LDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Topic Modeling**\n",
    "- Definition: Recognize the words from the topics present in the document or the corpus of data, which can be more efficient than document extraction. Unsupervised learning method.\n",
    "- Document Processing: \n",
    "  - Remove stopwords and punctuation marks\n",
    "  - Stemming\n",
    "     - Porter\n",
    "     - Lancaster \n",
    "     - Snowball\n",
    "  - Lemmatization: \n",
    "    - bring shorter and base words, cut the words to get the lemma word, which is more meaningful than stemming\n",
    "    - From WordNet lemmatization, TextBlob,SpaCy,Tree Tagger,\n",
    "  - Encoding use countvectorizer or tfidfvectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#parameter tuning use grid search CV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "from sklearn.manifold import TSNE\n",
    "grid_params={'n_components': list(range(5,10))}\n",
    "#build LDA model\n",
    "lda=LatentDirichletAllocation()\n",
    "lda_model=GridSearchCV(lda,param_grid=grid_params)\n",
    "#__class__ sklearn.model_selection.GridSearchCV(estimator,param_grid, scoring=None, n_jobs=None, refit=True,cv=None,verbose=0,error_score=nan,return_train_score=False)\n",
    "\n",
    "#we use tfidf or countvectorizer convert documents into document-term matrix\n",
    "tf_vec=CountVectorizer(strip_accents='unicode',\n",
    "                       stop_word='english',\n",
    "                       lowercase=True,\n",
    "                       token_pattern=r'\\b[^a-zA-z]{3,}\\b',\n",
    "                       max_df=0.5,\n",
    "                       min_df=10)\n",
    "dtm_tf=tf_vec.fit_transform(docs_raw)\n",
    "\n",
    "tfidf_vec=TfidfVector(tf_vectorizer.get_garams())\n",
    "dtm_tfidf=tfidf_vec.fit_transform(docs_raw)\n",
    "\n",
    "lda_model.fit(dtm_tfidf)\n",
    "\n",
    "#model prediction\n",
    "lda_model1=lda_model.best_estimator_\n",
    "print('Best lda model parameter', lda_model.best_estimator_)\n",
    "print('Best log likelihood score for lda model', lda_model.best_score_)\n",
    "print('lda model perplexity on training data', lda_model1.perplexity(document_term_matrix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LDA model has three important parameters:\n",
    "- 𝛂: document topic density factor\n",
    "- 𝛃: word-density in a topic\n",
    "- k: the number of components representing the number of topics we want docu to be clustered or divided into parts.\n",
    "\n",
    "Then we use `tSNE` method for *dimensionality reduction*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid Search CV method: \n",
    "- optimize ml model with hyperparameters(control learning features, compute internal features, weights a prediction)\n",
    "- **Grid Searching**: Selecting optimzal settings required iterating through different options to see how affects model accuracy.\n",
    "- In Sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html?highlight=grid#sklearn.model_selection.GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Visualization for topic modeling with Sklearn:\n",
    "import pyLDAvis.sklearn\n",
    "pyLDAvis.enable_notebook()\n",
    "pyLDAvis.sklearn.prepare(best_lda_model, small_document_term_matrix, small_count_vectorizer, mds='tsne') #mds:multi-dimensional scaling:MMDS,tSNE,PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use Kaggle API\n",
    "! mkdir -p ~/.kaggle\n",
    "! cp kaggle.json ~/.kaggle\n",
    "! chmod 600 /root/.kaggle/kaggle.json\n",
    "! kaggle datasets download -d snap/amazon-fine-food-reviews\n",
    "! unzip /content/amazon-fine-food-reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import string\n",
    "import spacy\n",
    "import gensim\n",
    "from gensim import corpora\n",
    "#lda visualization\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models as gensimvis\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id   ProductId          UserId ProfileName  HelpfulnessNumerator  \\\n",
      "0   1  B001E4KFG0  A3SGXH7AUHU8GW  delmartian                     1   \n",
      "1   2  B00813GRG4  A1D87F6ZCVE5NK      dll pa                     0   \n",
      "\n",
      "   HelpfulnessDenominator  Score        Time                Summary  \\\n",
      "0                       1      5  1303862400  Good Quality Dog Food   \n",
      "1                       0      1  1346976000      Not as Advertised   \n",
      "\n",
      "                                                Text  \n",
      "0  I have bought several of the Vitality canned d...  \n",
      "1  Product arrived labeled as Jumbo Salted Peanut...  \n",
      "568454\n",
      "Unique Products\n",
      "74258\n",
      "Unique Users\n",
      "256059\n"
     ]
    }
   ],
   "source": [
    "#import dataset and the basic exploration\n",
    "review_data= pd.read_csv(\"/Content/Reviews.csv\")\n",
    "print(review_data.head(2))\n",
    "print(len(review_data))\n",
    "print('Unique Products')\n",
