Topic Modeling Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f4e0176b",
   "metadata": {},
   "source": [
    "# Final Project (Topic Modeling Analysis)\n",
    "### By: Gabe Owens\n",
    "#### Data:  Disneyland Reviews: Reviews and Ratings of 3 Disneyland branches - California, Hong Kong and Paris \n",
    "#### Source: https://www.kaggle.com/datasets/arushchillar/disneyland-reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f7a3ac4",
   "metadata": {},
   "source": [
    "## Imports and Data Checks"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a2d3fa6d",
   "metadata": {},
   "source": [
    "Sources: Some Topic Modeling code obtained from\n",
    "https://www.kaggle.com/code/imnoob/topic-modelling-lda-on-trip-advisor-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bb5b9cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re, random, os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from matplotlib import style\n",
    "import plotly.graph_objs as go\n",
    "import plotly.io as pio\n",
    "import string, pprint\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import nltk\n",
    "import re \n",
    "import spacy\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import wordnet\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.sentiment import SentimentIntensityAnalyzer\n",
    "from textblob import Word, TextBlob\n",
    "from wordcloud import WordCloud \n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "import gensim, spacy, logging, warnings\n",
    "import gensim.corpora as corpora\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV, cross_validate\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell \n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "pd.options.display.max_colwidth = 100\n",
    "pd.options.display.max_rows = 10\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', 200)\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "\n",
    "\n",
    "from pprint import pprint\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "30bb36ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Review</th>\n",
       "      <th>Branch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>Have been to Disney World, Disneyland Anaheim and Tokyo Disneyland but I feel that Disneyland Ho...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>Think of it as an intro to Disney magic for the little ones. Almost all of the attractions can b...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>Feel so let down with this place,the Disneyland train was fantastic until you get past the stati...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>We brought tickets before we left and got 2 days for less than the price of 1. After visiting wo...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>Around   60 per person and if you want to eat or drink at any point it was cost you a small fort...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sentiment                                                                                               Review               Branch\n",
       "5           0  Have been to Disney World, Disneyland Anaheim and Tokyo Disneyland but I feel that Disneyland Ho...  Disneyland_HongKong\n",
       "7           0  Think of it as an intro to Disney magic for the little ones. Almost all of the attractions can b...  Disneyland_HongKong\n",
       "8           0  Feel so let down with this place,the Disneyland train was fantastic until you get past the stati...  Disneyland_HongKong\n",
       "15          0  We brought tickets before we left and got 2 days for less than the price of 1. After visiting wo...  Disneyland_HongKong\n",
       "17          0  Around   60 per person and if you want to eat or drink at any point it was cost you a small fort...  Disneyland_HongKong"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import Data\n",
    "df = pd.read_csv(\"DisneylandReviews.csv\")\n",
    "df.drop(['Review_ID', 'Year_Month', 'Reviewer_Location'], axis=1, inplace=True)\n",
    "df.rename(columns={'Rating': 'Sentiment', 'Review_Text': 'Review'}, inplace=True)\n",
    "\n",
    "\n",
    "#Focus on negative reviews (<= 3 Stars)\n",
    "df['Sentiment'] = df['Sentiment'].apply(lambda x: 0 if x <= 3 else 1)\n",
    "df = df[df['Sentiment'] == 0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a26b2040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions: \n",
      "(8735, 3)\n",
      "\n",
      "Info: \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8735 entries, 5 to 42647\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Sentiment  8735 non-null   int64 \n",
      " 1   Review     8735 non-null   object\n",
      " 2   Branch     8735 non-null   object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 273.0+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#Calculating dimensions\n",
    "print('Dimensions: ')\n",
    "print(df.shape)\n",
    "print()\n",
    "#Checking info\n",
    "print('Info: ')\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f4faca3",
   "metadata": {},
   "source": [
    "## Text Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0637c973",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove duplicate values\n",
    "df.drop_duplicates(keep=False, inplace=True)\n",
    "\n",
    "# Remove punctuation\n",
    "from textacy.preprocessing.remove import punctuation\n",
    "df['Review'] = df['Review'].apply(punctuation)\n",
    "\n",
    "# Convert to lowercase \n",
    "df['Review'] = df['Review'].map(lambda x: x.lower())\n",
    "\n",
    "# Replace common entities\n",
    "from textacy.preprocessing.replace import urls, hashtags, numbers, emails, emojis, currency_symbols\n",
    "df['Review'] = df['Review'].\\\n",
    " apply(urls).\\\n",
    " apply(hashtags).\\\n",
    " apply(currency_symbols).\\\n",
    " apply(emojis).\\\n",
    " apply(emails)\n",
    "\n",
    "#Define and remove stopwords\n",
    "stop_words = stopwords.words('english')\n",
    "stop_words.extend(['from', 'subject', 're', 'edu', 'use', 'not', 'would', 'say', 'could', '_', 'be', 'know', \n",
    "                   'good', 'go', 'get', 'do', 'done', 'try', 'many', 'some', 'nice', 'thank', 'think', 'see',\n",
    "                   'rather', 'easy', 'easily', 'lot', 'lack', 'make', 'want', 'seem', 'run', 'need', 'even', \n",
    "                   'right', 'line', 'even', 'also', 'may', 'take', 'come'])\n",
    "df['Review'] = df['Review'].apply(lambda x: \" \".join(x for x in str(x).split() if x not in stop_words))\n",
    "\n",
    "# Clean out Disney location references and ride references\n",
    "df['Review'] = df['Review'].str.replace(r\"disney(?:land| park)?|park|place|hong kong|hk|cali|california|paris|pa|fr|Disneyland Railroad|\\\n",
    "                            Great Moments with Mr. Lincoln | Lincoln| The Disney Gallery | Gallery| Main Street Vehicles | \\\n",
