04E_Modeling_single_demoji_notes.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",
    "single_demoji_notes = pickle.load(open(\"Data/single_demoji_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": [
    "## demoji_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, 445)"
      ]
     },
     "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(single_demoji_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(['admission_tickets', 'airbnb', 'airplane', 'airplane_selector',\n",
       "       'amazon', 'american_football', 'april', 'ass', 'august', 'automobile',\n",
       "       ...\n",
       "       'women_with_bunny_ears_selector', 'work', 'wrapped_gift', 'ya', 'year',\n",
       "       'yellow_heart', 'yo', 'you’re', 'za', 'zany_face'],\n",
       "      dtype='object', length=445)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtm_lsa.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3468016830065389"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "\n",
    "sum(lsa.explained_variance_ratio_[0:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/lsa_single_demoji_100k.pickle', 'wb') as f:\n",
    "    pickle.dump(doc_topic, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff033b450d0>]"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic  0\n",
      "pizza      1.00, hamburger      0.01, eggplant      0.01\n",
      "\n",
      "Topic  1\n",
      "hot_beverage      1.00, cooking      0.02, red_heart_selector      0.00\n",
      "\n",
      "Topic  2\n",
      "money_with_wings      0.83, house      0.50, electric_plug      0.12\n",
      "\n",
      "Topic  3\n",
      "hamburger      1.00, french_fries      0.05, taco      0.05\n",
      "\n",
      "Topic  4\n",
      "food      1.00, chinese      0.02, dog      0.02\n",
      "\n",
      "Topic  5\n",
      "taco      0.86, burrito      0.48, tropical_drink      0.11\n",
      "\n",
      "Topic  6\n",
      "sushi      1.00, bento_box      0.02, eggplant      0.01\n",
      "\n",
      "Topic  7\n",
      "burrito      0.87, sport_utility_vehicle      0.01, red_heart_selector      0.01\n",
      "\n",
      "Topic  8\n",
      "american_football      0.99, fantasy      0.13, football      0.04\n",
      "\n",
      "Topic  9\n",
      "party_popper      0.94, eggplant      0.19, light_skin_tone      0.14\n",
      "\n",
      "Topic  10\n",
      "eggplant      0.84, sweat_droplets      0.41, electric_plug      0.16\n",
      "\n",
      "Topic  11\n",
      "light_skin_tone      0.98, red_heart_selector      0.06, folded_hands_medium      0.05\n",
      "\n",
      "Topic  12\n",
      "electric_plug      0.37, high_voltage      0.37, light_bulb      0.30\n",
      "\n",
      "Topic  13\n",
      "rent      0.95, electric_plug      0.12, high_voltage      0.11\n",
      "\n",
      "Topic  14\n",
      "red_heart_selector      0.98, thanks      0.08, face_blowing_a_kiss      0.06\n",
      "\n",
      "Topic  15\n",
      "beer_mug      0.96, clinking_beer_mugs      0.19, wine_glass      0.11\n",
      "\n",
      "Topic  16\n",
      "basketball      1.00, trophy      0.04, march      0.03\n",
      "\n",
      "Topic  17\n",
      "t      0.79, shirt      0.60, money      0.07\n",
      "\n",
      "Topic  18\n",
      "sweat_droplets      0.55, house      0.38, electric_plug      0.31\n",
      "\n",
      "Topic  19\n",
      "sandwich      0.98, poultry_leg      0.07, thanks      0.06\n",
      "\n",
      "Topic  20\n",
      "thanks      0.92, dollar_banknote      0.16, money      0.11\n",
      "\n",
      "Topic  21\n",
      "wine_glass      0.66, strawberry      0.44, banana      0.35\n",
      "\n",
      "Topic  22\n",
      "strawberry      0.62, banana      0.50, fuel_pump      0.11\n",
      "\n",
      "Topic  23\n",
      "fuel_pump      0.92, high_voltage      0.12, eggplant      0.10\n",
      "\n",
      "Topic  24\n",
      "money      0.56, dollar_banknote      0.50, sweat_droplets      0.41\n",
      "\n",
      "Topic  25\n",
      "money      0.70, high_voltage      0.20, mouth_face      0.19\n",
      "\n",
      "Topic  26\n",
      "thank      0.68, cookie      0.30, eyes      0.19\n",
      "\n",
      "Topic  27\n",
      "cookie      0.54, eyes      0.29, smiling_face_with_heart      0.22\n",
      "\n",
      "Topic  28\n",
      "dollar_banknote      0.45, high_voltage      0.32, house      0.24\n",
      "\n",
      "Topic  29\n",
      "jack      0.49, o      0.48, lantern      0.45\n",
      "\n",
      "Topic  30\n",
      "cookie      0.28, wine_glass      0.23, beer_mug      0.15\n",
      "\n",
      "Topic  31\n",
      "stuff      0.94, good      0.10, money_with_wings      0.07\n",
      "\n",
      "Topic  32\n",
      "wrapped_gift      0.57, birthday      0.33, s      0.30\n",
      "\n",
      "Topic  33\n",
      "poultry_leg      0.65, bread      0.46, wrapped_gift      0.25\n",
      "\n",
      "Topic  34\n",
      "flag_in_hole      0.98, tropical_drink      0.08, cookie      0.06\n",
      "\n",
      "Topic  35\n",
      "face_blowing_a_kiss      0.70, eyes      0.17, love      0.14\n",
      "\n",
      "Topic  36\n",
      "wrapped_gift      0.55, face_blowing_a_kiss      0.31, eyes      0.13\n",
      "\n",
      "Topic  37\n",
      "tropical_drink      0.85, fork_and_knife      0.24, cooking      0.08\n",
      "\n",
      "Topic  38\n",
      "money_bag      0.93, fork_and_knife      0.19, bills      0.17\n",
      "\n",
      "Topic  39\n",
      "s      0.58, uber      0.36, automobile      0.14\n",
      "\n",
      "Topic  40\n",
      "uber      0.75, fork_and_knife      0.60, automobile      0.07\n",
      "\n",
      "Topic  41\n",
      "fork_and_knife      0.69, s      0.25, clinking_beer_mugs      0.08\n",
      "\n",
      "Topic  42\n",
      "american_football      0.13, s      0.12, automobile      0.08\n",
      "\n",
      "Topic  43\n",
      "automobile      0.95, taxi      0.13, airplane_selector      0.10\n",
      "\n",
      "Topic  44\n",
      "eyes      0.60, smiling_face_with_heart      0.42, good      0.20\n",
      "\n",
      "Topic  45\n",
      "good      0.85, chicken      0.37, cookie      0.12\n",
      "\n",
      "Topic  46\n",
      "bills      0.79, splitwisecom      0.35, good      0.16\n",
      "\n",
      "Topic  47\n",
      "chicken      0.77, bills      0.39, splitwisecom      0.17\n",
      "\n",
      "Topic  48\n",
      "bottle_with_popping_cork      0.85, collision      0.11, clinking_glasses      0.10\n",
      "\n",
      "Topic  49\n",
      "gas      0.96, money_bag      0.04, electric      0.03\n",
      "\n",
      "Topic  50\n",
      "venmo_dollar      0.99, gas      0.13, dog_face      0.05\n",
      "\n",
      "Topic  51\n",
      "ticket      0.87, admission_tickets      0.46, movie_camera      0.06\n",
      "\n",
      "Topic  52\n",
      "love      0.89, red_heart      0.26, u      0.08\n",
      "\n",
      "Topic  53\n",
      "dog_face      0.70, pile_of_poo      0.67, red_heart      0.13\n",
      "\n",
      "Topic  54\n",
      "red_heart      0.51, dog_face      0.45, poultry_leg      0.12\n",
      "\n",
      "Topic  55\n",
      "red_heart      0.75, pile_of_poo      0.33, wedding      0.15\n",
      "\n",
      "Topic  56\n",
      "poultry_leg      0.52, steaming_bowl      0.13, french_fries      0.13\n",
      "\n",
      "Topic  57\n",
      "dinner      0.98, night      0.07, steaming_bowl      0.06\n",
      "\n",
      "Topic  58\n",
      "groceries      0.99, car      0.08, phone      0.07\n",
      "\n",
      "Topic  59\n",
      "car      0.92, payment      0.19, phone      0.13\n",
      "\n",
      "Topic  60\n",
      "lunch      0.98, steaming_bowl      0.04, admission_tickets      0.04\n",
      "\n",
      "Topic  61\n",
      "admission_tickets      0.87, baseball      0.09, smiling_face_with_sunglasses      0.08\n",
      "\n",
      "Topic  62\n",
      "baby      0.63, gift      0.56, day      0.33\n",
      "\n",
      "Topic  63\n",
      "steaming_bowl      0.96, cooking      0.09, bread      0.08\n",
      "\n",
      "Topic  64\n",
      "work      0.39, day      0.38, baseball      0.37\n",
      "\n",
      "Topic  65\n",
      "baseball      0.88, baby      0.11, dark_skin_tone      0.11\n",