    "print(len(review_data.groupby('ProductId')))\n",
    "print('Unique Users')\n",
    "print(len(review_data.groupby('UserId')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text ): \n",
    "    delete_dict = {sp_character: '' for sp_character in string.punctuation}  #delete punctuation\n",
    "    delete_dict[' '] = ' ' \n",
    "    table = str.maketrans(delete_dict)\n",
    "    text1 = text.translate(table)\n",
    "    \n",
    "    textArr= text1.split()\n",
    "    text2 = ' '.join([w for w in textArr if ( not w.isdigit() and  ( not w.isdigit() and len(w)>3))]) \n",
    "    \n",
    "    return text2.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------Dataset --------\n",
      "5    363111\n",
      "4     80655\n",
      "1     52264\n",
      "3     42638\n",
      "2     29743\n",
      "Name: Score, dtype: int64\n",
      "568411\n",
      "-------------------------\n",
      "No of Short reviews\n",
      "373281\n"
     ]
    }
   ],
   "source": [
    "#drop the missing value\n",
    "review_data.dropna(axis = 0, how ='any',inplace=True) \n",
    "#apply clean text function to Text column\n",
    "review_data['Text'] = review_data['Text'].apply(clean_text)\n",
    "#create the new column with lambda function to split Text column\n",
    "review_data['Num_words_text'] = review_data['Text'].apply(lambda x:len(str(x).split())) \n",
    "#check the resault\n",
    "print('-------Dataset --------')\n",
    "print(review_data['Score'].value_counts()) #get the numbers of different score\n",
    "print(len(review_data))\n",
    "print('-------------------------')\n",
    "max_review_data_sentence_length  = review_data['Num_words_text'].max() #get the maximum length of review\n",
    "#define the short review length\n",
    "mask = (review_data['Num_words_text'] < 100) & (review_data['Num_words_text'] >=20)\n",
    "df_short_reviews = review_data[mask]\n",
    "df_sampled = df_short_reviews.groupby('Score').apply(lambda x: x.sample(n=20000)).reset_index(drop = True)\n",
    "\n",
    "print('No of Short reviews')\n",
    "print(len(df_short_reviews))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Text Preprocssing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english')\n",
    "# function to remove stopwords\n",
    "def remove_stopwords(text):\n",
    "    textArr = text.split(' ')\n",
    "    rem_text = \" \".join([i for i in textArr if i not in stop_words])\n",
    "    return rem_text\n",
    "\n",
    "# apply the function \n",
    "df_sampled['Text']=df_sampled['Text'].apply(remove_stopwords)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "## python3 -m spacy download en\n",
    "nlp = spacy.load('en_core_web_md', disable=['parser', 'ner'])\n",
    "#Do lemmatization keeping only noun, adj\n",
    "def lemmatization(texts,allowed_postags=['NOUN', 'ADJ']): \n",
    "       output = []\n",
    "       for sent in texts:\n",
    "             doc = nlp(sent) \n",
    "             output.append([token.lemma_ for token in doc if token.pos_ in allowed_postags ])\n",
    "       return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "love stroopwafels tried sorts kind come shipper stroopie stroopwafels nice brand instead count advertised arrived fancy blue need even sticker says count either someone embezzling stroopwafels misleadingly advertised aware read count makes price point pretty different\n",
      "['love', 'stroopwafel', 'sort', 'shipper', 'stroopie', 'nice', 'brand', 'fancy', 'blue', 'need', 'sticker', 'stroopwafel', 'aware', 'read', 'count', 'price', 'point', 'different']\n"
     ]
    }
   ],
   "source": [
    "#create the text list and tokenized list\n",
    "text_list=df_sampled['Text'].tolist()\n",
    "print(text_list[1])\n",
    "\n",
    "tokenized_reviews = lemmatization(text_list)\n",
    "print(tokenized_reviews[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the Document-Term Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create the dictionary\n",
    "dictionary = corpora.Dictionary(tokenized_reviews)\n",
    "#create the dtm\n",
    "dtm= [dictionary.doc2bow(rev) for rev in tokenized_reviews]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use LDA model using Gensim library\n",
    "#https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/\n",
    "LDA = gensim.models.ldamodel.LdaModel\n",
    "\n",
    "#Build LDA model\n",
    "lda_model = LDA(corpus=dtm, \n",
    "                id2word=dictionary, \n",
    "                num_topics=10,             #numbers of topics with combination\n",
    "                random_state=100,\n",
    "                chunksize=1000,            #the number of documents to be used in each training chunk.\n",
    "                passes=50,                 #the total number of training passes.\n",
    "                iterations=100)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.036*\"flavor\" + 0.033*\"chip\" + 0.028*\"snack\" + 0.028*\"good\" + 0.026*\"great\" + 0.024*\"salt\" + 0.017*\"nice\" + 0.016*\"candy\" + 0.015*\"perfect\" + 0.014*\"little\"'),\n",