    "                            Main Street Cinema | Main Street | Walt Disney's Enchanted Tiki Room | Enchanted Tiki Room | Jungle Cruise | \\\n",
    "                            Indiana Jones | Pirates of the Caribbean | Haunted Mansion | \\\n",
    "                            Davy Crockett Explorer Canoes | Davy Crockett | Explorer Canoes | Many Adventures of Winnie the Pooh | Winnie the Pooh | Splash Mountain | \\\n",
    "                            Big Thunder Mountain Railroad | Mark Twain Riverboat | Mark Twain | Frontierland Shootin Exposition |Frontierland| \\\n",
    "                            Pirate's Lair on Tom Sawyer Island | Pirate's Lair | Tom Sawyer | Sailing Ship Columbia | \\\n",
    "                            Mickey's House | Meet Mickey | Minnie's House | Roger Rabbit's Car Toon Spin | Roger Rabbit | \\\n",
    "                            Chip 'n' Dale's Gadget Coaster | Gadget Coaster | Fantasyland Theatre | It's a Small World | Small World | \\\n",
    "                            Mr. Toad's Wild Ride | Peter Pan's Flight | Pinocchio's Daring Journey | Pixie Hollow |\\\n",
    "                            Snow White's Enchanted Wish | Snow White | Enchanted Wish | Storybook Land Canal Boats | Storybook Land| Canal Boats |Dumbo the Flying Elephant | Dumbo |\\\n",
    "                            Casey Jr. Circus Train | Circus Train | Alice in Wonderland | Matterhorn Bobsleds | Bobsleds | King Arthur Carrousel | King Arthur |\\\n",
    "                            Mad Tea Party | Sleeping Beauty Castle Walkthrough | Sleeping Beauty| Astro Orbiter | \\\n",
    "                            Autopia Star Tours – The Adventures Continue | Autopia Star Tours | Buzz Lightyear Astro Blasters | Buzz Lightyear | Astro Blasters |\\\n",
    "                            Space Mountain | Monorail | Finding Nemo Submarine Voyage | Finding Nemo | Submarine Voyage |\\\n",
    "                            Star Wars Launch Bay | Star Wars | Millennium Falcon: Smuggler's Run | Millennium Falcon | Smuggler's Run| Rise of the Resistance | \\\n",
    "                            Electrical Parade | Galaxy's Edge | Tomorrowland | Fantasyland | \\\n",
    "                            Mickey's Toontown | Toontown | Frontierland | Critter Country | New Orleans Square | Adventureland | \\\n",
    "                            Main Street, U.S.A.| toy story | lion king | main street | small world | sce mountain | \\\n",
    "                            story land | king show | mickey mouse | grizzly gulch | mystic manor | \\\n",
    "                            big thunder mountain | chinese new year | slinky dog ride | jungle river cruise | lion king show | \\\n",
    "                            toy story| festival lion king | runaway mine cars | mine cars | iron man |\\\n",
    "                            big grizzly mountain | sunny bay station | small world | mountain runaway mine | \\\n",
    "                            iron man experience | sleeping beauty castle | sce mountain | buzz light year | \\\n",
    "                            big thunder | iron man experience | mickey | minnie | \\\n",
    "                            big grizzley | buzz lightyear | mountain\",'')\n",
    "\n",
    "# Drop words with less than 5 frequencies\n",
    "temp_df = pd.Series(' '.join(df['Review']).split()).value_counts()\n",
    "drops = temp_df[temp_df <= 5]\n",
    "df['Review'] = df['Review'].apply(lambda x: \" \".join(x for x in x.split() if x not in drops))\n",
    "\n",
    "\n",
    "\n",
    "#Regex (parse out and normalize all references to people)\n",
    "PERSON_PATTERN: re.Pattern = re.compile(r\"\\b(?:Great-grandfather'?s?\\b|Great-grandmother'?s?\\b|Great-uncle'?s?\\b|\\\n",
    "                                        Grandfather'?s?\\b|Grandmother'?s?\\b|Great-aunt'?s?\\b|Uncle|Aunt'?s?\\b|Father'?s?\\b|\\\n",
    "                                        Mother'?s?\\b|Uncle'?s?\\b|Sister'?s?\\b|Brother-in-law'?s?\\b|Brother'?s?\\b|\\\n",
    "                                        Sister-in-law'?s?\\b|Husband'?s?\\b|Wife'?s?\\b|Cousin'?s?\\b|Cousins wife'?s?\\b|\\\n",
    "                                        Cousin'?s?\\b|Cousin'?s?\\b husband'?s?\\b|Nephew'?s?\\b|Niece'?s?\\b|Son'?s?\\b|\\\n",
    "                                        Daughter-in-law'?s?\\b|Daughter'?s?\\b|Son-in-law'?s?\\b|First cousin once removed'?s?\\b|\\\n",
    "                                        grandson'?s?\\b|grandson'?s?\\b|grand\\sson'?s?\\b|grand\\sdaughter'?s?\\b|Granddaughter'?s?\\b|Baby'?s?\\b|Child'?s?\\b|Infants'?s?\\b|Newborn'?s?\\b|\\\n",
    "                                        Mom'?s?\\b|Dad'?s?\\b|\\d{0,3}\\s?years?\\sold'?s?\\b|\\d{0,3}\\s?month'?s?\\sold'?s?\\b|\\\n",
    "                                        \\d{0,3}\\s?yr'?s?\\sold'?s?\\b|kid|kids|yr old|year old|u|pa|Mom'?s?\\b|Dad'?s?\\b|honey'?s?\\b|\\\n",
    "                                        hubby'?s?\\b|papa'?s?\\b|mama'?s?\\b|gram'?s?\\b|gramp'?s?\\b)\\b\", flags=re.IGNORECASE)\n",
    "result = []\n",
    "for value in df[\"Review\"]:\n",
    "    result.append(re.sub(PERSON_PATTERN, '_PERSON_', value))\n",
    "df[\"Reviews\"] = result \n",
    "df = df.drop('Review', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0bfdb78",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c72bfac7",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Wordcloud graph\n",
    "text = \" \".join(i for i in df.Reviews) \n",
    "wordcloud = WordCloud(max_font_size=50,\n",
    "                      max_words=100,\n",
    "                      background_color=\"white\").generate(text)\n",
    "plt.figure()\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d90fa12a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Branches: \n",
      "['Disneyland_HongKong', 'Disneyland_California', 'Disneyland_Paris']\n"
     ]
    }
   ],
   "source": [
    "#Will do an analysis on all 3 branches \n",
    "df_all = df\n",
    "print('All Branches: ')\n",
    "print(list(df_all.Branch.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf0c9807",
   "metadata": {},
   "source": [
    "## Build the Topic Model (DisneyLand HongKong)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9ab16a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define Hong Kong Branch\n",
    "df_HK = df_all[df_all['Branch']=='Disneyland_HongKong']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bdc1379b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to list\n",
    "data_words = df_HK.Reviews.values.tolist()\n",
    "\n",
    "# Build bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100)\n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n",
    "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
    "trigram_mod = gensim.models.phrases.Phraser(trigram)\n",
    "\n",