      "\n",
      "Topic  66\n",
      "smiling_face_with_sunglasses      0.84, baseball      0.19, baby      0.16\n",
      "\n",
      "Topic  67\n",
      "dark_skin_tone      0.98, folded_hands_medium      0.09, work      0.07\n",
      "\n",
      "Topic  68\n",
      "mobile_phone      0.66, phone      0.53, cell      0.15\n",
      "\n",
      "Topic  69\n",
      "baby      0.57, work      0.23, life      0.12\n",
      "\n",
      "Topic  70\n",
      "cooking      0.72, bacon      0.37, cheese_wedge      0.28\n",
      "\n",
      "Topic  71\n",
      "volleyball      0.89, work      0.12, soccer_ball      0.07\n",
      "\n",
      "Topic  72\n",
      "phone      0.47, gift      0.27, laptop_computer      0.24\n",
      "\n",
      "Topic  73\n",
      "mobile_phone      0.33, gift      0.26, work      0.21\n",
      "\n",
      "Topic  74\n",
      "dog      0.94, gift      0.10, face_with_tears_of_joy      0.09\n",
      "\n",
      "Topic  75\n",
      "u      0.84, know      0.33, face_with_tears_of_joy      0.15\n",
      "\n",
      "Topic  76\n",
      "soccer_ball      0.60, high_voltage      0.38, sweat_droplets      0.13\n",
      "\n",
      "Topic  77\n",
      "soccer_ball      0.76, house_with_garden      0.57, electric_plug      0.14\n",
      "\n",
      "Topic  78\n",
      "taxi      0.79, airplane_selector      0.46, sport_utility_vehicle      0.33\n",
      "\n",
      "Topic  79\n",
      "house_with_garden      0.60, high_voltage      0.52, sweat_droplets      0.19\n",
      "\n",
      "Topic  80\n",
      "french_fries      0.90, cup_with_straw      0.18, movie_camera      0.12\n",
      "\n",
      "Topic  81\n",
      "face_with_tears_of_joy      0.96, loudly_crying_face      0.04, ass      0.03\n",
      "\n",
      "Topic  82\n",
      "payment      0.91, deposit      0.19, face_with_tears_of_joy      0.15\n",
      "\n",
      "Topic  83\n",
      "peach      0.97, ok_hand      0.10, french_fries      0.07\n",
      "\n",
      "Topic  84\n",
      "hotel      0.98, deposit      0.07, room      0.03\n",
      "\n",
      "Topic  85\n",
      "fun      0.86, cash      0.31, night      0.19\n",
      "\n",
      "Topic  86\n",
      "deposit      0.93, refund      0.17, wedding      0.11\n",
      "\n",
      "Topic  87\n",
      "movie_camera      0.91, clown_face      0.26, know      0.14\n",
      "\n",
      "Topic  88\n",
      "tumbler_glass      0.87, cocktail_glass      0.25, movie_camera      0.13\n",
      "\n",
      "Topic  89\n",
      "know      0.82, growing_heart      0.18, two_hearts      0.14\n",
      "\n",
      "Topic  90\n",
      "smiling_face_with_3_hearts      0.36, christmas_tree      0.23, fun      0.15\n",
      "\n",
      "Topic  91\n",
      "christmas_tree      0.77, santa_claus      0.28, night      0.26\n",
      "\n",
      "Topic  92\n",
      "smiling_face_with_3_hearts      0.81, night      0.33, partying_face      0.27\n",
      "\n",
      "Topic  93\n",
      "clown_face      0.94, birthday_cake      0.08, night      0.05\n",
      "\n",
      "Topic  94\n",
      "birthday_cake      0.84, cheese_wedge      0.18, tickets      0.15\n",
      "\n",
      "Topic  95\n",
      "tickets      0.70, tip      0.41, week      0.28\n",
      "\n",
      "Topic  96\n",
      "time      0.61, laptop_computer      0.44, week      0.34\n",
      "\n",
      "Topic  97\n",
      "laptop_computer      0.39, tickets      0.29, mobile_phone      0.23\n",
      "\n",
      "Topic  98\n",
      "time      0.57, tickets      0.34, fish      0.17\n",
      "\n",
      "Topic  99\n",
      "tip      0.45, laptop_computer      0.35, mobile_phone      0.20\n",
      "\n",
      "Topic  100\n",
      "festival_beer      0.85, united_states      0.40, musical_notes      0.16\n",
      "\n",
      "Topic  101\n",
      "ok_hand      0.70, beaming_face_with_smiling_eyes      0.54, trip      0.24\n",
      "\n",
      "Topic  102\n",
      "fish      0.89, crab      0.07, tip      0.06\n",
      "\n",
      "Topic  103\n",
      "water      0.66, internet      0.35, electric      0.25\n",
      "\n",
      "Topic  104\n",
      "green_salad      0.55, partying_face      0.54, shallow_pan_of_food      0.28\n",
      "\n",
      "Topic  105\n",
      "two_hearts      0.62, growing_heart      0.44, just      0.23\n",
      "\n",
      "Topic  106\n",
      "united_states      0.72, party      0.18, flexed_biceps      0.13\n",
      "\n",
      "Topic  107\n",
      "airplane_selector      0.49, refund      0.40, green_salad      0.29\n",
      "\n",
      "Topic  108\n",
      "airplane_selector      0.53, partying_face      0.37, sport_utility_vehicle      0.21\n",
      "\n",
      "Topic  109\n",
      "green_salad      0.34, festival_beer      0.21, shallow_pan_of_food      0.18\n",
      "\n",
      "Topic  110\n",
      "party      0.79, birthday      0.21, utilities      0.18\n",
      "\n",
      "Topic  111\n",
      "party      0.29, phone      0.20, two_hearts      0.12\n",
      "\n",
      "Topic  112\n",
      "running_shoe      0.30, just      0.22, july      0.16\n",
      "\n",
      "Topic  113\n",
      "cheese_wedge      0.54, cut_of_meat      0.33, cut      0.22\n",
      "\n",
      "Topic  114\n",
      "ass      0.31, cheese_wedge      0.20, television      0.16\n",
      "\n",
      "Topic  115\n",
      "running_shoe      0.81, television      0.29, golf      0.20\n",
      "\n",
      "Topic  116\n",
      "golf      0.68, just      0.37, trip      0.16\n",
      "\n",
      "Topic  117\n",
      "ass      0.37, just      0.31, birthday      0.20\n",
      "\n",
      "Topic  118\n",
      "july      0.48, television      0.33, ass      0.33\n",
      "\n",
      "Topic  119\n",
      "happy      0.35, just      0.29, bday      0.19\n",
      "\n",
      "Topic  120\n",
      "running_shoe      0.27, fee      0.25, july      0.14\n",
      "\n",
      "Topic  121\n",
      "cup_with_straw      0.43, meat_on_bone      0.18, television      0.16\n",
      "\n",
      "Topic  122\n",
      "cup_with_straw      0.65, poker      0.40, green_salad      0.16\n",
      "\n",
      "Topic  123\n",
      "shallow_pan_of_food      0.62, hundred_points      0.15, internet      0.13\n",
      "\n",
      "Topic  124\n",
      "fee      0.64, shallow_pan_of_food      0.40, electric      0.16\n",
      "\n",
      "Topic  125\n",
      "meat_on_bone      0.49, cut_of_meat      0.47, poker      0.18\n",
      "\n",
      "Topic  126\n",
      "balloon      0.43, trip      0.41, electric      0.26\n",
      "\n",
      "Topic  127\n",
      "balloon      0.62, drinks      0.32, july      0.22\n",
      "\n",
      "Topic  128\n",
      "balloon      0.26, shirt      0.18, poker      0.15\n",
      "\n",
      "Topic  129\n",
      "hundred_points      0.47, smiling_face_with_smiling_eyes      0.32, spaghetti      0.30\n",
      "\n",
      "Topic  130\n",
      "spaghetti      0.43, internet      0.23, person_getting_haircut      0.20\n",
      "\n",
      "Topic  131\n",
      "electric      0.48, cocktail_glass      0.24, drinks      0.22\n",
      "\n",
      "Topic  132\n",
      "cocktail_glass      0.41, internet      0.32, june      0.25\n",
      "\n",
      "Topic  133\n",
      "person_getting_haircut      0.40, cocktail_glass      0.28, oncoming_automobile      0.26\n",
      "\n",
      "Topic  134\n",
      "sport_utility_vehicle      0.43, cocktail_glass      0.23, march      0.19\n",
      "\n",
      "Topic  135\n",
      "cocktail_glass      0.29, smiling_face_with_smiling_eyes      0.27, pay      0.19\n",
      "\n",
      "Topic  136\n",
      "smiling_face_with_smiling_eyes      0.35, sport_utility_vehicle      0.31, internet      0.26\n",
      "\n",
      "Topic  137\n",
      "person_getting_haircut      0.61, bday      0.24, cleaning      0.13\n",
      "\n",
      "Topic  138\n",
      "shirt      0.38, trip      0.31, cocktail_glass      0.28\n",
      "\n",
      "Topic  139\n",
      "pay      0.43, big      0.26, fork_and_knife_with_plate      0.18\n",
      "\n",
      "Topic  140\n",
      "din      0.37, n      0.36, june      0.24\n",
      "\n",
      "Topic  141\n",
      "smiling_face_with_smiling_eyes      0.37, insurance      0.29, best      0.26\n",