       " (1,\n",
       "  '0.078*\"product\" + 0.053*\"free\" + 0.025*\"gluten\" + 0.018*\"yummy\" + 0.017*\"cracker\" + 0.017*\"snack\" + 0.011*\"package\" + 0.010*\"pretzel\" + 0.010*\"shipping\" + 0.010*\"banana\"'),\n",
       " (2,\n",
       "  '0.047*\"sauce\" + 0.035*\"cereal\" + 0.032*\"good\" + 0.027*\"cheese\" + 0.021*\"pasta\" + 0.017*\"fresh\" + 0.017*\"seed\" + 0.015*\"beef\" + 0.014*\"texture\" + 0.013*\"taste\"'),\n",
       " (3,\n",
       "  '0.041*\"product\" + 0.039*\"time\" + 0.031*\"order\" + 0.028*\"great\" + 0.018*\"good\" + 0.016*\"small\" + 0.014*\"package\" + 0.013*\"size\" + 0.013*\"bag\" + 0.013*\"soup\"'),\n",
       " (4,\n",
       "  '0.024*\"bread\" + 0.022*\"time\" + 0.018*\"product\" + 0.018*\"year\" + 0.014*\"ginger\" + 0.013*\"clean\" + 0.013*\"pill\" + 0.012*\"stomach\" + 0.011*\"mild\" + 0.011*\"week\"'),\n",
       " (5,\n",
       "  '0.091*\"food\" + 0.030*\"treat\" + 0.024*\"dog\" + 0.020*\"good\" + 0.014*\"great\" + 0.014*\"cat\" + 0.014*\"healthy\" + 0.013*\"meal\" + 0.013*\"little\" + 0.012*\"ingredient\"'),\n",
       " (6,\n",
       "  '0.090*\"price\" + 0.085*\"store\" + 0.038*\"good\" + 0.037*\"local\" + 0.034*\"great\" + 0.029*\"grocery\" + 0.017*\"cheap\" + 0.016*\"shipping\" + 0.015*\"well\" + 0.014*\"product\"'),\n",
       " (7,\n",
       "  '0.040*\"flavor\" + 0.032*\"taste\" + 0.027*\"water\" + 0.026*\"good\" + 0.023*\"sugar\" + 0.019*\"drink\" + 0.016*\"sweet\" + 0.015*\"fruit\" + 0.014*\"great\" + 0.013*\"product\"'),\n",
       " (8,\n",
       "  '0.056*\"chocolate\" + 0.035*\"good\" + 0.034*\"cookie\" + 0.025*\"milk\" + 0.024*\"butter\" + 0.023*\"great\" + 0.021*\"peanut\" + 0.021*\"taste\" + 0.019*\"bar\" + 0.017*\"flavor\"'),\n",
       " (9,\n",
       "  '0.143*\"coffee\" + 0.032*\"flavor\" + 0.030*\"good\" + 0.026*\"strong\" + 0.017*\"taste\" + 0.016*\"bean\" + 0.015*\"kcup\" + 0.014*\"blend\" + 0.013*\"great\" + 0.013*\"morning\"')]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the topic of LDA models\n",
    "lda_model.print_topics()\n",
    "#each topic is a combination of keywords and each keyword contributes a certain weightage to the topic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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_el1051623536666518642584673089\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el1051623536666518642584673089_data = {\"mdsDat\": {\"x\": [-0.14135100041147633, 0.03724625767654214, 0.17356335771153322, -0.14494239301119033, -0.13092632205464327, -0.20807995513440183, 0.05531795294506977, 0.2704784737859241, 0.19286562402090512, -0.10417199552826177], \"y\": [0.04787345941900245, 0.05340857605130146, 0.021283551775612596, 0.014900591096642732, 0.036636611896229195, -0.020108533426776518, -0.23697019681387024, -0.16827206683518844, 0.2984523405693684, -0.04720433373232244], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [16.145686340836534, 12.615353600217826, 12.4072327392275, 11.442961553316563, 11.124015690426713, 9.16750464104609, 7.714573126622907, 6.81043374868622, 6.712844617147966, 5.859393942471671]}, \"tinfo\": {\"Term\": [\"coffee\", \"food\", \"price\", \"store\", \"product\", \"chocolate\", \"flavor\", \"free\", \"chip\", \"order\", \"treat\", \"sauce\", \"cookie\", \"snack\", \"taste\", \"local\", \"time\", \"water\", \"sugar\", \"strong\", \"good\", \"dog\", \"salt\", \"cereal\", \"grocery\", \"milk\", \"sweet\", \"butter\", \"drink\", \"peanut\", \"fruit\", \"honey\", \"syrup\", \"juice\", \"lemon\", \"cinnamon\", \"sweetener\", \"apple\", \"caffeine\", \"soda\", \"artificial\", \"mint\", \"cherry\", \"restaurant\", \"homemade\", \"pumpkin\", \"berry\", \"stevia\", \"pocket\", \"refreshing\", \"strawberry\", \"blueberry\", \"chai\", \"hint\", \"orange\", \"shake\", \"unique\", \"beautiful\", \"tart\", \"gram\", \"water\", \"drink\", \"sugar\", \"energy\", \"green\", \"tea\", \"drinking\", \"taste\", \"flavor\", \"sweet\", \"natural\", \"breakfast\", \"good\", \"packet\", \"little\", \"bottle\", \"great\", \"product\", \"love\", \"organic\", \"stuff\", \"light\", \"well\", \"brand\", \"much\", \"thing\", \"food\", \"treat\", \"dog\", \"cat\", \"meal\", \"rice\", \"chicken\", \"baby\", \"grain\", \"picky\", \"formula\", \"noodle\", \"bone\", \"flour\", \"allergy\", \"veggie\", \"pouch\", \"coat\", \"wellness\", \"puppy\", \"eater\", \"feed\", \"salmon\", \"afternoon\", \"shape\", \"blood\", \"ball\", \"onion\", \"raisin\", \"kitty\", \"fish\", \"tooth\", \"ingredient\", \"healthy\", \"diet\", \"month\", \"organic\", \"little\", \"whole\", \"happy\", \"good\", \"great\", \"love\", \"year\", \"small\", \"brand\", \"time\", \"thing\", \"stuff\", \"product\", \"high\", \"order\", \"soup\", \"popcorn\", \"container\", \"can\", \"delivery\", \"plastic\", \"service\", \"training\", \"portion\", \"shipment\", \"condition\", \"sensitive\", \"wine\", \"egg\", \"oil\", \"breath\", \"kibble\", \"fabulous\", \"handy\", \"filling\", \"internet\", \"dairy\", \"ordering\", \"messy\", \"earth\", \"middle\", \"retail\", \"parent\", \"feeding\", \"cooking\", \"bulk\", \"customer\", \"time\", \"last\", \"package\", \"bag\", \"packaging\", \"next\", \"bottle\", \"size\", \"large\", \"product\", \"small\", \"convenient\", \"open\", \"case\", \"item\", \"month\", \"great\", \"first\", \"year\", \"easy\", \"long\", \"excellent\", \"good\", \"problem\", \"purchase\", \"several\", \"happy\", \"many\", \"quality\", \"chip\", \"salt\", \"candy\", \"gift\", \"spicy\", \"cake\", \"salty\", \"hair\", \"pepper\", \"pound\", \"olive\", \"vinegar\", \"tuna\", \"garlic\", \"pancake\", \"pleased\", \"lime\", \"chili\", \"maple\", \"style\", \"freezer\", \"movie\", \"scent\", \"peach\", \"slice\", \"steak\", \"early\", \"incredible\", \"crust\", \"kettle\", \"potato\", \"snack\", \"blue\", \"perfect\", \"nice\", \"flavor\", \"heat\", \"chewy\", \"great\", \"kid\", \"good\", \"little\", \"texture\", \"favorite\", \"taste\", \"love\", \"sweet\", \"tasty\", \"delicious\", \"bag\", \"much\", \"family\", \"healthy\", \"coffee\", \"bean\", \"kcup\", \"blend\", \"bold\", \"roast\", \"cup\", \"maker\", \"french\", \"aroma\", \"pod\", \"brew\", \"decaf\", \"weak\", \"start\", \"espresso\", \"crispy\", \"bitterness\", \"tree\", \"latte\", \"mountain\", \"brewing\", \"brewer\", \"leaf\", \"selection\", \"hazelnut\", \"strength\", \"magic\", \"take\", \"fuel\", \"smooth\", \"strong\", \"machine\", \"vanilla\", \"bitter\", \"rich\", \"dark\", \"morning\", \"drinker\", \"flavor\", \"ground\", \"good\", \"black\", \"nice\", \"taste\", \"perfect\", \"favorite\", \"great\", \"full\", \"brand\", \"chocolate\", \"cookie\", \"butter\", \"peanut\", \"bar\", \"coconut\", \"almond\", \"oatmeal\", \"fiber\", \"salad\", \"cocoa\", \"creamy\", \"pricey\", \"smoothie\", \"balance\", \"dessert\", \"enjoy\", \"pantry\", \"raspberry\", \"supplement\", \"kick\", \"mango\", \"mustard\", \"fruity\", \"vegetarian\", \"holiday\", \"bonus\", \"owner\", \"intense\", \"delicate\", \"milk\", \"protein\", \"calorie\", \"cold\", \"amazing\", \"delicious\", \"good\", \"cream\", \"sweet\", \"taste\", \"great\", \"tasty\", \"flavor\", \"sugar\", \"dark\", \"love\", \"healthy\", \"little\", \"favorite\", \"price\", \"store\", \"local\", \"grocery\", \"medium\", 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\"snack\", \"piece\", \"company\", \"shipping\", \"package\", \"item\", \"ingredient\", \"size\", \"nice\", \"small\", \"many\", \"star\", \"review\", \"bread\", \"ginger\", \"clean\", \"pill\", \"stomach\", \"mild\", \"skin\", \"italian\", \"vegan\", \"baking\", \"canned\", \"mother\", \"tablespoon\", \"jar\", \"adult\", \"licorice\", \"plant\", \"teaspoon\", \"instruction\", \"roll\", \"program\", \"birthday\", \"bill\", \"mix\", \"winter\", \"fancy\", \"frozen\", \"yeast\", \"scoop\", \"flower\", \"care\", \"help\", \"trouble\", \"hour\", \"day\", \"result\", \"week\", \"year\", \"time\", \"work\", \"minute\", \"weight\", \"wonderful\", \"product\", \"easy\", \"long\", \"problem\", \"many\", \"water\", \"month\", \"sauce\", \"cereal\", \"cheese\", \"pasta\", \"seed\", \"beef\", \"nut\", \"date\", \"shelf\", \"tomato\", \"pizza\", \"old\", \"jerky\", \"everyday\", \"fridge\", \"herbal\", \"pork\", \"nuts\", \"staple\", \"basis\", \"toast\", \"stale\", \"tough\", \"expiration\", 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764.5387514342976, 723.5182310532354, 700.6943693753385, 652.6658008219372, 620.5553428798934, 597.1617539017196, 587.994014184099, 535.7066257283384, 523.9897122925739, 510.00436182538294, 505.5663285364642, 481.13929633061514, 466.4979134406044, 436.03251470523645, 414.6914967780466, 395.74767714102023, 349.451839973471, 1028.0156517321288, 1349.1000090318046, 980.3871922823989, 1520.7651794940741, 1916.6084474253346, 922.0668023495668, 3588.6542806078824, 1481.7439149553, 787.2589747432831, 1022.7672891754855, 1265.357937121606, 881.6878700193505, 824.7727111313882, 939.9067366520247, 884.650617059327, 737.2963047826038], \"Total\": [30007.0, 21620.0, 13131.0, 12459.0, 30629.0, 9658.0, 31250.0, 8361.0, 7096.0, 7258.0, 7212.0, 5229.0, 5969.0, 8352.0, 21639.0, 5417.0, 17459.0, 9524.0, 8695.0, 7037.0, 44444.0, 5635.0, 5102.0, 3864.0, 4202.0, 5476.0, 9535.0, 4101.0, 6633.0, 3712.0, 4491.061154590195, 3051.9533482603642, 2645.4051622391294, 2363.954953146998, 1916.0402722937638, 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21620.321070538314, 7212.084607206732, 5635.609772172741, 3395.160701344313, 3062.7364282525864, 2754.7788316784045, 2662.397675892931, 2317.496332082845, 1665.4701596505438, 1528.3102233810073, 1464.2788225584256, 1372.6696637072453, 1362.9680635163759, 1276.070553075877, 1146.0730270265492, 1094.3066310318447, 1048.4730262677626, 953.8658199422691, 901.1728556854332, 897.7718718533262, 853.9856532724626, 836.9790412359268, 806.1219205326142, 776.6086219815322, 719.1491480319805, 678.0389733146202, 638.2260608217128, 626.6680466198683, 558.2755761830399, 