    "def process_words(texts, stop_words=stop_words, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    \"\"\"Remove Stopwords, Form Bigrams, Trigrams and Lemmatization\"\"\"\n",
    "    texts = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
    "    texts = [bigram_mod[doc] for doc in texts]\n",
    "    texts = [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
    "    texts_out = []\n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent)) \n",
    "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
    "    # remove stopwords again after lemmatization\n",
    "    texts_out = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts_out]    \n",
    "    return texts_out\n",
    "\n",
    "data_ready = process_words(data_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b5c6f076",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.054*\"ride\" + 0.032*\"time\" + 0.032*\"day\" + 0.027*\"attraction\" + '\n",
      "  '0.025*\"small\" + 0.017*\"visit\" + 0.016*\"long\" + 0.015*\"hour\" + 0.015*\"quite\" '\n",
      "  '+ 0.015*\"wait\"'),\n",
      " (1,\n",
      "  '0.036*\"staff\" + 0.026*\"people\" + 0.020*\"magic\" + 0.017*\"real\" + '\n",
      "  '0.014*\"service\" + 0.013*\"rude\" + 0.013*\"everywhere\" + 0.013*\"kingdom\" + '\n",
      "  '0.012*\"bad\" + 0.011*\"fact\"'),\n",
      " (2,\n",
      "  '0.030*\"queue\" + 0.025*\"ride\" + 0.022*\"child\" + 0.022*\"food\" + 0.021*\"well\" '\n",
      "  '+ 0.019*\"much\" + 0.018*\"really\" + 0.017*\"small\" + 0.016*\"show\" + '\n",
      "  '0.014*\"world\"'),\n",
      " (3,\n",
      "  '0.036*\"experience\" + 0.031*\"hotel\" + 0.016*\"leave\" + 0.014*\"end\" + '\n",
      "  '0.013*\"open\" + 0.013*\"tell\" + 0.013*\"allow\" + 0.012*\"entrance\" + '\n",
      "  '0.011*\"hand\" + 0.011*\"bag\"')]\n"
     ]
    }
   ],
   "source": [
    "# Create Dictionary\n",
    "id2word = corpora.Dictionary(data_ready)\n",
    "\n",
    "# Create Corpus (Term Document Frequency)\n",
    "corpus = [id2word.doc2bow(text) for text in data_ready]\n",
    "\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=4, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=10,\n",
    "                                           passes=10,\n",
    "                                           alpha='symmetric',\n",
    "                                           iterations=100,\n",
    "                                           per_word_topics=True)\n",
    "\n",
    "pprint(lda_model.print_topics())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b8f92a0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Document_No</th>\n",
       "      <th>Dominant_Topic</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.71</td>\n",
       "      <td>queue, ride, child, food, well, much, really, small, show, world</td>\n",
       "      <td>[feel, really, small, call, way, ride, attraction, souvenir, food, entrance, ticket, slightly, e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.59</td>\n",
       "      <td>staff, people, magic, real, service, rude, everywhere, kingdom, bad, fact</td>\n",
       "      <td>[magic, little, one, almost, attraction, complete, drawback, timing, example, storybook, theatre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>queue, ride, child, food, well, much, really, small, show, world</td>\n",
       "      <td>[feel, let, train, fantastic, station, bad, signage, terrible, staff, ride, work, find, map, pri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.68</td>\n",
       "      <td>ride, time, day, attraction, small, visit, long, hour, quite, wait</td>\n",
       "      <td>[bring, ticket, leave, day, less, price, visit, advise, look, online, deal, worth, walk, price, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>ride, time, day, attraction, small, visit, long, hour, quite, wait</td>\n",
       "      <td>[around, person, eat, drink, point, cost, small, fortune, week, day, assume, wait, around, half,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Document_No  Dominant_Topic  Topic_Perc_Contrib                                                                   Keywords  \\\n",
       "0            0            2.00                0.71           queue, ride, child, food, well, much, really, small, show, world   \n",
       "1            1            1.00                0.59  staff, people, magic, real, service, rude, everywhere, kingdom, bad, fact   \n",
       "2            2            2.00                0.85           queue, ride, child, food, well, much, really, small, show, world   \n",
       "3            3            0.00                0.68         ride, time, day, attraction, small, visit, long, hour, quite, wait   \n",
       "4            4            0.00                0.80         ride, time, day, attraction, small, visit, long, hour, quite, wait   \n",
       "\n",
       "                                                                                                  Text  \n",
       "0  [feel, really, small, call, way, ride, attraction, souvenir, food, entrance, ticket, slightly, e...  \n",
       "1  [magic, little, one, almost, attraction, complete, drawback, timing, example, storybook, theatre...  \n",
       "2  [feel, let, train, fantastic, station, bad, signage, terrible, staff, ride, work, find, map, pri...  \n",
       "3  [bring, ticket, leave, day, less, price, visit, advise, look, online, deal, worth, walk, price, ...  \n",
       "4  [around, person, eat, drink, point, cost, small, fortune, week, day, assume, wait, around, half,...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the dominant topics and percentage contributions\n",
    "def format_topics_sentences(ldamodel=None, corpus=corpus, texts=df):\n",
    "    sent_topics_df = pd.DataFrame()\n",
    "    for i, row_list in enumerate(ldamodel[corpus]):\n",
    "        row = row_list[0] if ldamodel.per_word_topics else row_list            \n",
    "        row = sorted(row, key=lambda x: (x[1]), reverse=True)\n",
    "        for j, (topic_num, prop_topic) in enumerate(row):\n",
    "            if j == 0:\n",
    "                wp = ldamodel.show_topic(topic_num)\n",
    "                topic_keywords = \", \".join([word for word, prop in wp])\n",
    "                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n",
    "            else:\n",
    "                break\n",
    "    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']\n",
    "    contents = pd.Series(texts)\n",
    "    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)\n",
    "    return(sent_topics_df)\n",
    "\n",
    "df_topic_sents_keywords = format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data_ready)\n",
    "\n",
    "df_dominant_topic = df_topic_sents_keywords.reset_index()\n",
    "df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n",