      "\n",
      "Topic  142\n",
      "best      0.26, star      0.26, oncoming_automobile      0.24\n",
      "\n",
      "Topic  143\n",
      "oncoming_automobile      0.42, june      0.27, star      0.22\n",
      "\n",
      "Topic  144\n",
      "march      0.30, star      0.26, struck      0.22\n",
      "\n",
      "Topic  145\n",
      "cleaning      0.29, insurance      0.25, cash      0.24\n",
      "\n",
      "Topic  146\n",
      "bday      0.28, woman_dancing      0.27, beer      0.23\n",
      "\n",
      "Topic  147\n",
      "bacon      0.29, fork_and_knife_with_plate      0.25, musical_notes      0.24\n",
      "\n",
      "Topic  148\n",
      "big      0.39, face_with_medical_mask      0.33, bacon      0.20\n",
      "\n",
      "Topic  149\n",
      "woman_dancing      0.45, fork_and_knife_with_plate      0.37, march      0.26\n"
     ]
    }
   ],
   "source": [
    "display_topics(lsa, vectorizer.get_feature_names(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff033b0d160>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZS54nr5JPuuyu7wTG9oggFbGYIxKN4Zzj3d97mht+vclzvw37mrPiMDTb9Ydtw0lWi0MlP0/ak4I7lnRe5kQCPRZz9Pj4KDH5jbOupeek7qs3aapir1+mLZrZcjPbYWa1ZnaLx3Yzsx8ntm82s3PT39T0S35HTX4xpTptsf/F0NzVx76mrhNuRygcZf3eZu5es5u/uPWZQdVUNOYoLQgAcKg1/uRrTPMLPxWvH2zl+v+3kVA4yrW/3MBFP3iOw4nq5k/b6o/bf09DJ59c+RLf+cPW47btOtbBD57czod//PxAuGw60DJsFXmyLv/RWq69f0NK+8Zijt+8coCO0PBBt+lAC/Xtgyu7PQ2d3PbU9hM6euqfvzxcUREKR/nm797gQNPbG5JJnsHRmhTcHUknQk8k0G9/egdnfutJ375Z9w9ZVpUXDrymTtTQk6LhaGzQBYBPbjnKM9vG79/TjRroZhYA7gSuAJYCnzKzpUN2uwJYnPi6Drg7ze0csLmulS89UHPcCy4Wc/xx61HaPKrX5q4+fvT0zkFVRXsozK5jnQPLw54UHaEtTYkXTHNXH7vr3wr0t/uiTm7HkbYQbxxqG2hPOBpjRkUR5YVvfexO/5vPvz7+JsvvWMtZ//yUZwC9drCV3792aMTHDoWjgw5Bh/ON373Bk1uP8uz2ep7b0cDR9hDrahuP2+97j73JvS/sZe3OBgA27Bs8BBUKR/ngj9Zy95rdbD3czq76TrYdaedjd63jXx57c8Q29EaitPWEufb+DbxR18YvXtzLUY9D5iNtPQNh0xEKs+NYB89sr8e5+JHFxv3Nw/6N1uys5xu/e4Nbnzj+aAnih+sfu2sdn/zZS4PWX/XTF7nzud0pVdk1+5q5+aHNA1MGjyV+h4YO78P/57bX8+tXDvDdx45/cxwqufpuSQ70pNdFcoh7vV5Gc+dz8Vkzmw6kPry4/Wj7wO/rnOOxzYcHDXMOtWZHPZvrRp49VtfSzcfuepHa+o5B66NJWXCkrYdbHt486Pfsf74vmVnOoZM8EZ18MVFPX4RLblvD1x7aDMT/Ftf/aiPX/rJm3IZJU/lwrvOBWufcHgAzWwWsAJJffSuAB1y81S+b2WQzm+WcS+/HmRF/F3z6zWO889t/5Nx5k/nuirMIBvK4f91eHlx/kCmlBfz71efQ1Rsl5hzV8yu54Teb2LCvhf+sOcgV75zF/KklAx/Ok2dQVpjPqvUHWTKznMOtIZ7aenTg8Z7YcoQ/bj3KpgMtLJxWyl+ffQoHm7uZW1lCS+IM+RNbjvD4G2/9qg+8tJ9QOEr1gkoONvdQVpjP7oZOTq0qoygYoC8aJT8vj/yAgYMXhgTjx+6KD9ssm1/Jxv0tLJ5exsKqUjbXtQHwYm0TX/jFetbsaBj4mVsefoPJJUHOWzCF3716iJ3HOgbGB/+8s4FI1PGRd5/CC7uamDWpiDNmltMeCvPDp3fS3NXHTZctpjcSY/bkYvY1drF+XzOHWnu45sIFFAYDA0cF/f0G8NXEExfg5T1NNHb2cu8LewE4fUYZALX1nazZUc95C6bQ1Nl33BvM5XesZdG0UgB+9coB3ndGFZGoY+OBFh7ZdIgvv+9UqsoLOdzaw+1P76S8MJ+mrj6e2R4/KrjzuVru/PS59EVjRGKOssJ8PrnyJSpLgvzwb87hzSPtA49115rd7G3s4qGNdaw45xS+tnwJj28+zKr1B/n7S09jRkUhD2+Mt2/93mae39VAJObo7o1SXpRPzDme3BJ/buxv6mZdbSPhmCOYZwOH8S/tbqKiKIjDcbg1hBlMKg5SHAzQEYpwqLWH257aAcB/1Bzk0xfMoyPxsw+uP8jrB9swgwtPnUpenvGeRVN55NV4m/60rZ5vP7qV9yyaSnsozI6jHQQDeXzmgnk0dPayv6mLu57bTWVpAVedfQr/9F9bBn73P207RmNnL6FwlN0NbxUf96zdwzvnTGLT/hZeqG3k5uVL2H60g1A4yhkzy+kIRXhiyxGuOns2eTa46r/3+b1EY46Ygz+9eYy6lm4uXTKdRdPK2Li/ha2H2/jEsjms3dXAr14+wKTiIH9/yamEwjF+9KedFObnsXhGGUtnVTC1rJBYzDFvaglH20L85NlaioJ5XLBwKhXFQW66bDHFBQF+u+EgHz93Dl19Ef718W1sOtDKR+9ax7985CzaesJMKS3g1y8f4KU9TZQX5tMdjhKNORo7+1g6q5w5lSWsfuMIeQanTS/j+V2NvJh4/R1q7aFmXzOvH2zj8rNm8o5TKjjY3M3uhk7mVJZQVpjPM9vr6QiFmTWpiEvPmE59UiH442drAXh4Ux1/efq0QVNC71+3jzNmlNPSHaalu48zZ5WzbP4U0s1Ge+cws08Ay51zX0wsfxa4wDl3Y9I+jwH/5px7IbH8DHCzc65muPutrq52NTXDbh7RZ+99hed3NTKtrGDQ7JSCQB7FBYERDyMDeTbokOjixdMoLcjnyaQQB7jx0tP46XO1J9S+dDtvQSULppbyn4nZEKkoKQikNBsmP8+oKA4ed4FFaUFg0HxdgDNnVbAtEZBFwbxRL3X+5LI5PLSpDudg3pQSjrWHBt40nvjHi/n4XevYlTgvYBZvy3h8vMH8qSUcaO7O+FXA/eZOKaalK/y2T4qaxYcE+7+/HUNfB5kWyDMCZvQlqvj3njaVl3Y3kdzEypLgwEyv0cyeXDzicMp/f98ils6q4KZVr51QeyuK8oedw18cDNAzynDUly5eyDc/PHSgIzVmttE5V+21LZUK3TzWDX0mpLIPZnYd8SEZ5s2bl8JDe/u/n6smFI6y9XA7n/n5KwD85ksX8I5TJhEKR9l+tIOSggBNnX08tfUol505nfMWTCEcjTGpOMhDG+t415xJRGOwqKqUycVBjrSFaOnuY1FVGY0dvcybUsJFi6cxa1IRMyqKiDnHhn0t5CUqruauPiYVB1kys4KdxzqYUVFEMGA0dPbSF4kxc1IRa3Y0MLk4yNSyAqrKiqhr6SaYn0cwkEckGqMvGiPPjDwzTplcRGF+gN5IlCNtIZbNqyTmHK/XtTK3soSiggAfXzaHxdPL6AlH2dvYRfX8KRxrD1FWlE9vJEZpQYD9Td10hCJceOrUgSd0a3eYytIgtfWdnDu/kgNN3YTCUYqCAU6bXkYwkMeOox3MmlTEvqYuppQWMH9qKY2dvTy8sW5g+OH7Hz2LaWWFdPdFObWqlB3HOphZUcTrda0cag1x+vQyZk0qZsvhNk6fUcZp08u56QOLefVAK7eu3kZhfh7fueodXHbmDCqKgjx0/YVYHuxp6GJRVSmhcJQDTd0UBQNMryhkSkkB+5u7iSWqwPlTS9hyqI3TZ5bTGYqQZ8ah1h7ae8KUFARoD0X40gPxIuF/fuh0LlpcRX6eMb2ikPaeMF29UV7e08RV55zCjb95lY37W7jynTO57RNns+VQG4E8Iy/PWDStlF31nRjxoCnMD9DZGyGQZ4BjSmkhexs7KcoPUBjMIxx11NZ3DlTEX738DC5YOIXyRKUeiToiMUd5UT7Tyws50hZi8fQyOnsjrNvdxLL5lbx6oJXdDZ0sf8dM5k0pIeYcR9tD1Hf0UlIQYEHi79HZG6EzFKGsKH+g2uw/xP9v1XP56vIzKMzPY+exDmZOKubF2saB7Y/9w0UUFwQoDgaoKA7S0xelsbOXlu4+5k8t5bHXD3PrE9upKMrn0RsvoqmrD+cc86eWsrexi4L8PKaWFtARirBwWilvHmnHOYcZVBQFmVxSQHsoTENHL6UF+cyoKORgSw9FwTzmTinBgMOtIXoj8ep/17FOZk8uprm7j6ryQsoK8jnWEaKlK8ySmeUcbQ9RFAzQ1NnLV377Oo2dvbx/SSXPJo7OHvi78zlvwRT2NHYSjjpmTSriSFuI0oL487q2vpOC/DymlRXSEYpQHAxwqLWH8qJ85lQWY2acM3cyR9pCGFBZWkB5UT5dvRF6IzFisfhrPT8QPwIrL8qnMD/Ahn3NfPDMGdQ2dNIRCjO9vIiC/DzqWro5bXo5hfl57G3soi8SY9bkIo62hejpixJzUFkapLKkgMklwRPOv5GkUqH/BfBt59zlieWvAzjnbk3a52fAGufcg4nlHcAlIw25nEyF3i8Wc1zyf9bwoaUz+Ke/OrF3OxlZfXuI87//DABbv3M5pYUn9hH6PX1ReiNRJpcUpLN5g8RijkXfWA3A6v9xMUtPqRh23x89vZN/f2YX932hmvcvmXHSj72/qYv33bYGgP+47j1csGjqSd9nKrYfbWf5Hc8D8TeSGy497bh9HtpYx8WLpzGjoiil+7rirJnc/bfLxqS9JyocjRFzjsc3H+Erv30dgL23XomZVy2Z2062Qt8ALDazhcAh4Grg00P2eRS4MTG+fgHQNhbj50Pl5Rlrv3ZpVs3LzjVV5YUDt080zIF4ZZiYrTNW8vLeenHPm1oy4r43vv80zp47iUvPmJ6Wx55c/NYb1bSkPhtrZUl/k7Jh/j6fWDYnpftaMrOCuz9zLu9dPC0tbUunYCA+f2NB4nwLMCHDfDSjvkKdcxEzuxF4CggA9znntprZ9YntK4HVwJVALdANXDN2TT6e/rBjx8y4bMn0Ma2s0+nUqlJ2N3QNG279goG8tFTm/cqL3nq8qnEM9PLCtw7dR/udU3HFO2ed9H2MpYVTS0ffaQJL6RngnFtNPLST161Muu2AG9LbNMkW937hvEw3IWW/u+G9JzQV72QlHx2UpyFYU1WW9EaSfDtXVZYWMKOikM9fuCDTTclKuf8MkAmloihIRdHYnHBK1XgeMQYy9EaSSa984wOZbkLWmvCX/ovkiolQocvI9AwQSZOff66avAyWSOkYQxd/0zNAJE0+sDR9J1lPhCp00ZCLSI5InvEiE5MCXSRHFAX1cp7o9AwQyRG6HkM06Cbicw9/+UK2H20ffUfJeQp0EZ9bNr+SZfMrM90MyQIachERyREKdBGRHKFAFxHJEQp0EZEcoUAXEckRCnQRkRyhQBcRyREKdBGRHDHqP4keswc2awD2n+CPTwMa09icXKA+OZ76ZDD1x/H82CfznXNVXhsyFugnw8xqhvuv1xOV+uR46pPB1B/Hy7U+0ZCLiEiOUKCLiOQIvwb6PZluQBZSnxxPfTKY+uN4OdUnvhxDFxGR4/m1QhcRkSF8F+hmttzMdphZrZndkun2jBczu8/M6s1sS9K6KWb2tJntSnyvTNr29UQf7TCzyzPT6rFjZnPN7Dkz22ZmW83spsT6CdknZlZkZuvN7PVEf3wnsX5C9kc/MwuY2atm9lhiObf7wznnmy8gAOwGFgEFwOvA0ky3a5x+978EzgW2JK3738Atidu3AD9I3F6a6JtCYGGizwKZ/h3S3B+zgHMTt8uBnYnfe0L2CWBAWeJ2EHgFeM9E7Y+kfvkK8BvgscRyTveH3yr084Fa59we51wfsApYkeE2jQvn3FqgecjqFcAvE7d/CXwkaf0q51yvc24vUEu873KGc+6Ic25T4nYHsA2YzQTtExfXmVgMJr4cE7Q/AMxsDvBh4OdJq3O6P/wW6LOBg0nLdYl1E9UM59wRiAccMD2xfkL1k5ktAN5NvCqdsH2SGF54DagHnnbOTej+AO4AvgbEktbldH/4LdC9/q25pukcb8L0k5mVAQ8D/+icG+k/Jed8nzjnos65c4A5wPlmdtYIu+d0f5jZXwH1zrmNqf6Ixzrf9YffAr0OmJu0PAc4nKG2ZINjZjYLIPG9PrF+QvSTmQWJh/mvnXOPJFZP6D4BcM61AmuA5Uzc/ngvcJWZ7SM+NPt+M/sVOd4ffgv0DcBiM1toZgXA1cCjGW5TJj0KfD5x+/PA75PWX21mhWa2EFgMrM9A+8aMmRlwL7DNOffDpE0Tsk/MrMrMJiduFwMfALYzQfvDOfd159wc59wC4jnxrHPub8n1/sj0Wdm3+wVcSXxGw27gm5luzzj+3g8CR4Aw8WriWmAq8AywK/F9StL+30z00Q7giky3fwz64yLih8SbgdcSX1dO1D4B3gW8muiPLcC3EusnZH8M6ZtLeGuWS073h64UFRHJEX4bchERkWEo0EVEcoQCXUQkRyjQRURyhAJdRCRHKNBFRKJu+YwAAAATSURBVHKEAl1EJEco0EVEcsT/B3HePLTjp6iCAAAAAElFTkSuQmCC\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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmf_model = NMF(20)\n",
    "nmf_doc_topic = nmf_model.fit_transform(dtm[:100000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/nmf_single_demoji_100k.pickle', 'wb') as f:\n",
    "    pickle.dump(nmf_doc_topic, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic  0\n",
      "pizza      9.18, poultry_leg      0.04, french_fries      0.03\n",
      "\n",
      "Topic  1\n",
      "hot_beverage      8.42, cooking      0.13, coffee      0.02\n",
      "\n",
      "Topic  2\n",
      "money_with_wings      7.73, mobile_phone      1.02, laptop_computer      0.93\n",
      "\n",
      "Topic  3\n",
      "hamburger      7.42, french_fries      0.37, back_arrow      0.15\n",
      "\n",
      "Topic  4\n",
      "food      7.31, chinese      0.14, dog      0.11\n",
      "\n",
      "Topic  5\n",
      "taco      7.76, tropical_drink      0.99, bell      0.19\n",
      "\n",
      "Topic  6\n",