527.800187471441, 1219.0067567732976, 2432.5173167992957, 5668.3854549281405, 7107.567875741096, 3572.611312120448, 5953.245534921293, 4615.143126111528, 14132.219849168741, 4377.993132449574, 5099.812093959377, 44444.10521149404, 32166.681926482153, 13486.75315088715, 10844.594123070929, 8012.898451520745, 10013.134288636156, 17459.831649116404, 8062.961496159155, 6183.6107771839415, 30629.485085208817, 5129.018560102422, 7258.142803543798, 3012.9823207522013, 2971.07214780397, 2619.861387923084, 2206.5806876585457, 1992.594731845657, 1746.0809458635315, 1428.8842726664957, 1319.6163313658972, 1271.135178737256, 1125.6354791675674, 1103.5325312705688, 881.8712645547596, 873.0437234548144, 852.2484811583764, 693.6333020075753, 643.8298591162156, 598.1263750313462, 594.262392129343, 545.3392679121902, 539.5471754372828, 535.9819951074488, 487.0837450481097, 486.99457843279833, 482.3551666956208, 469.69667605193484, 446.194524672843, 428.9894101870694, 421.1689705421433, 414.3591743398368, 1439.609667479148, 1419.5095747982016, 1070.5640268061227, 17459.831649116404, 4075.3618237742808, 5582.938296888978, 5227.23249807692, 2118.088815755397, 1793.2847716611257, 4813.690038308232, 5695.106697881953, 5112.542400739225, 30629.485085208817, 8012.898451520745, 1776.6354816590401, 2160.746342290658, 3717.844925548512, 3743.0536259831215, 5953.245534921293, 32166.681926482153, 5840.19706753801, 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      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "7     -0.141351  0.047873       1        1  16.145686\n",
       "5      0.037246  0.053409       2        1  12.615354\n",
       "3      0.173563  0.021284       3        1  12.407233\n",
       "0     -0.144942  0.014901       4        1  11.442962\n",
       "9     -0.130926  0.036637       5        1  11.124016\n",
       "8     -0.208080 -0.020109       6        1   9.167505\n",
       "6      0.055318 -0.236970       7        1   7.714573\n",
       "1      0.270478 -0.168272       8        1   6.810434\n",
       "4      0.192866  0.298452       9        1   6.712845\n",
       "2     -0.104172 -0.047204      10        1   5.859394, topic_info=        Term          Freq         Total Category  logprob  loglift\n",
       "211   coffee  30007.000000  30007.000000  Default  30.0000  30.0000\n",
       "384     food  21620.000000  21620.000000  Default  29.0000  29.0000\n",
       "28     price  13131.000000  13131.000000  Default  28.0000  28.0000\n",
       "98     store  12459.000000  12459.000000  Default  27.0000  27.0000\n",
       "50   product  30629.000000  30629.000000  Default  26.0000  26.0000\n",
       "..       ...           ...           ...      ...      ...      ...\n",
       "540     easy    881.687870   6233.924390  Topic10  -4.8335   0.8812\n",
       "144    tasty    824.772711   4953.834953  Topic10  -4.9002   1.0443\n",
       "222   little    939.906737  14132.219849  Topic10  -4.7695   0.1267\n",
       "16      year    884.650617  10844.594123  Topic10  -4.8301   0.3309\n",
       "184    piece    737.296305   3062.060625  Topic10  -5.0123   1.4133\n",
       "\n",
       "[548 rows x 6 columns], token_table=      Topic      Freq       Term\n",
       "term                            \n",
       "1224      9  0.997894      adult\n",
       "1982      2  0.999216  afternoon\n",
       "1963      2  0.999064    allergy\n",
       "844       6  0.999464     almond\n",
       "3305      7  0.997322       alot\n",
       "...     ...       ...        ...\n",
       "16       10  0.081607       year\n",
       "940       9  0.996954      yeast\n",
       "17        8  0.998159     yellow\n",
       "3332      8  0.999496     yogurt\n",
       "1137      8  0.999393      yummy\n",
       "\n",
       "[822 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[8, 6, 4, 1, 10, 9, 7, 2, 5, 3])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the topic modeling\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, doc_term_matrix, dictionary)\n",
    "vis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Compute Model Perplexity and Coherence Score**\n",
    "- Provide a convenient measure to judge how good a given topic model is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perplexity:  -8.661281920727216\n",
      "\n",
      "Coherence Score:  0.41794426803997153\n"
     ]
    }
   ],
   "source": [
    "#Compute Model Perplexity and Coherence Score\n",
    "print('\\nPerplexity: ', lda_model.log_perplexity(doc_term_matrix,total_docs=10000))  \n",
    "#a measure of how good the model is. lower the better.\n",
    "\n",
    "# Compute Coherence Score\n",
    "from gensim.models.coherencemodel import CoherenceModel\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, \n",
    "                                    texts=tokenized_reviews, \n",
    "                                    dictionary=dictionary , \n",