    "df_dominant_topic.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4c2a2907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic_Num</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Representative Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>ride, time, day, attraction, small, visit, long, hour, quite, wait</td>\n",
       "      <td>[ride, shut, hour, due, lightening, boring, little, size, quite, small, fun]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>staff, people, magic, real, service, rude, everywhere, kingdom, bad, fact</td>\n",
       "      <td>[colorful, meet, duck, moment]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>queue, ride, child, food, well, much, really, small, show, world</td>\n",
       "      <td>[ever, definitely, feel, disappointed, visit, food, atmosphere, souvenir, surprising, small, cas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.00</td>\n",
       "      <td>0.78</td>\n",
       "      <td>experience, hotel, leave, end, open, tell, allow, entrance, hand, bag</td>\n",
       "      <td>[week, end, open, indoor, door, storm, okay]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Topic_Num  Topic_Perc_Contrib                                                                   Keywords  \\\n",
       "0       0.00                0.94         ride, time, day, attraction, small, visit, long, hour, quite, wait   \n",
       "1       1.00                0.85  staff, people, magic, real, service, rude, everywhere, kingdom, bad, fact   \n",
       "2       2.00                0.96           queue, ride, child, food, well, much, really, small, show, world   \n",
       "3       3.00                0.78      experience, hotel, leave, end, open, tell, allow, entrance, hand, bag   \n",
       "\n",
       "                                                                                   Representative Text  \n",
       "0                         [ride, shut, hour, due, lightening, boring, little, size, quite, small, fun]  \n",
       "1                                                                       [colorful, meet, duck, moment]  \n",
       "2  [ever, definitely, feel, disappointed, visit, food, atmosphere, souvenir, surprising, small, cas...  \n",
       "3                                                         [week, end, open, indoor, door, storm, okay]  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The most representative sentence for each topic\n",
    "sent_topics_sorteddf_mallet = pd.DataFrame()\n",
    "sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\n",
    "\n",
    "for i, grp in sent_topics_outdf_grpd:\n",
    "    sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, \n",
    "                                             grp.sort_values(['Perc_Contribution'], ascending=False).head(1)], \n",
    "                                            axis=0)\n",
    "sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)\n",
    "sent_topics_sorteddf_mallet.columns = ['Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Representative Text\"]\n",
    "sent_topics_sorteddf_mallet.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2799a2c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Wordcloud of Top 10 words in each topic\n",
    "from matplotlib import pyplot as plt\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "cols = [color for name, color in mcolors.TABLEAU_COLORS.items()]\n",
    "\n",
    "cloud = WordCloud(stopwords=stop_words,\n",
    "                  background_color='white',\n",
    "                  width=2500,\n",
    "                  height=1800,\n",
    "                  max_words=10,\n",
    "                  colormap='tab10',\n",
    "                  color_func=lambda *args, **kwargs: cols[i],\n",
    "                  prefer_horizontal=1.0)\n",
    "\n",
    "topics = lda_model.show_topics(formatted=False)\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(10,10), sharex=True, sharey=True)\n",
    "\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    fig.add_subplot(ax)\n",
    "    topic_words = dict(topics[i][1])\n",
    "    cloud.generate_from_frequencies(topic_words, max_font_size=300)\n",
    "    plt.gca().imshow(cloud)\n",
    "    plt.gca().set_title('Topic ' + str(i), fontdict=dict(size=16))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "plt.axis('off')\n",
    "plt.margins(x=0, y=0)\n",
    "plt.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "392ee87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2560x1600 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Word Count and Weights of Topic Keywords\n",
    "from collections import Counter\n",
    "topics = lda_model.show_topics(formatted=False)\n",
    "data_flat = [w for w_list in data_ready for w in w_list]\n",
    "counter = Counter(data_flat)\n",
    "\n",
    "out = []\n",
    "for i, topic in topics:\n",
    "    for word, weight in topic:\n",
    "        out.append([word, i , weight, counter[word]])\n",
    "\n",
    "df = pd.DataFrame(out, columns=['word', 'topic_id', 'importance', 'word_count'])        \n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16,10), sharey=True, dpi=160)\n",
    "cols = [color for name, color in mcolors.TABLEAU_COLORS.items()]\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    ax.bar(x='word', height=\"word_count\", data=df.loc[df.topic_id==i, :], color=cols[i], width=0.5, alpha=0.3, label='Word Count')\n",
    "    ax_twin = ax.twinx()\n",
    "    ax_twin.bar(x='word', height=\"importance\", data=df.loc[df.topic_id==i, :], color=cols[i], width=0.2, label='Weights')\n",
    "    ax.set_ylabel('Word Count', color=cols[i])\n",
    "    ax_twin.set_ylim(0, 0.030); ax.set_ylim(0, 3500)\n",
    "    ax.set_title('Topic: ' + str(i), color=cols[i], fontsize=16)\n",
    "    ax.tick_params(axis='y', left=False)\n",
    "    ax.set_xticklabels(df.loc[df.topic_id==i, 'word'], rotation=30, horizontalalignment= 'right')\n",
    "    ax.legend(loc='upper left'); ax_twin.legend(loc='upper right')\n",
    "\n",
    "fig.tight_layout(w_pad=2)    \n",