      "sushi      6.93, bento_box      0.11, water_wave      0.08\n",
      "\n",
      "Topic  7\n",
      "burrito      6.42, sport_utility_vehicle      0.09, cookie      0.06\n",
      "\n",
      "Topic  8\n",
      "american_football      6.67, fantasy      0.85, football      0.26\n",
      "\n",
      "Topic  9\n",
      "party_popper      6.58, birthday_cake      0.79, wine_glass      0.55\n",
      "\n",
      "Topic  10\n",
      "eggplant      6.36, sweat_droplets      1.77, peach      0.41\n",
      "\n",
      "Topic  11\n",
      "light_skin_tone      6.13, wine_glass      0.34, folded_hands_medium      0.33\n",
      "\n",
      "Topic  12\n",
      "house      8.02, money_with_wings      0.44, stuff      0.10\n",
      "\n",
      "Topic  13\n",
      "rent      6.08, july      0.26, june      0.21\n",
      "\n",
      "Topic  14\n",
      "red_heart_selector      5.88, thanks      0.47, face_blowing_a_kiss      0.33\n",
      "\n",
      "Topic  15\n",
      "beer_mug      5.71, clinking_beer_mugs      1.09, wine_glass      0.58\n",
      "\n",
      "Topic  16\n",
      "basketball      5.81, trophy      0.23, march      0.20\n",
      "\n",
      "Topic  17\n",
      "t      4.52, shirt      3.46, money      0.38\n",
      "\n",
      "Topic  18\n",
      "electric_plug      3.74, high_voltage      3.37, light_bulb      2.52\n",
      "\n",
      "Topic  19\n",
      "sandwich      5.58, poultry_leg      0.38, shallow_pan_of_food      0.31\n"
     ]
    }
   ],
   "source": [
    "display_topics(nmf_model, vectorizer.get_feature_names(), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tdm = dtm.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_counts = scipy.sparse.csr_matrix(tdm)\n",
    "corpus = matutils.Sparse2Corpus(sparse_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim.corpora as corpora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "id2word2 = corpora.Dictionary([[k for k,v in vectorizer.vocabulary_.items()]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('b', 12), ('food', 160), ('person_getting_haircut', 293), ('bowling', 54), ('teacup_without_handle', 380), ('good', 184), ('bro', 59), ('fuel_pump', 171), ('woman_dancing', 431), ('taco', 374), ('avocado', 10), ('know', 232), ('tickets', 392), ('folded_hands_medium', 159), ('light_skin_tone', 243), ('uber', 407), ('got', 186), ('ya', 438), ('cash', 72), ('money', 263), ('poultry_leg', 306), ('chicken', 80), ('ticket', 391), ('popcorn', 303), ('airbnb', 1), ('hot_pepper', 211), ('hundred_points', 216), ('sweat_droplets', 371), ('lunch', 251), ('cocktail_glass', 92), ('smiling_face_with_3_hearts', 345), ('birthday_cake', 44), ('party_popper', 287), ('burrito', 62), ('eggplant', 135), ('beer_mug', 37), ('groceries', 191), ('partying_face', 288), ('video_game', 416), ('two_hearts', 404), ('o', 276), ('s', 325), ('mouth_face', 266), ('hamburger', 198), ('spaghetti', 355), ('pizza', 298), ('clothes', 90), ('red_heart_selector', 314), ('eye', 140), ('face_with_tears_of_joy', 145), ('i’m', 221), ('hot', 208), ('house', 213), ('pile_of_poo', 296), ('cup_with_straw', 106), ('dog', 121), ('tv', 403), ('phone', 294), ('dollar_banknote', 123), ('money_with_wings', 265), ('mobile_phone', 261), ('hi', 203), ('hot_beverage', 209), ('flag_in_hole', 155), ('fork_and_knife', 164), ('tropical_drink', 399), ('march', 256), ('rent', 316), ('kids', 230), ('january', 223), ('make', 254), ('gift', 179), ('happy', 199), ('birthday', 43), ('bitch', 45), ('girl', 181), ('stuff', 366), ('thanks', 387), ('night', 275), ('bento_box', 39), ('things', 388), ('girls', 182), ('parking', 285), ('sushi', 370), ('squares', 360), ('hotel', 212), ('ring', 321), ('forgot', 163), ('lol', 248), ('thank', 386), ('test', 384), ('baseball', 28), ('game', 176), ('sport_utility_vehicle', 359), ('poker', 301), ('man', 255), ('refund', 315), ('july', 226), ('gas', 177), ('electric', 136), ('love', 250), ('late', 237), ('fees', 150), ('water', 418), ('school', 330), ('la', 233), ('wifi', 425), ('present', 307), ('little', 246), ('golf', 183), ('face_savoring_food', 143), ('g', 175), ('fantasy', 147), ('american_football', 5), ('growing_heart', 193), ('red_heart', 313), ('dinner', 120), ('yo', 441), ('thumbs_up', 389), ('class', 86), ('face_blowing_a_kiss', 142), ('payment', 290), ('trophy', 398), ('new', 274), ('year', 439), ('hot_dog', 210), ('venmo_dollar', 415), ('n', 271), ('bbq', 30), ('back_arrow', 18), ('party', 286), ('fund', 173), ('drinks', 130), ('bad', 20), ('wings', 428), ('fee', 149), ('bar', 27), ('u', 406), ('christmas', 84), ('day', 116), ('raffle', 310), ('fun', 172), ('ty', 405), ('cookies', 98), ('shortcake', 341), ('musical_note', 269), ('coffee', 93), ('table', 373), ('flexed_biceps', 156), ('united_states', 409), ('life', 241), ('taxi', 378), ('airplane_selector', 3), ('white_medium_star', 424), ('bed', 35), ('automobile', 9), ('clinking_beer_mugs', 88), ('beer', 36), ('festival_beer', 151), ('heart_suit_selector', 200), ('lessons', 240), ('best', 40), ('friend', 169), ('collision', 94), ('water_wave', 419), ('bottle_with_popping_cork', 51), ('cheese_wedge', 78), ('musical_notes', 270), ('help', 201), ('bills', 42), ('dad', 113), ('volleyball', 417), ('fish', 154), ('bread', 56), ('woman_dancing_medium', 433), ('high_voltage', 205), ('shirts', 338), ('smiling_face_with_smiling_eyes', 347), ('ok_hand', 278), ('time', 393), ('basketball', 29), ('favorite', 148), ('bus', 63), ('service', 333), ('sandwich', 328), ('candy', 67), ('hey', 202), ('da', 112), ('jack', 222), ('wine_glass', 427), ('chinese', 81), ('fuck', 170), ('dog_face', 122), ('upside', 410), ('down_face', 127), ('chocolate_bar', 83), ('soft_ice_cream', 352), ('house_with_garden', 214), ('eyes', 141), ('haircut', 196), ('strawberry', 364), ('t', 372), ('shirt', 337), ('costco', 100), ('june', 227), ('utilities', 412), ('room', 323), ('cleaning', 87), ('supplies', 369), ('movie', 267), ('ice', 217), ('cat', 73), ('great', 188), ('way', 420), ('internet', 219), ('work', 436), ('flowers', 157), ('blossom', 47), ('money_bag', 264), ('admission_tickets', 0), ('brunch', 61), ('weekend', 423), ('bank', 26), ('today', 396), ('juice', 225), ('cream', 103), ('car', 69), ('vegas', 413), ('santa_claus', 329), ('christmas_tree', 85), ('festive_holly', 153), ('babysitting', 17), ('movie_camera', 268), ('plus', 300), ('red_apple', 312), ('za', 443), ('baby', 13), ('sitting', 343), ('football', 162), ('mom', 262), ('week', 422), ('shower', 342), ('smiling_face_with_sunglasses', 348), ('super', 368), ('goods', 185), ('electric_plug', 137), ('festival_floaty', 152), ('honeybee', 207), ('light_bulb', 242), ('home', 206), ('boy', 55), ('need', 273), ('credit_card', 104), ('baby_light_skin_tone', 15), ('bagel', 21), ('wrapped_gift', 437), ('ghost', 178), ('doughnut', 126), ('fried_shrimp', 168), ('wedding', 421), ('peach', 291), ('books', 49), ('chipotle', 82), ('sex', 334), ('it’s', 220), ('pig_face', 295), ('winking_face', 429), ('card', 70), ('august', 8), ('tip', 394), ('big', 41), ('clown_face', 91), ('french_fries', 166), ('dues', 132), ('wine', 426), ('pay', 289), ('like', 244), ('thai', 385), ('pumpkin_spice_latte', 308), ('microphone', 260), ('drooling_face', 131), ('clinking_glasses', 89), ('cooked_rice', 96), ('cooking', 99), ('rest', 318), ('insurance', 218), ('airplane', 2), ('december', 117), ('october', 277), ('people_with_bunny_ears', 292), ('april', 6), ('potato', 305), ('oncoming_automobile', 279), ('tax', 377), ('return', 319), ('shit', 339), ('half', 197), ('steaming_bowl', 363), ('smiling_face_selector', 344), ('donation', 124), ('face_with_medical_mask', 144), ('shallow_pan_of_food', 335), ('foods', 161), ('high', 204), ('unicorn_face', 408), ('woman_dancing_light_skin_tone', 432), ('beach', 32), ('crab', 102), ('cause', 74), ('hair', 195), ('deposit', 118), ('venmo', 414), ('balance', 23), ('you’re', 442), ('cut_of_meat', 109), ('amazon', 4), ('sorry', 354), ('thx', 390), ('meat_on_bone', 258), ('pot_of_food', 304), ('lantern', 234), ('guitar', 194), ('kale', 229), ('salad', 327), ('send', 332), ('fork_and_knife_with_plate', 165), ('balloon', 24), ('camping', 66), ('kiss_mark', 231), ('telephone_receiver', 382), ('television', 383), ('paid', 282), ('sailboat', 326), ('bride_with_veil', 