    "                                    coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coherence score is 0.42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#find the optimal number of topics for lda model\n",
    "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
    "    \"\"\"\n",
    "    Compute c_v coherence for various number of topics\n",
    "\n",
    "    Parameters:\n",
    "    ----------\n",
    "    dictionary : Gensim dictionary\n",
    "    corpus : Gensim corpus\n",
    "    texts : List of input texts\n",
    "    limit : Max num of topics\n",
    "\n",
    "    Returns:\n",
    "    -------\n",
    "    model_list : List of LDA topic models\n",
    "    coherence_values : Coherence values corresponding to the LDA model with respective number of topics\n",
    "    \"\"\"\n",
    "    coherence_values = []\n",
    "    model_list = []\n",
    "    for num_topics in range(start, limit, step):\n",
    "        model = gensim.models.ldamodel.LdaModel(corpus=corpus, num_topics=num_topics, id2word=dictionary)\n",
    "        model_list.append(model)\n",
    "        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "        coherence_values.append(coherencemodel.get_coherence())\n",
    "\n",
    "    return model_list, coherence_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_list, coherence_values = compute_coherence_values(dictionary=dictionary, corpus=doc_term_matrix, texts=tokenized_reviews, start=2, limit=50, step=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show graph\n",
    "limit=50; start=2; step=1;\n",
    "x = range(start, limit, step)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Topics = 2  has Coherence Value of 0.2863\n",
      "Num Topics = 3  has Coherence Value of 0.3488\n",
      "Num Topics = 4  has Coherence Value of 0.3492\n",
      "Num Topics = 5  has Coherence Value of 0.3594\n",
      "Num Topics = 6  has Coherence Value of 0.4134\n",
      "Num Topics = 7  has Coherence Value of 0.4387\n",
      "Num Topics = 8  has Coherence Value of 0.4166\n",
      "Num Topics = 9  has Coherence Value of 0.4418\n",
      "Num Topics = 10  has Coherence Value of 0.4094\n",
      "Num Topics = 11  has Coherence Value of 0.3902\n",
      "Num Topics = 12  has Coherence Value of 0.3917\n",
      "Num Topics = 13  has Coherence Value of 0.397\n",
      "Num Topics = 14  has Coherence Value of 0.4273\n",
      "Num Topics = 15  has Coherence Value of 0.4412\n",
      "Num Topics = 16  has Coherence Value of 0.415\n",
      "Num Topics = 17  has Coherence Value of 0.427\n",
      "Num Topics = 18  has Coherence Value of 0.3984\n",
      "Num Topics = 19  has Coherence Value of 0.4086\n",
      "Num Topics = 20  has Coherence Value of 0.4046\n",
      "Num Topics = 21  has Coherence Value of 0.4033\n",
      "Num Topics = 22  has Coherence Value of 0.3983\n",
      "Num Topics = 23  has Coherence Value of 0.4133\n",
      "Num Topics = 24  has Coherence Value of 0.3992\n",
      "Num Topics = 25  has Coherence Value of 0.3761\n",
      "Num Topics = 26  has Coherence Value of 0.3862\n",
      "Num Topics = 27  has Coherence Value of 0.393\n",
      "Num Topics = 28  has Coherence Value of 0.3868\n",
      "Num Topics = 29  has Coherence Value of 0.3599\n",
      "Num Topics = 30  has Coherence Value of 0.3782\n",
      "Num Topics = 31  has Coherence Value of 0.3741\n",
      "Num Topics = 32  has Coherence Value of 0.3741\n",
      "Num Topics = 33  has Coherence Value of 0.3648\n",
      "Num Topics = 34  has Coherence Value of 0.3655\n",
      "Num Topics = 35  has Coherence Value of 0.3628\n",
      "Num Topics = 36  has Coherence Value of 0.3596\n",
      "Num Topics = 37  has Coherence Value of 0.3726\n",
      "Num Topics = 38  has Coherence Value of 0.3493\n",
      "Num Topics = 39  has Coherence Value of 0.335\n",
      "Num Topics = 40  has Coherence Value of 0.3342\n",
      "Num Topics = 41  has Coherence Value of 0.3544\n",
      "Num Topics = 42  has Coherence Value of 0.3378\n",
      "Num Topics = 43  has Coherence Value of 0.3487\n",
      "Num Topics = 44  has Coherence Value of 0.3468\n",
      "Num Topics = 45  has Coherence Value of 0.3253\n",
      "Num Topics = 46  has Coherence Value of 0.3329\n",
      "Num Topics = 47  has Coherence Value of 0.3342\n",
      "Num Topics = 48  has Coherence Value of 0.3301\n",
      "Num Topics = 49  has Coherence Value of 0.3241\n"
     ]
    }
   ],
   "source": [
    "# Print the coherence scores\n",
    "for m, cv in zip(x, coherence_values):\n",
    "    print(\"Num Topics =\", m, \" has Coherence Value of\", round(cv, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.080*\"chocolate\" + 0.050*\"cookie\" + 0.034*\"butter\" + 0.031*\"peanut\" + 0.020*\"milk\" + 0.019*\"taste\" + 0.018*\"great\" + 0.016*\"flavor\" + 0.016*\"good\" + 0.014*\"oatmeal\"'),\n",
       " (1,\n",
       "  '0.036*\"chip\" + 0.025*\"salt\" + 0.025*\"good\" + 0.024*\"flavor\" + 0.018*\"great\" + 0.014*\"product\" + 0.013*\"store\" + 0.011*\"love\" + 0.011*\"fresh\" + 0.010*\"time\"'),\n",
       " (2,\n",