    "fig.suptitle('Word Count and Importance of Topic Keywords', fontsize=22, y=1.05)    \n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ed15a623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el2596814105418811523581199863\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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0.6735235633245293, 0.325952144226192, 0.9975987987511289, 0.9995091675433921, 0.9439589127576151, 0.05626245175376514, 0.9931944361347249, 0.9954801954898967, 0.996962513887365, 0.04445868815719913, 0.9536388609719213, 0.9967806630373313, 0.7932258542861209, 0.2084760258059677, 0.9990527674883184], \"Term\": [\"actually\", \"adult\", \"allow\", \"almost\", \"ask\", \"attraction\", \"available\", \"avoid\", \"away\", \"back\", \"back\", \"bad\", \"bad\", \"bag\", \"behavior\", \"believe\", \"bit\", \"buy\", \"call\", \"car\", \"character\", \"cheap\", \"check\", \"child\", \"child\", \"day\", \"day\", \"design\", \"disappointment\", \"eat\", \"eat\", \"else\", \"end\", \"enjoy\", \"enough\", \"enter\", \"entrance\", \"everywhere\", \"expect\", \"expect\", \"expectation\", \"experience\", \"face\", \"fact\", \"family\", \"fan\", \"far\", \"fee\", \"feel\", \"find\", \"firework\", \"first\", \"food\", \"food\", \"foot\", \"foot\", \"fountain\", \"gate\", \"give\", \"give\", \"great\", \"guest\", \"half\", \"hand\", \"happy\", \"haunt\", \"help\", \"hold\", \"holiday\", \"hollywood\", \"hot\", \"hotel\", \"hour\", \"however\", \"huge\", \"iendly\", \"include\", \"kingdom\", \"land\", \"last\", \"leave\", \"leave\", \"less\", \"light\", \"little\", \"little\", \"long\", \"love\", \"magic\", \"magical\", \"main\", \"mainlander\", \"man\", \"maybe\", \"mean\", \"merchandise\", \"minute\", \"miss\", \"money\", \"move\", \"much\", \"next\", \"ocean\", \"old\", \"one\", \"open\", \"people\", \"people\", \"picture\", \"plan\", \"pm\", \"point\", \"probably\", \"problem\", \"push\", \"queue\", \"quite\", \"rade\", \"real\", \"really\", \"really\", \"rent\", \"restaurant\", \"restaurant\", \"ride\", \"ride\", \"rt\", \"rude\", \"sce\", \"service\", \"shop\", \"short\", \"show\", \"size\", \"small\", \"small\", \"smoke\", \"smoke\", \"smoke\", \"soon\", \"speak\", \"staff\", \"stay\", \"still\", \"store\", \"tell\", \"theme\", \"theme\", \"thing\", \"thing\", \"ticket\", \"ticket\", \"time\", \"tiny\", \"train\", \"travel\", \"trip\", \"turn\", \"understand\", \"version\", \"visit\", \"visit\", \"visitor\", \"wait\", \"walk\", \"walk\", \"waste\", \"water\", \"way\", \"well\", \"well\", \"world\", \"year\", \"year\", \"young\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 3, 4, 2]};\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_el2596814105418811523581199863\", ldavis_el2596814105418811523581199863_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_el2596814105418811523581199863\", ldavis_el2596814105418811523581199863_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_el2596814105418811523581199863\", ldavis_el2596814105418811523581199863_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=          x     y  topics  cluster  Freq\n",
       "topic                                   \n",
       "0     -0.24 -0.11       1        1 39.35\n",
       "2     -0.19  0.12       2        1 34.54\n",
       "3      0.22 -0.23       3        1 15.44\n",
       "1      0.20  0.23       4        1 10.67, topic_info=            Term    Freq   Total Category  logprob  loglift\n",
       "140         time  760.00  760.00  Default    30.00    30.00\n",
       "93         queue  616.00  616.00  Default    29.00    29.00\n",
       "196   experience  336.00  336.00  Default    28.00    28.00\n",
       "34          ride 1784.00 1784.00  Default    27.00    27.00\n",
       "108          day  786.00  786.00  Default    26.00    26.00\n",
       "...          ...     ...     ...      ...      ...      ...\n",
       "574      believe   43.26   43.96   Topic4    -5.00     2.22\n",
       "1744    behavior   47.04   48.02   Topic4    -4.91     2.22\n",
       "26        people  163.79  448.17   Topic4    -3.66     1.23\n",
       "2            bad   75.97  305.99   Topic4    -4.43     0.84\n",
       "66    restaurant   60.19  141.27   Topic4    -4.67     1.38\n",
       "\n",
       "[187 rows x 6 columns], token_table=      Topic  Freq      Term\n",
       "term                       \n",
       "230       3  0.99  actually\n",
       "169       1  1.00     adult\n",
       "719       3  1.00     allow\n",
       "49        4  0.99    almost\n",
       "571       4  1.00       ask\n",
       "...     ...   ...       ...\n",
       "47        2  0.95      well\n",
       "210       2  1.00     world\n",
       "167       1  0.79      year\n",
       "167       2  0.21      year\n",
       "211       2  1.00     young\n",
       "\n",
       "[175 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 3, 4, 2])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final pyLDAvis output\n",
    "import pyLDAvis.gensim\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, dictionary=lda_model.id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a53686c",
   "metadata": {},
   "source": [
    "## Build the Topic Model (Disneyland California)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "46cfa917",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define California Branch\n",
    "df_Cali = df_all[df_all['Branch']=='Disneyland_California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "753043d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to list\n",
    "data_words = df_Cali.Reviews.values.tolist()\n",
    "\n",
    "# Build bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100)\n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n",
    "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
    "trigram_mod = gensim.models.phrases.Phraser(trigram)\n",
    "\n",
    "def process_words(texts, stop_words=stop_words, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    \"\"\"Remove Stopwords, Form Bigrams, Trigrams and Lemmatization\"\"\"\n",
    "    texts = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
    "    texts = [bigram_mod[doc] for doc in texts]\n",
    "    texts = [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
    "    texts_out = []\n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent)) \n",
    "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
    "    texts_out = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts_out]    \n",
    "    return texts_out\n",
    "\n",
    "data_ready = process_words(data_words)\n",
    "\n",
    "# Create Dictionary\n",
    "id2word = corpora.Dictionary(data_ready)\n",
    "\n",
    "# Create Corpus (Term Document Frequency)\n",
    "corpus = [id2word.doc2bow(text) for text in data_ready]\n",
    "\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=4, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=10,\n",
    "                                           passes=10,\n",
    "                                           alpha='symmetric',\n",
    "                                           iterations=100,\n",
    "                                           per_word_topics=True)\n",
    "\n",
    "#Get the dominant topics and percentage contributions\n",
    "def format_topics_sentences(ldamodel=None, corpus=corpus, texts=df):\n",
    "    sent_topics_df = pd.DataFrame()\n",
    "    for i, row_list in enumerate(ldamodel[corpus]):\n",
    "        row = row_list[0] if ldamodel.per_word_topics else row_list            \n",
    "        row = sorted(row, key=lambda x: (x[1]), reverse=True)\n",
    "        for j, (topic_num, prop_topic) in enumerate(row):\n",
    "            if j == 0:\n",
    "                wp = ldamodel.show_topic(topic_num)\n",