58), ('beach_with_umbrella', 33), ('target', 376), ('gifts', 180), ('sun_selector', 367), ('bouquet', 52), ('running_shoe', 324), ('jeans', 224), ('blue_heart', 48), ('pill', 297), ('shoes', 340), ('soccer_ball', 351), ('merry', 259), ('yellow_heart', 440), ('purple_heart', 309), ('shipping', 336), ('star', 361), ('struck', 365), ('palm_tree', 283), ('just', 228), ('loan', 247), ('ass', 7), ('m', 253), ('ur', 411), ('bday', 31), ('lyft', 252), ('extra', 139), ('baby_bottle', 14), ('grapes', 187), ('baby_medium', 16), ('bacon', 19), ('cowboy_hat_face', 101), ('tumbler_glass', 400), ('zany_face', 444), ('pancakes', 284), ('ride', 320), ('change', 76), ('egg', 134), ('dress', 128), ('dumpling', 133), ('splitwisecom', 358), ('baguette_bread', 22), ('seedling', 331), ('trip', 397), ('curry_rice', 107), ('cookie', 97), ('turkey', 401), ('broccoli', 60), ('green_salad', 190), ('bowl', 53), ('tix', 395), ('breakfast', 57), ('confetti_ball', 95), ('don’t', 125), ('drink', 129), ('folded_hands', 158), ('lawn', 238), ('evergreen_tree', 138), ('care', 71), ('women_with_bunny_ears_selector', 435), ('laptop_computer', 235), ('cell', 75), ('loudly_crying_face', 249), ('pool', 302), ('rental', 317), ('smiling_face_with_heart', 346), ('buy', 64), ('rocket_popsicle', 322), ('awesome', 11), ('sparkles', 356), ('dark_skin_tone', 114), ('package', 281), ('banana', 25), ('telephone', 381), ('p_button', 280), ('starbucks', 362), ('tacos', 375), ('din', 119), ('cut', 108), ('nail_polish', 272), ('soap', 350), ('cherry_blossom', 79), ('hugging_face', 215), ('bell', 38), ('family', 146), ('winking_face_with_tongue', 430), ('masks', 257), ('snacks', 349), ('tea', 379), ('candy_heart', 68), ('crown', 105), ('lipstick', 245), ('beaming_face_with_smiling_eyes', 34), ('dashing_away', 115), ('camera', 65), ('friday', 167), ('place', 299), ('d', 111), ('check', 77), ('green_heart', 189), ('rainbow', 311), ('woman’s_clothes', 434), ('booze', 50), ('sparkling_heart', 357), ('cuz', 110), ('softball', 353), ('lashes', 236), ('grocery', 192), ('fundraiser', 174), ('tutoring', 402), ('lesson', 239), ('black_heart', 46)])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.vocabulary_.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "id2word = dict((v, k) for k, v in vectorizer.vocabulary_.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21:38:04\n",
      "21:51:54\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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/lda_single_demoji_286k.pickle', 'wb') as f:\n",
    "    pickle.dump(lda, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.245*\"thanks\" + 0.098*\"car\" + 0.079*\"bills\" + 0.075*\"cleaning\" + 0.066*\"cocktail_glass\" + 0.045*\"n\" + 0.043*\"june\" + 0.043*\"star\" + 0.036*\"struck\" + 0.035*\"folded_hands\"'),\n",
       " (1,\n",
       "  '0.202*\"face_blowing_a_kiss\" + 0.167*\"stuff\" + 0.090*\"smiling_face_with_smiling_eyes\" + 0.073*\"deposit\" + 0.059*\"know\" + 0.056*\"splitwisecom\" + 0.055*\"wedding\" + 0.037*\"pot_of_food\" + 0.036*\"bills\" + 0.036*\"dumpling\"'),\n",
       " (2,\n",
       "  '0.223*\"hot_beverage\" + 0.122*\"dinner\" + 0.079*\"basketball\" + 0.040*\"lipstick\" + 0.040*\"red_apple\" + 0.037*\"strawberry\" + 0.037*\"cowboy_hat_face\" + 0.035*\"hot_pepper\" + 0.032*\"fried_shrimp\" + 0.032*\"dues\"'),\n",
       " (3,\n",
       "  '0.142*\"tropical_drink\" + 0.139*\"fork_and_knife\" + 0.138*\"money\" + 0.122*\"lunch\" + 0.042*\"mouth_face\" + 0.042*\"books\" + 0.039*\"fantasy\" + 0.039*\"festival_beer\" + 0.031*\"big\" + 0.029*\"movie\"'),\n",
       " (4,\n",
       "  '0.161*\"american_football\" + 0.116*\"bottle_with_popping_cork\" + 0.063*\"red_heart\" + 0.056*\"week\" + 0.043*\"electric\" + 0.043*\"winking_face\" + 0.039*\"help\" + 0.037*\"weekend\" + 0.033*\"cherry_blossom\" + 0.031*\"water_wave\"'),\n",
       " (5,\n",
       "  '0.457*\"pizza\" + 0.121*\"uber\" + 0.087*\"poultry_leg\" + 0.054*\"person_getting_haircut\" + 0.050*\"clinking_glasses\" + 0.048*\"cheese_wedge\" + 0.048*\"flag_in_hole\" + 0.027*\"cut_of_meat\" + 0.025*\"parking\" + 0.025*\"you’re\"'),\n",
       " (6,\n",
       "  '0.080*\"high_voltage\" + 0.079*\"light_bulb\" + 0.066*\"hair\" + 0.064*\"electric_plug\" + 0.060*\"night\" + 0.055*\"fun\" + 0.052*\"eggplant\" + 0.050*\"just\" + 0.047*\"tumbler_glass\" + 0.046*\"life\"'),\n",
       " (7,\n",
       "  '0.130*\"hamburger\" + 0.112*\"sushi\" + 0.111*\"automobile\" + 0.101*\"two_hearts\" + 0.095*\"french_fries\" + 0.051*\"u\" + 0.048*\"best\" + 0.045*\"shallow_pan_of_food\" + 0.044*\"sparkles\" + 0.035*\"fee\"'),\n",
       " (8,\n",
       "  '0.231*\"red_heart_selector\" + 0.152*\"rent\" + 0.064*\"dog_face\" + 0.055*\"dog\" + 0.055*\"smiling_face_with_3_hearts\" + 0.036*\"refund\" + 0.035*\"face_with_tears_of_joy\" + 0.032*\"fork_and_knife_with_plate\" + 0.029*\"heart_suit_selector\" + 0.027*\"water\"'),\n",
       " (9,\n",
       "  '0.137*\"beer_mug\" + 0.113*\"t\" + 0.107*\"shirt\" + 0.063*\"steaming_bowl\" + 0.054*\"tip\" + 0.048*\"tickets\" + 0.044*\"time\" + 0.038*\"mom\" + 0.035*\"cookies\" + 0.034*\"sun_selector\"'),\n",
       " (10,\n",
       "  '0.221*\"light_skin_tone\" + 0.139*\"wrapped_gift\" + 0.092*\"fuel_pump\" + 0.064*\"ticket\" + 0.051*\"house_with_garden\" + 0.047*\"christmas_tree\" + 0.037*\"payment\" + 0.035*\"sandwich\" + 0.028*\"musical_notes\" + 0.026*\"utilities\"'),\n",
       " (11,\n",
       "  '0.116*\"baby\" + 0.108*\"cookie\" + 0.088*\"gas\" + 0.066*\"phone\" + 0.049*\"bday\" + 0.046*\"face_savoring_food\" + 0.038*\"new\" + 0.036*\"shower\" + 0.033*\"girl\" + 0.033*\"back_arrow\"'),\n",
       " (12,\n",
       "  '0.191*\"taco\" + 0.090*\"birthday\" + 0.084*\"happy\" + 0.068*\"dollar_banknote\" + 0.055*\"money_bag\" + 0.052*\"baseball\" + 0.045*\"movie_camera\" + 0.033*\"shortcake\" + 0.032*\"purple_heart\" + 0.032*\"dress\"'),\n",
       " (13,\n",
       "  '0.179*\"party_popper\" + 0.123*\"clinking_beer_mugs\" + 0.091*\"groceries\" + 0.090*\"sweat_droplets\" + 0.081*\"birthday_cake\" + 0.040*\"drinks\" + 0.037*\"spaghetti\" + 0.036*\"zany_face\" + 0.035*\"peach\" + 0.032*\"doughnut\"'),\n",
       " (14,\n",
       "  '0.111*\"eyes\" + 0.072*\"cooking\" + 0.066*\"green_salad\" + 0.063*\"smiling_face_with_heart\" + 0.062*\"coffee\" + 0.061*\"cash\" + 0.042*\"sorry\" + 0.039*\"march\" + 0.033*\"way\" + 0.033*\"bitch\"'),\n",
       " (15,\n",
       "  '0.303*\"thank\" + 0.126*\"s\" + 0.095*\"burrito\" + 0.053*\"party\" + 0.041*\"o\" + 0.039*\"jack\" + 0.033*\"lantern\" + 0.030*\"ice\" + 0.028*\"costco\" + 0.026*\"amazon\"'),\n",
       " (16,\n",
       "  '0.447*\"food\" + 0.157*\"wine_glass\" + 0.107*\"mobile_phone\" + 0.093*\"chicken\" + 0.071*\"bread\" + 0.037*\"work\" + 0.022*\"pig_face\" + 0.021*\"drooling_face\" + 0.021*\"clown_face\" + 0.013*\"chinese\"'),\n",
       " (17,\n",
       "  '0.322*\"money_with_wings\" + 0.303*\"house\" + 0.052*\"admission_tickets\" + 0.043*\"trip\" + 0.041*\"soft_ice_cream\" + 0.041*\"laptop_computer\" + 0.041*\"beaming_face_with_smiling_eyes\" + 0.036*\"dark_skin_tone\" + 0.029*\"i’m\" + 0.028*\"chocolate_bar\"'),\n",
       " (18,\n",
       "  '0.099*\"gift\" + 0.077*\"good\" + 0.058*\"partying_face\" + 0.051*\"pile_of_poo\" + 0.048*\"nail_polish\" + 0.048*\"smiling_face_with_sunglasses\" + 0.046*\"face_with_medical_mask\" + 0.044*\"sport_utility_vehicle\" + 0.042*\"wifi\" + 0.039*\"bacon\"'),\n",
       " (19,\n",
       "  '0.131*\"love\" + 0.083*\"day\" + 0.072*\"venmo_dollar\" + 0.057*\"hotel\" + 0.051*\"babysitting\" + 0.049*\"taxi\" + 0.044*\"cup_with_straw\" + 0.040*\"july\" + 0.039*\"hugging_face\" + 0.036*\"lol\"')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.print_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import CoherenceModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.562780163937201"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.log_perplexity(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coherence Score:  -15.892743312435845\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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis.gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{12: 'b',\n",
       " 160: 'food',\n",
       " 293: 'person_getting_haircut',\n",
       " 54: 'bowling',\n",
