\n",
       "164    face  22346.000000  22346.000000  Default  30.0000  30.0000\n",
       "235   heart   9966.000000   9966.000000  Default  29.0000  29.0000\n",
       "185    food   7818.000000   7818.000000  Default  28.0000  28.0000\n",
       "425    skin  11137.000000  11137.000000  Default  27.0000  27.0000\n",
       "487    tone  11111.000000  11111.000000  Default  26.0000  26.0000\n",
       "..      ...           ...           ...      ...      ...      ...\n",
       "469  teacup    303.708069    304.630737  Topic20  -4.2663   3.4816\n",
       "603       🍵    303.708069    304.630737  Topic20  -4.2663   3.4816\n",
       "584       🍖    299.363617    300.286285  Topic20  -4.2807   3.4816\n",
       "373  potato    298.748016    299.670685  Topic20  -4.2828   3.4816\n",
       "102   class    288.986816    289.909485  Topic20  -4.3160   3.4815\n",
       "\n",
       "[715 rows x 6 columns], token_table=      Topic      Freq       Term\n",
       "term                            \n",
       "0        19  0.998947          3\n",
       "1         6  0.999381  admission\n",
       "2        16  0.996111     airbnb\n",
       "3         5  0.999177   airplane\n",
       "5        16  0.996853     amazon\n",
       "...     ...       ...        ...\n",
       "750      14  0.999187          🥪\n",
       "752      19  0.998947          🥰\n",
       "753      14  0.998661          🥳\n",
       "754       4  0.996317          🦄\n",
       "755      13  0.999118          🧀\n",
       "\n",
       "[723 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[12, 4, 11, 3, 2, 10, 15, 9, 18, 19, 1, 14, 16, 17, 20, 7, 13, 6, 5, 8])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the topics\n",
    "pyLDAvis.enable_notebook()\n",
    "LDAvis_prepared = pyLDAvis.gensim.prepare(lda, corpus, dictionary=id2word2)\n",
    "LDAvis_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/ldavis_defined_286k.pickle', 'wb') as f:\n",
    "    pickle.dump(LDAvis_prepared, f)"
   ]
  }
 ],
 "metadata": {
  "environment": {
   "name": "common-cpu.m54",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m54"
  },
  "kernelspec": {
   "display_name": "Python [conda env:metis] *",
   "language": "python",
   "name": "conda-env-metis-py"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
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 },
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