    "                topic_keywords = \", \".join([word for word, prop in wp])\n",
    "                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n",
    "            else:\n",
    "                break\n",
    "    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']\n",
    "    contents = pd.Series(texts)\n",
    "    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)\n",
    "    return(sent_topics_df)\n",
    "\n",
    "df_topic_sents_keywords = format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data_ready)\n",
    "\n",
    "df_dominant_topic = df_topic_sents_keywords.reset_index()\n",
    "df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n",
    "\n",
    "#The most representative sentence for each topic\n",
    "sent_topics_sorteddf_mallet = pd.DataFrame()\n",
    "sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\n",
    "\n",
    "for i, grp in sent_topics_outdf_grpd:\n",
    "    sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, \n",
    "                                             grp.sort_values(['Perc_Contribution'], ascending=False).head(1)], \n",
    "                                            axis=0)\n",
    "sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)\n",
    "sent_topics_sorteddf_mallet.columns = ['Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Representative Text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b7ba36d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el2596814105457582721006825759\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el2596814105457582721006825759_data = {\"mdsDat\": {\"x\": [-0.15530231005174183, -0.09046560287260229, -0.11914339081363466, 0.364911303737979], \"y\": [-0.1414827024757614, -0.15775395958213034, 0.30045930697656925, -0.0012226449186774242], \"topics\": [1, 2, 3, 4], \"cluster\": [1, 1, 1, 1], \"Freq\": [42.2086651463123, 25.333776269962964, 18.84884530176629, 13.608713281958448]}, \"tinfo\": {\"Term\": [\"ride\", \"wait\", \"hour\", \"family\", \"day\", \"minute\", \"close\", \"ticket\", \"attraction\", \"fast\", \"ss\", \"world\", \"tell\", \"first\", \"magic\", \"break\", \"ask\", \"people\", \"time\", \"small\", \"food\", \"sse\", \"member\", \"show\", \"fornia\", \"adventure\", \"hotel\", \"thing\", \"staff\", \"decide\", \"food\", \"adventure\", \"thing\", \"look\", \"happy\", \"expensive\", \"rade\", \"night\", \"eat\", \"earth\", \"move\", \"water\", \"ever\", \"rude\", \"firework\", \"actually\", \"character\", \"whole\", \"check\", \"bring\", \"morning\", 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1.9867, 1.9858, 1.5476, 1.6174, 1.0839, 0.7216, 0.8529, 1.0749, 0.3955]}, \"token.table\": {\"Topic\": [1, 2, 1, 1, 1, 4, 4, 3, 1, 2, 4, 1, 3, 3, 2, 3, 1, 2, 3, 4, 4, 4, 3, 1, 1, 1, 3, 4, 3, 3, 4, 1, 2, 4, 4, 1, 2, 3, 4, 3, 1, 4, 3, 1, 2, 1, 1, 4, 4, 2, 1, 1, 1, 3, 4, 4, 4, 2, 2, 2, 2, 1, 3, 2, 1, 3, 4, 1, 2, 4, 1, 1, 3, 1, 2, 4, 1, 3, 3, 1, 1, 2, 2, 4, 3, 2, 2, 1, 4, 3, 3, 2, 3, 1, 3, 3, 1, 2, 1, 1, 3, 4, 2, 1, 4, 4, 2, 2, 1, 2, 4, 1, 1, 1, 3, 1, 2, 2, 1, 4, 3, 1, 3, 4, 1, 2, 2, 3, 2, 2, 1, 1, 3, 1, 3, 4, 2, 3, 1, 1, 2, 2, 4, 1, 1, 2, 1, 2, 3, 3, 1, 1, 4, 3, 1, 2, 4, 2, 2, 3, 3, 2, 1, 3, 3, 4, 4, 1, 3, 4, 4, 1, 2, 3, 2, 2, 1, 4, 1, 2, 4, 2, 4, 1, 2, 2, 2, 2, 4, 3, 1, 3, 2, 1, 2, 3, 4, 4, 3, 3, 1, 3, 4, 1, 4, 3, 2, 4, 1, 2, 3, 2, 1, 2, 1, 1, 2, 2, 4, 1, 2, 3, 1, 1, 3, 1, 2, 1, 3, 4, 3], \"Freq\": [0.088855628561196, 0.909881636466647, 0.9960806643981713, 0.9980482792494374, 0.9990346092537604, 0.9924626149676594, 0.9972805659060197, 0.998543097177528, 0.44552227570494946, 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0.9981302678293978, 0.6565888302800212, 0.06011685999702627, 0.2827371071735142, 0.9998264182256894, 0.5454735554953878, 0.45367865295247467, 0.9977949011379617, 0.7635212901760524, 0.23549219869414645, 0.973264422733339, 0.021157922233333456, 0.7253126407132771, 0.07958291474492901, 0.19442408285786453, 0.9964405618998388, 0.37447734543485056, 0.6248741376062132, 0.9633682970936531, 0.03683467018299262, 0.8831985974198358, 0.1099611498774365, 0.007018796800687437, 0.9944898213581878], \"Term\": [\"able\", \"able\", \"actually\", \"adult\", \"adventure\", \"ago\", \"ask\", \"attraction\", \"back\", \"back\", \"back\", \"big\", \"big\", \"bit\", \"break\", \"break\", \"bring\", \"buy\", \"call\", \"care\", \"cast\", \"certain\", \"change\", \"character\", \"check\", \"child\", \"child\", \"choose\", \"close\", \"coaster\", \"complain\", \"crowd\", \"crowd\", \"customer\", \"cut\", \"day\", \"day\", \"day\", \"decide\", \"different\", \"dinner\", \"dollar\", \"due\", \"early\", \"early\", \"earth\", \"eat\", \"else\", \"employee\", \"end\", \"ever\", \"expensive\", \"experience\", \"experience\", \"experience\", \"face\", \"family\", \"far\", \"fast\", \"fastss\", \"fastsse\", \"feel\", \"feel\", \"finally\", \"find\", \"find\", \"find\", \"firework\", \"first\", \"first\", \"food\", \"fornia\", \"fornia\", \"give\", \"give\", \"give\", \"great\", \"great\", \"guest\", \"happy\", \"hard\", \"hotel\", \"hour\", \"iendly\", \"include\", \"information\", \"ing\", \"keep\", \"kingdom\", \"land\", \"large\", \"later\", \"life\", \"little\", \"little\", \"local\", \"long\", \"long\", \"look\", \"love\", \"love\", \"magic\", \"manage\", \"maybe\", \"meet\", \"member\", \"min\", \"minute\", \"money\", \"money\", \"month\", \"morning\", \"move\", \"much\", \"much\", \"never\", \"never\", \"new\", \"night\", \"notice\", \"offer\", \"old\", \"old\", \"overprice\", \"people\", \"people\", \"picture\", \"pirate\", \"pm\", \"popular\", \"pretty\", \"price\", \"price\", \"probably\", \"problem\", \"problem\", \"queue\", \"quite\", \"rade\", \"really\", \"really\", \"reservation\", \"resort\", \"restaurant\", \"return\", \"return\", \"ride\", \"ride\", \"ride\", \"roller\", \"room\", \"rude\", \"save\", \"sce\", \"security\", \"security\", \"security\", \"sell\", \"send\", \"service\", \"several\", \"short\", \"show\", \"show\", \"shut\", \"sign\", \"single\", \"sit\", \"small\", \"smile\", \"snack\", \"spend\", \"spend\", \"splash\", \"ss\", \"sse\", \"staff\", \"staff\", \"stand\", \"stand\", \"stand\", \"stay\", \"stay\", \"still\", \"still\", \"stroller\", \"system\", \"tell\", \"tell\", \"theme\", \"thing\", \"thunder\", \"ticket\", \"time\", \"time\", \"time\", \"total\", \"tour\", \"town\", \"travel\", \"trip\", \"trip\", \"trip\", \"turn\", \"turn\", \"twice\", \"universal\", \"vacation\", \"visit\", \"visit\", \"visit\", \"wait\", \"walk\", \"walk\", \"water\", \"way\", \"way\", \"week\", \"week\", \"well\", \"well\", \"well\", \"whole\", \"world\", \"world\", \"worth\", \"worth\", \"year\", \"year\", \"year\", \"young\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 4, 1, 3]};\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_el2596814105457582721006825759\", ldavis_el2596814105457582721006825759_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_el2596814105457582721006825759\", ldavis_el2596814105457582721006825759_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_el2596814105457582721006825759\", ldavis_el2596814105457582721006825759_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=          x     y  topics  cluster  Freq\n",