       " 380: 'teacup_without_handle',\n",
       " 184: 'good',\n",
       " 59: 'bro',\n",
       " 171: 'fuel_pump',\n",
       " 431: 'woman_dancing',\n",
       " 374: 'taco',\n",
       " 10: 'avocado',\n",
       " 232: 'know',\n",
       " 392: 'tickets',\n",
       " 159: 'folded_hands_medium',\n",
       " 243: 'light_skin_tone',\n",
       " 407: 'uber',\n",
       " 186: 'got',\n",
       " 438: 'ya',\n",
       " 72: 'cash',\n",
       " 263: 'money',\n",
       " 306: 'poultry_leg',\n",
       " 80: 'chicken',\n",
       " 391: 'ticket',\n",
       " 303: 'popcorn',\n",
       " 1: 'airbnb',\n",
       " 211: 'hot_pepper',\n",
       " 216: 'hundred_points',\n",
       " 371: 'sweat_droplets',\n",
       " 251: 'lunch',\n",
       " 92: 'cocktail_glass',\n",
       " 345: 'smiling_face_with_3_hearts',\n",
       " 44: 'birthday_cake',\n",
       " 287: 'party_popper',\n",
       " 62: 'burrito',\n",
       " 135: 'eggplant',\n",
       " 37: 'beer_mug',\n",
       " 191: 'groceries',\n",
       " 288: 'partying_face',\n",
       " 416: 'video_game',\n",
       " 404: 'two_hearts',\n",
       " 276: 'o',\n",
       " 325: 's',\n",
       " 266: 'mouth_face',\n",
       " 198: 'hamburger',\n",
       " 355: 'spaghetti',\n",
       " 298: 'pizza',\n",
       " 90: 'clothes',\n",
       " 314: 'red_heart_selector',\n",
       " 140: 'eye',\n",
       " 145: 'face_with_tears_of_joy',\n",
       " 221: 'i’m',\n",
       " 208: 'hot',\n",
       " 213: 'house',\n",
       " 296: 'pile_of_poo',\n",
       " 106: 'cup_with_straw',\n",
       " 121: 'dog',\n",
       " 403: 'tv',\n",
       " 294: 'phone',\n",
       " 123: 'dollar_banknote',\n",
       " 265: 'money_with_wings',\n",
       " 261: 'mobile_phone',\n",
       " 203: 'hi',\n",
       " 209: 'hot_beverage',\n",
       " 155: 'flag_in_hole',\n",
       " 164: 'fork_and_knife',\n",
       " 399: 'tropical_drink',\n",
       " 256: 'march',\n",
       " 316: 'rent',\n",
       " 230: 'kids',\n",
       " 223: 'january',\n",
       " 254: 'make',\n",
       " 179: 'gift',\n",
       " 199: 'happy',\n",
       " 43: 'birthday',\n",
       " 45: 'bitch',\n",
       " 181: 'girl',\n",
       " 366: 'stuff',\n",
       " 387: 'thanks',\n",
       " 275: 'night',\n",
       " 39: 'bento_box',\n",
       " 388: 'things',\n",
       " 182: 'girls',\n",
       " 285: 'parking',\n",
       " 370: 'sushi',\n",
       " 360: 'squares',\n",
       " 212: 'hotel',\n",
       " 321: 'ring',\n",
       " 163: 'forgot',\n",
       " 248: 'lol',\n",
       " 386: 'thank',\n",
       " 384: 'test',\n",
       " 28: 'baseball',\n",
       " 176: 'game',\n",
       " 359: 'sport_utility_vehicle',\n",
       " 301: 'poker',\n",
       " 255: 'man',\n",
       " 315: 'refund',\n",
       " 226: 'july',\n",
       " 177: 'gas',\n",
       " 136: 'electric',\n",
       " 250: 'love',\n",
       " 237: 'late',\n",
       " 150: 'fees',\n",
       " 418: 'water',\n",
       " 330: 'school',\n",
       " 233: 'la',\n",
       " 425: 'wifi',\n",
       " 307: 'present',\n",
       " 246: 'little',\n",
       " 183: 'golf',\n",
       " 143: 'face_savoring_food',\n",
       " 175: 'g',\n",
       " 147: 'fantasy',\n",
       " 5: 'american_football',\n",
       " 193: 'growing_heart',\n",
       " 313: 'red_heart',\n",
       " 120: 'dinner',\n",
       " 441: 'yo',\n",
       " 389: 'thumbs_up',\n",
       " 86: 'class',\n",
       " 142: 'face_blowing_a_kiss',\n",
       " 290: 'payment',\n",
       " 398: 'trophy',\n",
       " 274: 'new',\n",
       " 439: 'year',\n",
       " 210: 'hot_dog',\n",
       " 415: 'venmo_dollar',\n",
       " 271: 'n',\n",
       " 30: 'bbq',\n",
       " 18: 'back_arrow',\n",
       " 286: 'party',\n",
       " 173: 'fund',\n",
       " 130: 'drinks',\n",
       " 20: 'bad',\n",
       " 428: 'wings',\n",
       " 149: 'fee',\n",
       " 27: 'bar',\n",
       " 406: 'u',\n",
       " 84: 'christmas',\n",
       " 116: 'day',\n",
       " 310: 'raffle',\n",
       " 172: 'fun',\n",
       " 405: 'ty',\n",
       " 98: 'cookies',\n",
       " 341: 'shortcake',\n",
       " 269: 'musical_note',\n",
       " 93: 'coffee',\n",
       " 373: 'table',\n",
       " 156: 'flexed_biceps',\n",
       " 409: 'united_states',\n",
       " 241: 'life',\n",
       " 378: 'taxi',\n",
       " 3: 'airplane_selector',\n",
       " 424: 'white_medium_star',\n",
       " 35: 'bed',\n",
       " 9: 'automobile',\n",
       " 88: 'clinking_beer_mugs',\n",
       " 36: 'beer',\n",
       " 151: 'festival_beer',\n",
       " 200: 'heart_suit_selector',\n",
       " 240: 'lessons',\n",
       " 40: 'best',\n",
       " 169: 'friend',\n",
       " 94: 'collision',\n",
       " 419: 'water_wave',\n",
       " 51: 'bottle_with_popping_cork',\n",
       " 78: 'cheese_wedge',\n",
       " 270: 'musical_notes',\n",
       " 201: 'help',\n",
       " 42: 'bills',\n",
       " 113: 'dad',\n",
       " 417: 'volleyball',\n",
       " 154: 'fish',\n",
       " 56: 'bread',\n",
       " 433: 'woman_dancing_medium',\n",
       " 205: 'high_voltage',\n",
       " 338: 'shirts',\n",
       " 347: 'smiling_face_with_smiling_eyes',\n",
       " 278: 'ok_hand',\n",
       " 393: 'time',\n",
       " 29: 'basketball',\n",
       " 148: 'favorite',\n",
       " 63: 'bus',\n",
       " 333: 'service',\n",
       " 328: 'sandwich',\n",
       " 67: 'candy',\n",
       " 202: 'hey',\n",
       " 112: 'da',\n",
       " 222: 'jack',\n",
       " 427: 'wine_glass',\n",
       " 81: 'chinese',\n",
       " 170: 'fuck',\n",
       " 122: 'dog_face',\n",
       " 410: 'upside',\n",
       " 127: 'down_face',\n",
       " 83: 'chocolate_bar',\n",
       " 352: 'soft_ice_cream',\n",
       " 214: 'house_with_garden',\n",
       " 141: 'eyes',\n",
       " 196: 'haircut',\n",
       " 364: 'strawberry',\n",
       " 372: 't',\n",
       " 337: 'shirt',\n",
       " 100: 'costco',\n",
       " 227: 'june',\n",
       " 412: 'utilities',\n",
       " 323: 'room',\n",
       " 87: 'cleaning',\n",
       " 369: 'supplies',\n",
       " 267: 'movie',\n",
       " 217: 'ice',\n",
       " 73: 'cat',\n",
       " 188: 'great',\n",
       " 420: 'way',\n",
       " 219: 'internet',\n",
       " 436: 'work',\n",
       " 157: 'flowers',\n",
       " 47: 'blossom',\n",
       " 264: 'money_bag',\n",
       " 0: 'admission_tickets',\n",
       " 61: 'brunch',\n",
       " 423: 'weekend',\n",
       " 26: 'bank',\n",
       " 396: 'today',\n",
       " 225: 'juice',\n",
       " 103: 'cream',\n",
       " 69: 'car',\n",
       " 413: 'vegas',\n",
       " 329: 'santa_claus',\n",
       " 85: 'christmas_tree',\n",
       " 153: 'festive_holly',\n",
       " 17: 'babysitting',\n",
       " 268: 'movie_camera',\n",
       " 300: 'plus',\n",
       " 312: 'red_apple',\n",
       " 443: 'za',\n",
       " 13: 'baby',\n",
       " 343: 'sitting',\n",
       " 162: 'football',\n",
       " 262: 'mom',\n",
       " 422: 'week',\n",
       " 342: 'shower',\n",
       " 348: 'smiling_face_with_sunglasses',\n",
       " 368: 'super',\n",
       " 185: 'goods',\n",
       " 137: 'electric_plug',\n",
       " 152: 'festival_floaty',\n",
       " 207: 'honeybee',\n",
       " 242: 'light_bulb',\n",
       " 206: 'home',\n",
       " 55: 'boy',\n",
       " 273: 'need',\n",
       " 104: 'credit_card',\n",
       " 15: 'baby_light_skin_tone',\n",
       " 21: 'bagel',\n",
       " 437: 'wrapped_gift',\n",
       " 178: 'ghost',\n",
       " 126: 'doughnut',\n",
       " 168: 'fried_shrimp',\n",
       " 421: 'wedding',\n",
       " 291: 'peach',\n",
       " 49: 'books',\n",
       " 82: 'chipotle',\n",
       " 334: 'sex',\n",
       " 220: 'it’s',\n",
       " 295: 'pig_face',\n",
       " 429: 'winking_face',\n",
       " 70: 'card',\n",
       " 8: 'august',\n",
       " 394: 'tip',\n",
       " 41: 'big',\n",
       " 91: 'clown_face',\n",
       " 166: 'french_fries',\n",
       " 132: 'dues',\n",
       " 426: 'wine',\n",
       " 289: 'pay',\n",
       " 244: 'like',\n",
       " 385: 'thai',\n",
       " 308: 'pumpkin_spice_latte',\n",
       " 260: 'microphone',\n",
       " 131: 'drooling_face',\n",
       " 89: 'clinking_glasses',\n",
       " 96: 'cooked_rice',\n",
       " 99: 'cooking',\n",
       " 318: 'rest',\n",
       " 218: 'insurance',\n",
       " 2: 'airplane',\n",
       " 117: 'december',\n",
       " 277: 'october',\n",
       " 292: 'people_with_bunny_ears',\n",
       " 6: 'april',\n",
       " 305: 'potato',\n",
       " 279: 'oncoming_automobile',\n",
       " 377: 'tax',\n",
       " 319: 'return',\n",
       " 339: 'shit',\n",
       " 197: 'half',\n",
       " 363: 'steaming_bowl',\n",
       " 344: 'smiling_face_selector',\n",
       " 124: 'donation',\n",
       " 144: 'face_with_medical_mask',\n",
       " 335: 'shallow_pan_of_food',\n",
       " 161: 'foods',\n",
       " 204: 'high',\n",
       " 408: 'unicorn_face',\n",
       " 432: 'woman_dancing_light_skin_tone',\n",
       " 32: 'beach',\n",
       " 102: 'crab',\n",
       " 74: 'cause',\n",
       " 195: 'hair',\n",
       " 118: 'deposit',\n",
       " 414: 'venmo',\n",
       " 23: 'balance',\n",
       " 442: 'you’re',\n",
       " 109: 'cut_of_meat',\n",
       " 4: 'amazon',\n",
       " 354: 'sorry',\n",
       " 390: 'thx',\n",
       " 258: 'meat_on_bone',\n",
       " 304: 'pot_of_food',\n",
       " 234: 'lantern',\n",
       " 194: 'guitar',\n",
       " 229: 'kale',\n",
       " 327: 'salad',\n",
       " 332: 'send',\n",
       " 165: 'fork_and_knife_with_plate',\n",
       " 24: 'balloon',\n",
       " 66: 'camping',\n",
       " 231: 'kiss_mark',\n",
       " 382: 'telephone_receiver',\n",
       " 383: 'television',\n",
       " 282: 'paid',\n",
       " 326: 'sailboat',\n",
       " 58: 'bride_with_veil',\n",
       " 33: 'beach_with_umbrella',\n",
       " 376: 'target',\n",
       " 180: 'gifts',\n",
       " 367: 'sun_selector',\n",
       " 52: 'bouquet',\n",
       " 324: 'running_shoe',\n",
       " 224: 'jeans',\n",
       " 48: 'blue_heart',\n",
       " 297: 'pill',\n",
       " 340: 'shoes',\n",
       " 351: 'soccer_ball',\n",
       " 259: 'merry',\n",
       " 440: 'yellow_heart',\n",
       " 309: 'purple_heart',\n",
       " 336: 'shipping',\n",
       " 361: 'star',\n",
       " 365: 'struck',\n",
       " 283: 'palm_tree',\n",
       " 228: 'just',\n",
       " 247: 'loan',\n",
       " 7: 'ass',\n",
       " 253: 'm',\n",
       " 411: 'ur',\n",
       " 31: 'bday',\n",
       " 252: 'lyft',\n",
       " 139: 'extra',\n",
       " 14: 'baby_bottle',\n",
       " 187: 'grapes',\n",
       " 16: 'baby_medium',\n",
       " 19: 'bacon',\n",
       " 101: 'cowboy_hat_face',\n",
       " 400: 'tumbler_glass',\n",
       " 444: 'zany_face',\n",
       " 284: 'pancakes',\n",
       " 320: 'ride',\n",
       " 76: 'change',\n",
       " 134: 'egg',\n",
       " 128: 'dress',\n",
       " 133: 'dumpling',\n",
       " 358: 'splitwisecom',\n",
       " 22: 'baguette_bread',\n",
       " 331: 'seedling',\n",
       " 397: 'trip',\n",
       " 107: 'curry_rice',\n",
       " 97: 'cookie',\n",
       " 401: 'turkey',\n",
       " 60: 'broccoli',\n",
       " 190: 'green_salad',\n",
       " 53: 'bowl',\n",
       " 395: 'tix',\n",
       " 57: 'breakfast',\n",
       " 95: 'confetti_ball',\n",
       " 125: 'don’t',\n",
       " 129: 'drink',\n",
       " 158: 'folded_hands',\n",
       " 238: 'lawn',\n",
       " 138: 'evergreen_tree',\n",
       " 71: 'care',\n",
       " 435: 'women_with_bunny_ears_selector',\n",
       " 235: 'laptop_computer',\n",
       " 75: 'cell',\n",
       " 249: 'loudly_crying_face',\n",
       " 302: 'pool',\n",
       " 317: 'rental',\n",
       " 346: 'smiling_face_with_heart',\n",
       " 64: 'buy',\n",
       " 322: 'rocket_popsicle',\n",
       " 11: 'awesome',\n",
       " 356: 'sparkles',\n",
       " 114: 'dark_skin_tone',\n",
       " 281: 'package',\n",
       " 25: 'banana',\n",
       " 381: 'telephone',\n",
       " 280: 'p_button',\n",
       " 362: 'starbucks',\n",
       " 375: 'tacos',\n",
       " 119: 'din',\n",
       " 108: 'cut',\n",
       " 272: 'nail_polish',\n",
       " 350: 'soap',\n",
       " 79: 'cherry_blossom',\n",
       " 215: 'hugging_face',\n",
       " 38: 'bell',\n",
       " 146: 'family',\n",
       " 430: 'winking_face_with_tongue',\n",
       " 257: 'masks',\n",
       " 349: 'snacks',\n",
       " 379: 'tea',\n",
       " 68: 'candy_heart',\n",
       " 105: 'crown',\n",
       " 245: 'lipstick',\n",
       " 34: 'beaming_face_with_smiling_eyes',\n",
       " 115: 'dashing_away',\n",
       " 65: 'camera',\n",
       " 167: 'friday',\n",
       " 299: 'place',\n",
       " 111: 'd',\n",
       " 77: 'check',\n",
       " 189: 'green_heart',\n",
       " 311: 'rainbow',\n",
       " 434: 'woman’s_clothes',\n",
       " 50: 'booze',\n",
       " 357: 'sparkling_heart',\n",
       " 110: 'cuz',\n",
       " 353: 'softball',\n",
       " 236: 'lashes',\n",
       " 192: 'grocery',\n",
       " 174: 'fundraiser',\n",
       " 402: 'tutoring',\n",
       " 239: 'lesson',\n",
       " 46: 'black_heart'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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0.9974917769432068, 0.998323917388916, 0.9966093897819519, 0.9963530898094177, 0.9986441135406494], \"Term\": [\"admission_tickets\", \"airbnb\", \"airplane\", \"airplane_selector\", \"amazon\", \"american_football\", \"april\", \"ass\", \"august\", \"august\", \"automobile\", \"avocado\", \"awesome\", \"b\", \"baby\", \"baby_bottle\", \"baby_light_skin_tone\", \"baby_medium\", \"babysitting\", \"back_arrow\", \"bacon\", \"bad\", \"bagel\", \"baguette_bread\", \"balance\", \"balloon\", \"banana\", \"bank\", \"bar\", \"baseball\", \"basketball\", \"bbq\", \"bday\", \"beach\", \"beach_with_umbrella\", \"beaming_face_with_smiling_eyes\", \"bed\", \"beer\", \"beer_mug\", \"bell\", \"bento_box\", \"bento_box\", \"best\", \"big\", \"bills\", \"bills\", \"birthday\", \"birthday_cake\", \"bitch\", \"black_heart\", \"blossom\", \"blue_heart\", \"blue_heart\", \"books\", \"booze\", \"bottle_with_popping_cork\", \"bouquet\", \"bowl\", \"bowling\", \"boy\", \"bread\", \"breakfast\", \"bride_with_veil\", \"bro\", \"broccoli\", \"brunch\", \"burrito\", \"bus\", \"buy\", \"camera\", \"camping\", \"candy\", \"candy_heart\", \"car\", \"card\", \"care\", \"cash\", \"cat\", \"cause\", \"cell\", \"change\", \"check\", \"cheese_wedge\", \"cheese_wedge\", \"cherry_blossom\", \"chicken\", \"chinese\", \"chipotle\", \"chocolate_bar\", \"christmas\", \"christmas\", \"christmas_tree\", \"class\", \"cleaning\", \"clinking_beer_mugs\", \"clinking_glasses\", \"clinking_glasses\", \"clothes\", \"clown_face\", \"cocktail_glass\", \"coffee\", \"collision\", \"collision\", \"confetti_ball\", \"cooked_rice\", \"cookie\", \"cookies\", \"cooking\", \"cooking\", \"costco\", \"cowboy_hat_face\", \"crab\", \"cream\", \"credit_card\", \"crown\", \"cup_with_straw\", \"curry_rice\", \"cut\", \"cut_of_meat\", \"cuz\", \"d\", \"da\", \"dad\", \"dark_skin_tone\", \"dashing_away\", \"day\", \"december\", \"deposit\", \"din\", \"dinner\", \"dog\", \"dog_face\", \"dollar_banknote\", \"dollar_banknote\", \"donation\", \"don\\u2019t\", \"doughnut\", \"down_face\", \"dress\", \"drink\", \"drinks\", \"drooling_face\", \"dues\", \"dumpling\", \"egg\", \"eggplant\", \"eggplant\", \"electric\", \"electric_plug\", \"electric_plug\", \"evergreen_tree\", \"extra\", \"eye\", \"eyes\", \"face_blowing_a_kiss\", \"face_savoring_food\", \"face_with_medical_mask\", \"face_with_tears_of_joy\", \"family\", \"fantasy\", \"fantasy\", \"favorite\", \"fee\", \"fees\", \"festival_beer\", \"festival_floaty\", \"festive_holly\", \"fish\", \"flag_in_hole\", \"flexed_biceps\", \"flowers\", \"folded_hands\", \"folded_hands_medium\", \"food\", \"foods\", \"football\", \"forgot\", \"fork_and_knife\", \"fork_and_knife_with_plate\", \"french_fries\", \"friday\", \"fried_shrimp\", \"friend\", \"fuck\", \"fuel_pump\", \"fun\", \"fun\", \"fund\", \"fundraiser\", \"g\", \"game\", \"gas\", \"ghost\", \"ghost\", \"gift\", \"gifts\", \"girl\", \"girl\", \"girls\", \"golf\", \"good\", \"goods\", \"got\", \"grapes\", \"great\", \"green_heart\", \"green_salad\", \"groceries\", \"grocery\", \"growing_heart\", \"guitar\", \"hair\", \"haircut\", \"half\", \"hamburger\", \"happy\", \"heart_suit_selector\", \"help\", \"hey\", \"hi\", \"high\", \"high_voltage\", \"home\", \"honeybee\", \"hot\", \"hot_beverage\", \"hot_dog\", \"hot_dog\", \"hot_pepper\", \"hotel\", \"house\", \"house_with_garden