       "topic                                   \n",
       "1     -0.16 -0.14       1        1 42.21\n",
       "3     -0.09 -0.16       2        1 25.33\n",
       "0     -0.12  0.30       3        1 18.85\n",
       "2      0.36 -0.00       4        1 13.61, topic_info=           Term    Freq   Total Category  logprob  loglift\n",
       "131        ride 4734.00 4734.00  Default    30.00    30.00\n",
       "16         wait 2005.00 2005.00  Default    29.00    29.00\n",
       "207        hour 1432.00 1432.00  Default    28.00    28.00\n",
       "176      family  746.00  746.00  Default    27.00    27.00\n",
       "55          day 2915.00 2915.00  Default    26.00    26.00\n",
       "..          ...     ...     ...      ...      ...      ...\n",
       "305       first  327.67  814.49   Topic4    -4.11     1.08\n",
       "73         find  190.03  678.62   Topic4    -4.66     0.72\n",
       "79         give  163.13  510.88   Topic4    -4.81     0.85\n",
       "156        turn  120.72  302.79   Topic4    -5.11     1.07\n",
       "68   experience  123.91  613.10   Topic4    -5.09     0.40\n",
       "\n",
       "[218 rows x 6 columns], token_table=      Topic  Freq       Term\n",
       "term                        \n",
       "18        1  0.09       able\n",
       "18        2  0.91       able\n",
       "579       1  1.00   actually\n",
       "350       1  1.00      adult\n",
       "20        1  1.00  adventure\n",
       "...     ...   ...        ...\n",
       "166       2  0.04      worth\n",
       "167       1  0.88       year\n",
       "167       3  0.11       year\n",
       "167       4  0.01       year\n",
       "418       3  0.99      young\n",
       "\n",
       "[231 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 4, 1, 3])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final pyLDAvis output\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, dictionary=lda_model.id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb927caa",
   "metadata": {},
   "source": [
    "## Build the Topic Model (Disneyland Paris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "20e0eab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define Paris Branch\n",
    "df_Paris = df_all[df_all['Branch']=='Disneyland_Paris']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a3d3f76a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to list\n",
    "data_words = df_Paris.Reviews.values.tolist()\n",
    "\n",
    "# Build bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) \n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n",
    "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
    "trigram_mod = gensim.models.phrases.Phraser(trigram)\n",
    "\n",
    "def process_words(texts, stop_words=stop_words, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    \"\"\"Remove Stopwords, Form Bigrams, Trigrams and Lemmatization\"\"\"\n",
    "    texts = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
    "    texts = [bigram_mod[doc] for doc in texts]\n",
    "    texts = [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
    "    texts_out = []\n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent)) \n",
    "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
    "    texts_out = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts_out]    \n",
    "    return texts_out\n",
    "\n",
    "data_ready = process_words(data_words)\n",
    "\n",
    "# Create Dictionary\n",
    "id2word = corpora.Dictionary(data_ready)\n",
    "\n",
    "# Create Corpus (Term Document Frequency)\n",
    "corpus = [id2word.doc2bow(text) for text in data_ready]\n",
    "\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=4, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=10,\n",
    "                                           passes=10,\n",
    "                                           alpha='symmetric',\n",
    "                                           iterations=100,\n",
    "                                           per_word_topics=True)\n",
    "\n",
    "#Get the dominant topics and percentage contributions\n",
    "def format_topics_sentences(ldamodel=None, corpus=corpus, texts=df):\n",
    "    sent_topics_df = pd.DataFrame()\n",
    "    for i, row_list in enumerate(ldamodel[corpus]):\n",
    "        row = row_list[0] if ldamodel.per_word_topics else row_list            \n",
    "        row = sorted(row, key=lambda x: (x[1]), reverse=True)\n",
    "        for j, (topic_num, prop_topic) in enumerate(row):\n",
    "            if j == 0:\n",
    "                wp = ldamodel.show_topic(topic_num)\n",
    "                topic_keywords = \", \".join([word for word, prop in wp])\n",
    "                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n",
    "            else:\n",
    "                break\n",
    "    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']\n",
    "    contents = pd.Series(texts)\n",
    "    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)\n",
    "    return(sent_topics_df)\n",
    "\n",
    "df_topic_sents_keywords = format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data_ready)\n",
    "\n",
    "df_dominant_topic = df_topic_sents_keywords.reset_index()\n",
    "df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n",
    "\n",
    "#The most representative sentence for each topic\n",
    "sent_topics_sorteddf_mallet = pd.DataFrame()\n",
    "sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\n",
    "\n",
    "for i, grp in sent_topics_outdf_grpd:\n",
    "    sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, \n",
    "                                             grp.sort_values(['Perc_Contribution'], ascending=False).head(1)], \n",
    "                                            axis=0)\n",
    "sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)\n",