\", \"hugging_face\", \"hundred_points\", \"ice\", \"insurance\", \"insurance\", \"internet\", \"it\\u2019s\", \"i\\u2019m\", \"jack\", \"january\", \"jeans\", \"juice\", \"july\", \"june\", \"just\", \"kale\", \"kids\", \"kiss_mark\", \"know\", \"la\", \"lantern\", \"laptop_computer\", \"lashes\", \"late\", \"lawn\", \"lawn\", \"lesson\", \"lessons\", \"life\", \"light_bulb\", \"light_skin_tone\", \"like\", \"lipstick\", \"little\", \"loan\", \"lol\", \"loudly_crying_face\", \"love\", \"lunch\", \"lyft\", \"m\", \"make\", \"man\", \"march\", \"masks\", \"meat_on_bone\", \"merry\", \"microphone\", \"mobile_phone\", \"mom\", \"money\", \"money_bag\", \"money_with_wings\", \"money_with_wings\", \"mouth_face\", \"movie\", \"movie_camera\", \"musical_note\", \"musical_notes\", \"n\", \"nail_polish\", \"need\", \"need\", \"new\", \"night\", \"o\", \"october\", \"ok_hand\", \"oncoming_automobile\", \"p_button\", \"package\", \"paid\", \"palm_tree\", \"pancakes\", \"parking\", \"party\", \"party_popper\", \"partying_face\", \"pay\", \"payment\", \"peach\", \"people_with_bunny_ears\", \"person_getting_haircut\", \"phone\", \"pig_face\", \"pile_of_poo\", \"pill\", \"pizza\", \"place\", \"plus\", \"poker\", \"pool\", \"popcorn\", \"pot_of_food\", \"potato\", \"poultry_leg\", \"poultry_leg\", \"present\", \"pumpkin_spice_latte\", \"purple_heart\", \"purple_heart\", \"raffle\", \"rainbow\", \"red_apple\", \"red_heart\", \"red_heart_selector\", \"refund\", \"rent\", \"rental\", \"rest\", \"return\", \"ride\", \"ring\", \"ring\", \"rocket_popsicle\", \"room\", \"running_shoe\", \"s\", \"sailboat\", \"salad\", \"sandwich\", \"santa_claus\", \"school\", \"seedling\", \"send\", \"service\", \"sex\", \"shallow_pan_of_food\", \"shipping\", \"shirt\", \"shirts\", \"shit\", \"shoes\", \"shortcake\", \"shower\", \"sitting\", \"smiling_face_selector\", \"smiling_face_with_3_hearts\", \"smiling_face_with_heart\", \"smiling_face_with_smiling_eyes\", \"smiling_face_with_sunglasses\", \"snacks\", \"soap\", \"soccer_ball\", \"soft_ice_cream\", \"softball\", \"sorry\", \"spaghetti\", \"sparkles\", \"sparkling_heart\", \"splitwisecom\", \"sport_utility_vehicle\", \"squares\", \"squares\", \"star\", \"starbucks\", \"steaming_bowl\", \"strawberry\", \"struck\", \"stuff\", \"sun_selector\", \"super\", \"supplies\", \"sushi\", \"sweat_droplets\", \"t\", \"table\", \"taco\", \"tacos\", \"target\", \"tax\", \"taxi\", \"tea\", \"teacup_without_handle\", \"telephone\", \"telephone_receiver\", \"television\", \"television\", \"test\", \"thai\", \"thank\", \"thanks\", \"things\", \"things\", \"thumbs_up\", \"thx\", \"ticket\", \"tickets\", \"time\", \"tip\", \"tix\", \"today\", \"trip\", \"trophy\", \"trophy\", \"tropical_drink\", \"tumbler_glass\", \"turkey\", \"tutoring\", \"tv\", \"two_hearts\", \"ty\", \"u\", \"u\", \"uber\", \"unicorn_face\", \"united_states\", \"upside\", \"ur\", \"utilities\", \"vegas\", \"venmo\", \"venmo_dollar\", \"video_game\", \"volleyball\", \"water\", \"water_wave\", \"way\", \"wedding\", \"week\", \"weekend\", \"white_medium_star\", \"white_medium_star\", \"wifi\", \"wine\", \"wine_glass\", \"wings\", \"winking_face\", \"winking_face_with_tongue\", \"woman_dancing\", \"woman_dancing_light_skin_tone\", \"woman_dancing_medium\", \"woman\\u2019s_clothes\", \"women_with_bunny_ears_selector\", \"work\", \"wrapped_gift\", \"ya\", \"year\", \"year\", \"yellow_heart\", \"yo\", \"you\\u2019re\", \"za\", \"zany_face\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [11, 9, 13, 14, 18, 10, 8, 12, 6, 20, 17, 16, 7, 19, 15, 4, 3, 5, 2, 1]};\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_el24791406676056944965183393414\", ldavis_el24791406676056944965183393414_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_el24791406676056944965183393414\", ldavis_el24791406676056944965183393414_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_el24791406676056944965183393414\", ldavis_el24791406676056944965183393414_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster      Freq\n",
       "topic                                               \n",
       "10     0.025743  0.014657       1        1  6.281919\n",
       "8      0.015340 -0.097664       2        1  5.907653\n",
       "12     0.088645  0.106939       3        1  5.718300\n",
       "13    -0.175211  0.013655       4        1  5.327684\n",
       "17    -0.275292 -0.015472       5        1  5.325665\n",
       "9      0.055238 -0.049981       6        1  5.254226\n",
       "7      0.033418  0.049596       7        1  5.137000\n",
       "11     0.081708  0.069884       8        1  5.077379\n",
       "5      0.073865  0.116561       9        1  5.074123\n",
       "19     0.031856  0.049343      10        1  5.003810\n",
       "16     0.050579  0.051079      11        1  4.958625\n",
       "15     0.033461  0.039587      12        1  4.842322\n",
       "6     -0.341946 -0.009707      13        1  4.828021\n",
       "18     0.029488  0.029794      14        1  4.825402\n",
       "14    -0.064229  0.026745      15        1  4.570802\n",
       "3      0.059309  0.072948      16        1  4.553135\n",
       "2      0.027600  0.001937      17        1  4.391288\n",
       "4      0.091169  0.140538      18        1  4.376206\n",
       "1      0.081096 -0.300314      19        1  4.294273\n",
       "0      0.078163 -0.310125      20        1  4.252170, topic_info=                       Term         Freq        Total Category  logprob  \\\n",
       "298                   pizza  6651.000000  6651.000000  Default  30.0000   \n",
       "160                    food  6357.000000  6357.000000  Default  29.0000   \n",
       "265        money_with_wings  5441.000000  5441.000000  Default  28.0000   \n",
       "213                   house  4626.000000  4626.000000  Default  27.0000   \n",
       "386                   thank  4205.000000  4205.000000  Default  26.0000   \n",
       "..                      ...          ...          ...      ...      ...   \n",
       "149                     fee     0.052753   512.535889  Topic20 -12.3515   \n",
       "150                    fees     0.052753   203.222137  Topic20 -12.3515   \n",
       "151           festival_beer     0.052753   512.849731  Topic20 -12.3515   \n",
       "152         festival_floaty     0.052753   477.650665  Topic20 -12.3515   \n",
       "144  face_with_medical_mask     0.052753   639.018311  Topic20 -12.3515   \n",
       "\n",
       "     loglift  \n",
       "298  30.0000  \n",
       "160  29.0000  \n",
       "265  28.0000  \n",
       "213  27.0000  \n",
       "386  26.0000  \n",
       "..       ...  \n",
       "149  -6.0238  \n",
       "150  -5.0987  \n",
       "151  -6.0244  \n",
       "152  -5.9533  \n",
       "144  -6.2443  \n",
       "\n",
       "[751 rows x 6 columns], token_table=      Topic      Freq               Term\n",
       "term                                    \n",
       "0         5  0.999212  admission_tickets\n",
       "1         8  0.996502             airbnb\n",
       "2         3  0.994643           airplane\n",
       "3        13  0.999256  airplane_selector\n",
       "4        12  0.998047             amazon\n",
       "...     ...       ...                ...\n",
       "440      10  0.997492       yellow_heart\n",
       "441       7  0.998324                 yo\n",
       "442       9  0.996609             you’re\n",
       "443       8  0.996353                 za\n",
       "444       4  0.998644          zany_face\n",
       "\n",
       "[476 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[11, 9, 13, 14, 18, 10, 8, 12, 6, 20, 17, 16, 7, 19, 15, 4, 3, 5, 2, 1])"
      ]
     },
     "execution_count": 34,
     "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": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/ldavis_single_demoji_286k.pickle', 'wb') as f:\n",
    "    pickle.dump(LDAvis_prepared, f)"
   ]
  }
 ],
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   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m54"
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  "kernelspec": {
   "display_name": "Python [conda env:metis] *",
   "language": "python",
   "name": "conda-env-metis-py"
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