    "sent_topics_sorteddf_mallet.columns = ['Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Representative Text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d1601a25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el2596814105172618883729537210\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el2596814105172618883729537210_data = {\"mdsDat\": {\"x\": [0.03914773266859734, -0.2032926559519485, 0.328065699371264, -0.1639207760879127], \"y\": [-0.22830633062529015, -0.15513049474915042, 0.0818194176164418, 0.3016174077579988], \"topics\": [1, 2, 3, 4], \"cluster\": [1, 1, 1, 1], \"Freq\": [63.68051859456138, 15.800118966941262, 11.454053289603529, 9.065309148893832]}, \"tinfo\": {\"Term\": [\"hotel\", \"staff\", \"attraction\", \"ride\", \"food\", \"ench\", \"close\", \"time\", \"area\", \"service\", \"give\", \"find\", \"day\", \"stay\", \"tell\", \"room\", \"queue\", \"member\", \"last\", \"ask\", \"dlp\", \"eat\", \"car\", \"smoke\", \"meal\", \"look\", \"ticket\", \"restaurant\", \"village\", \"bus\", \"ride\", \"time\", \"day\", \"well\", \"child\", \"visit\", \"wait\", \"character\", \"minute\", \"hour\", \"much\", \"really\", \"long\", \"fast\", \"money\", \"expect\", \"thing\", \"price\", \"year\", \"trip\", \"feel\", \"small\", \"studio\", \"enjoy\", \"little\", \"euro\", \"ss\", \"spend\", \"holiday\", \"rude\", \"queue\", \"people\", \"great\", \"back\", \"experience\", \"way\", \"bad\", \"first\", \"food\", \"hotel\", \"room\", \"eat\", \"meal\", \"village\", \"bus\", \"breakfast\", \"night\", \"drink\", \"pm\", \"table\", \"coffee\", \"dinner\", \"station\", \"bag\", \"advice\", \"burger\", \"sequoia\", \"weekend\", \"chip\", \"birthday\", \"cafe\", \"lodge\", \"tired\", \"seat\", \"buffet\", \"evening\", \"choice\", \"shuttle\", \"bottle\", \"waiter\", \"pick\", \"stay\", \"lunch\", \"restaurant\", \"find\", \"food\", \"busy\", \"cost\", \"far\", \"expensive\", \"show\", \"train\", \"bit\", \"attraction\", \"ench\", \"give\", \"service\", \"ask\", \"last\", \"customer\", \"speak\", \"call\", \"english\", \"appear\", \"care\", \"large\", \"simply\", \"american\", \"group\", \"difference\", \"attitude\", \"complaint\", \"unhelpful\", \"talk\", \"manager\", \"provide\", \"matter\", \"sorry\", \"phone\", \"fail\", 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0.2982769855870669, 0.6972708753983381, 0.9910685767306753, 0.9913917169844113, 0.9978377325144652, 0.9998866776557177, 0.5287175833815732, 0.4706503343396663, 0.9994499682090893, 0.9964517907010976, 0.9356166515298209, 0.06374943116636055, 0.9953827836355161, 0.9945645263886773, 0.9995793161092582, 0.9998791466419222], \"Term\": [\"advice\", \"american\", \"ance\", \"ance\", \"ance\", \"appear\", \"approach\", \"area\", \"ask\", \"attitude\", \"attraction\", \"back\", \"back\", \"bad\", \"bad\", \"bad\", \"bag\", \"bathroom\", \"birthday\", \"bit\", \"bit\", \"boat\", \"bottle\", \"breakfast\", \"buffet\", \"building\", \"burger\", \"bus\", \"busy\", \"busy\", \"cafe\", \"call\", \"car\", \"care\", \"caribbean\", \"cast\", \"cause\", \"character\", \"child\", \"chip\", \"choice\", \"close\", \"close\", \"coffee\", \"complaint\", \"cost\", \"cost\", \"crush\", \"customer\", \"day\", \"designate\", \"difference\", \"dinner\", \"dirty\", \"dirty\", \"dlp\", \"dlp\", \"drink\", \"eat\", \"employee\", \"ench\", \"english\", \"enjoy\", \"euro\", \"evening\", \"expect\", \"expensive\", \"expensive\", \"experience\", \"experience\", \"face\", \"fail\", \"far\", \"far\", \"fast\", \"feel\", \"filthy\", \"finally\", \"finally\", \"find\", \"find\", \"first\", \"first\", \"follow\", \"follow\", \"food\", \"food\", \"general\", \"general\", \"give\", \"great\", \"great\", \"group\", \"holiday\", \"hotel\", \"hour\", \"ice\", \"kingdom\", \"large\", \"last\", \"leave\", \"leave\", \"leave\", \"level\", \"level\", \"little\", \"lodge\", \"long\", \"look\", \"look\", \"look\", \"lunch\", \"lunch\", \"magic\", \"magic\", \"man\", \"manager\", \"map\", \"matter\", \"meal\", \"member\", \"minute\", \"miss\", \"miss\", \"money\", \"much\", \"next\", \"next\", \"night\", \"people\", \"people\", \"phone\", \"pick\", \"pirate\", \"pm\", \"poor\", \"poor\", \"price\", \"problem\", \"problem\", \"problem\", \"provide\", \"queue\", \"queue\", \"really\", \"report\", \"restaurant\", \"restaurant\", \"restaurant\", \"ride\", \"rock\", \"room\", \"rude\", \"seat\", \"security\", \"send\", \"sequoia\", \"service\", \"several\", \"several\", \"show\", \"show\", \"shut\", \"shuttle\", \"simply\", \"small\", \"smell\", \"smoke\", \"smoking\", \"sorry\", \"speak\", \"spend\", \"ss\", \"staff\", \"staff\", \"standard\", \"station\", \"stay\", \"stay\", \"stick\", \"studio\", \"table\", \"talk\", \"technical\", \"tell\", \"tell\", \"terror\", \"thing\", \"thunder\", \"ticket\", \"ticket\", \"time\", \"tired\", \"tour\", \"train\", \"train\", \"trip\", \"understand\", \"understand\", \"unhelpful\", \"vacation\", \"village\", \"visit\", \"visitor\", \"visitor\", \"wait\", \"waiter\", \"way\", \"way\", \"wdw\", \"weekend\", \"well\", \"year\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 1, 4, 3]};\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_el2596814105172618883729537210\", ldavis_el2596814105172618883729537210_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_el2596814105172618883729537210\", ldavis_el2596814105172618883729537210_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_el2596814105172618883729537210\", ldavis_el2596814105172618883729537210_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=          x     y  topics  cluster  Freq\n",
       "topic                                   \n",
       "1      0.04 -0.23       1        1 63.68\n",
       "0     -0.20 -0.16       2        1 15.80\n",
       "3      0.33  0.08       3        1 11.45\n",
       "2     -0.16  0.30       4        1  9.07, topic_info=           Term    Freq   Total Category  logprob  loglift\n",
       "261       hotel 2006.00 2006.00  Default    30.00    30.00\n",
       "51        staff 2003.00 2003.00  Default    29.00    29.00\n",
       "306  attraction 1119.00 1119.00  Default    28.00    28.00\n",
       "93         ride 5382.00 5382.00  Default    27.00    27.00\n",
       "139        food 2311.00 2311.00  Default    26.00    26.00\n",
       "..          ...     ...     ...      ...      ...      ...\n",
       "618       level  150.49  188.09   Topic4    -5.08     2.18\n",
       "667     problem  181.94  420.66   Topic4    -4.89     1.56\n",
       "149        look  223.98 1158.52   Topic4    -4.69     0.76\n",
       "445     general  139.24  189.07   Topic4    -5.16     2.09\n",
       "79        magic  148.94 1021.99   Topic4    -5.09     0.47\n",
       "\n",
       "[199 rows x 6 columns], token_table=      Topic  Freq      Term\n",
       "term                       \n",
       "283       2  1.00    advice\n",
       "524       3  1.00  american\n",
       "837       1  0.31      ance\n",
       "837       3  0.56      ance\n",
       "837       4  0.13      ance\n",
       "...     ...   ...       ...\n",
       "186       2  0.06       way\n",
       "851       4  1.00       wdw\n",
       "489       2  0.99   weekend\n",
       "58        1  1.00      well\n",
       "214       1  1.00      year\n",
       "\n",
       "[213 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 1, 4, 3])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final pyLDAvis output\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, dictionary=lda_model.id2word)\n",
    "vis"
   ]
  }
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