       "  '0.039*\"treat\" + 0.025*\"dog\" + 0.021*\"time\" + 0.020*\"small\" + 0.018*\"product\" + 0.015*\"great\" + 0.014*\"size\" + 0.013*\"little\" + 0.011*\"year\" + 0.011*\"large\"'),\n",
       " (3,\n",
       "  '0.129*\"coffee\" + 0.047*\"flavor\" + 0.031*\"good\" + 0.027*\"strong\" + 0.021*\"taste\" + 0.015*\"vanilla\" + 0.013*\"bean\" + 0.013*\"blend\" + 0.013*\"great\" + 0.012*\"green\"'),\n",
       " (4,\n",
       "  '0.048*\"water\" + 0.027*\"drink\" + 0.021*\"taste\" + 0.020*\"bottle\" + 0.019*\"bread\" + 0.017*\"flavor\" + 0.015*\"energy\" + 0.012*\"cold\" + 0.012*\"product\" + 0.012*\"good\"'),\n",
       " (5,\n",
       "  '0.036*\"good\" + 0.031*\"sweet\" + 0.031*\"sugar\" + 0.028*\"flavor\" + 0.024*\"snack\" + 0.023*\"taste\" + 0.018*\"great\" + 0.017*\"fruit\" + 0.017*\"calorie\" + 0.016*\"healthy\"'),\n",
       " (6,\n",
       "  '0.026*\"good\" + 0.023*\"sauce\" + 0.017*\"great\" + 0.013*\"flavor\" + 0.013*\"popcorn\" + 0.012*\"taste\" + 0.011*\"soup\" + 0.011*\"product\" + 0.010*\"pasta\" + 0.010*\"time\"'),\n",
       " (7,\n",
       "  '0.111*\"food\" + 0.020*\"good\" + 0.017*\"cat\" + 0.015*\"brand\" + 0.015*\"ingredient\" + 0.012*\"baby\" + 0.012*\"healthy\" + 0.011*\"product\" + 0.010*\"organic\" + 0.009*\"stuff\"'),\n",
       " (8,\n",
       "  '0.044*\"price\" + 0.038*\"product\" + 0.035*\"store\" + 0.031*\"great\" + 0.030*\"good\" + 0.015*\"order\" + 0.015*\"local\" + 0.014*\"time\" + 0.012*\"shipping\" + 0.011*\"grocery\"')]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the model and print the topics\n",
    "optimal_model = model_list[7]\n",
    "model_topics = optimal_model.show_topics(formatted=False)\n",
    "optimal_model.print_topics(num_words=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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_el1051623523118350484869475138\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el1051623523118350484869475138_data = {\"mdsDat\": {\"x\": [0.15151825029083568, -0.10266364767857948, -0.17345217896660362, 0.04834174666157919, 0.210839673045624, -0.0058109909994191015, 0.12654712980318547, -0.07811003784683718, -0.17720994430978487], \"y\": [-0.06143184621598557, 0.1487078855569641, -0.17788905638963076, -0.01834827994590328, -0.02627499153624969, -0.10192146006723574, 0.15834359832838538, -0.0657623980650075, 0.14457654833466305], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [15.58229291033457, 12.897932359424457, 12.4573693071486, 12.130062483652443, 11.123560691494463, 10.470415400059897, 10.009680356860155, 8.501474609569444, 6.8272118814559715]}, \"tinfo\": {\"Term\": [\"coffee\", \"food\", \"chocolate\", \"price\", \"water\", \"treat\", \"cookie\", \"chip\", \"store\", \"sugar\", \"flavor\", \"sweet\", \"butter\", \"salt\", \"strong\", \"dog\", \"snack\", \"peanut\", \"drink\", \"sauce\", \"taste\", \"fruit\", \"ingredient\", \"milk\", \"vanilla\", \"bread\", \"bottle\", \"cat\", \"healthy\", \"calorie\", \"program\", \"bonus\", \"cent\", \"craving\", \"delightful\", \"amazed\", \"basket\", \"hearty\", \"savory\", \"beer\", \"storage\", \"sausage\", \"addicted\", \"shopping\", \"ease\", \"charge\", \"drawer\", \"fudge\", \"useful\", \"secret\", \"hot\", \"pricing\", \"core\", \"opportunity\", \"bran\", \"sport\", \"saver\", \"vacuum\", \"monthly\", \"sore\", \"shipping\", \"discount\", \"gift\", \"vendor\", \"birthday\", \"price\", \"cost\", \"reasonable\", \"bargain\", \"seller\", \"deal\", \"quantity\", \"service\", \"store\", \"item\", \"local\", \"ship\", \"grocery\", \"order\", \"thank\", \"bulk\", \"box\", \"subscription\", \"licorice\", \"sale\", \"online\", \"purchase\", \"product\", \"cheap\", \"supermarket\", \"pack\", \"cake\", \"stock\", \"available\", \"delivery\", \"great\", \"happy\", \"value\", \"package\", \"good\", \"time\", \"bag\", \"worth\", \"expensive\", \"free\", \"quality\", \"size\", \"year\", \"excellent\", \"small\", \"well\", \"apple\", \"fruit\", \"pricey\", \"pancake\", \"strawberry\", \"beautiful\", \"carb\", \"affordable\", \"diabetic\", \"splenda\", \"carbs\", \"trick\", \"syrup\", \"chamomile\", \"waffle\", \"cranberry\", \"antioxidant\", \"dense\", \"wafer\", \"agave\", \"sweetener\", \"sugarfree\", \"juice\", \"cider\", \"sweeten\", \"pleasure\", \"fructose\", \"pineapple\", \"pear\", \"cane\", \"honey\", \"toddler\", \"cherry\", \"plate\", \"sugar\", \"potato\", \"artificial\", \"almond\", \"sweet\", \"calorie\", \"gluten\", \"crisp\", \"snack\", \"dairy\", \"moist\", \"protein\", \"cereal\", \"flour\", \"healthy\", \"free\", \"kid\", \"natural\", \"diet\", \"ingredient\", \"taste\", \"good\", \"flavor\", \"bar\", \"great\", \"high\", \"product\", \"little\", \"tasty\", \"love\", \"regular\", \"roast\", \"bold\", \"french\", \"vanilla\", \"chai\", \"peppermint\", \"raisin\", \"decaf\", \"tea\", \"travel\", \"aroma\", \"hazelnut\", \"espresso\", \"flower\", \"mocha\", \"medication\", \"press\", \"bodied\", \"aromatic\", \"mellow\", \"ratio\", \"snacking\", \"oolong\", \"trade\", \"creme\", \"donut\", \"herbal\", \"bitterness\", \"earthy\", \"vacation\", \"coffee\", \"blend\", \"pod\", \"steep\", \"mountain\", \"brew\", \"weak\", \"grape\", \"fuel\", \"bean\", \"bitter\", \"strong\", \"hint\", \"drinker\", \"black\", \"smooth\", \"brewing\", \"green\", \"rich\", \"maker\", \"medium\", \"dark\", \"kcup\", \"flavor\", \"morning\", \"cinnamon\", \"taste\", \"good\", \"favorite\", \"brand\", \"nice\", \"great\", \"love\", \"different\", \"little\", \"tablespoon\", \"fridge\", \"pizza\", \"staple\", \"terrific\", \"kettle\", \"jalapeno\", \"dough\", \"expiration\", \"bear\", \"flax\", \"preservative\", \"gummy\", \"pecan\", \"childhood\", \"hooked\", \"raman\", \"miracle\", \"experiment\", \"automatic\", \"pickle\", \"satisfie\", \"pitcher\", \"boil\", \"finding\", \"mg\", \"batter\", \"fragrant\", \"spinach\", \"supplier\", \"chip\", \"salt\", \"date\", \"sesame\", \"vegetarian\", \"warehouse\", \"cracker\", \"firm\", \"salty\", \"saving\", \"vinegar\", \"seed\", \"sodium\", \"stale\", \"jar\", \"shelf\", \"cheddar\", \"share\", \"jerky\", \"fiber\", \"case\", \"pepper\", \"fresh\", \"spicy\", \"can\", \"cheese\", \"flavor\", \"bag\", \"original\", \"good\", \"snack\", \"store\", \"great\", \"one\", \"love\", \"product\", \"year\", \"time\", \"delicious\", \"brand\", \"taste\", \"favorite\", \"order\", \"many\", \"little\", \"tasty\", \"local\", \"treat\", \"pocket\", \"bone\", \"greenie\", \"iced\", \"pill\", \"addictive\", \"handy\", \"effective\", \"liver\", \"toy\", \"busy\", \"chew\", \"skeptical\", \"rawhide\", \"finicky\", \"consistent\", \"trap\", \"calm\", \"feel\", \"sized\", \"smoke\", \"basic\", \"mile\", \"hesitant\", \"college\", \"worry\", \"duck\", \"increase\", \"chewer\", \"ball\", \"gravy\", \"inch\", \"litter\", \"pain\", \"dog\", \"hour\", \"tooth\", \"sleep\", \"piece\", \"puppy\", \"small\", \"biscuit\", \"size\", \"night\", \"roll\", \"minute\", \"large\", \"time\", \"long\", \"last\", \"little\", \"product\", \"work\", \"year\", \"thing\", \"problem\", \"day\", \"week\", \"great\", \"month\", \"love\", \"easy\", \"good\", \"hard\", \"first\", \"popcorn\", \"hair\", \"sauce\", \"noodle\", \"vegan\", \"mustard\", \"toast\", \"shampoo\", \"guest\", \"pasta\", \"sardine\", \"take\", \"incredible\", \"microwave\", \"conditioner\", \"filter\", \"popper\", \"salad\", \"kernel\", \"truffle\", \"dressing\", \"golden\", \"salsa\", \"setting\", \"spaghetti\", \"occasional\", \"theater\", \"boy\", \"wasabi\", \"leftover\", \"robust\", \"guilty\", \"cook\", \"movie\", \"tomato\", \"restaurant\", \"dish\", \"soup\", \"chili\", \"pea\", \"mild\", \"heat\", \"cooking\", \"cup\", \"lime\", \"meal\", \"garlic\", \"machine\", \"olive\", \"cheese\", \"subtle\", \"rice\", \"good\", \"easy\", \"perfect\", \"great\", \"minute\", \"spicy\", 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\"fabulous\", \"boost\", \"leaf\", \"workout\", \"yeast\", \"economical\", \"gum\", \"winner\", \"loaf\", \"resealable\", \"memory\", \"shake\", \"squeeze\", \"banana\", \"magic\", \"portable\", \"degree\", \"alcohol\", \"beat\", \"marinade\", \"trail\", \"mate\", \"fond\", \"few\", \"want\", \"cold\", \"wine\", \"beverage\", \"gallon\", \"holiday\", \"gram\", \"refreshing\", \"brownie\", \"caramel\", \"delicate\", \"water\", \"energy\", \"glass\", \"bottle\", \"drink\", \"powder\", \"benefit\", \"cream\", \"summer\", \"liquid\", \"coconut\", \"milk\", \"taste\", \"packet\", \"flavor\", \"wonderful\", \"product\", \"recipe\", \"amazing\", \"time\", \"good\", \"stuff\", \"great\", \"little\", \"extra\", \"sweet\", \"morning\", \"peanut\", \"butter\", \"smoothie\", \"ginger\", \"bacon\", \"chocolate\", \"auto\", \"oatmeal\", \"specialty\", \"guilt\", \"oats\", \"calcium\", \"oily\", \"compliment\", \"friendly\", \"melt\", \"specific\", \"gentle\", \"student\", \"feast\", \"cookie\", 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\"guilty\", \"guilty\", \"gum\", \"gummy\", \"hair\", \"handy\", \"happy\", \"happy\", \"happy\", \"happy\", \"happy\", \"happy\", \"happy\", \"happy\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hazelnut\", \"health\", \"health\", \"health\", \"health\", \"health\", \"health\", \"health\", \"healthy\", \"healthy\", \"healthy\", \"healthy\", \"healthy\", \"healthy\", \"healthy\", \"healthy\", \"hearty\", \"heat\", \"heat\", \"heat\", \"heat\", \"heat\", \"heat\", \"herbal\", \"herbal\", \"hesitant\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"hint\", \"hint\", \"hint\", \"hip\", \"holiday\", \"holiday\", \"honey\", \"honey\", \"honey\", \"hooked\", \"hot\", \"hour\", \"hour\", \"hour\", \"hour\", \"hour\", \"hour\", \"husband\", \"husband\", \"husband\", \"husband\", \"husband\", \"iced\", \"inch\", \"increase\", \"incredible\", \"infection\", \"infection\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"item\", \"item\", \"item\", \"item\", \"item\", \"item\", \"item\", \"item\", \"item\", \"jalapeno\", \"jar\", \"jar\", \"jar\", \"jar\", \"jerky\", \"jerky\", \"jerky\", \"joint\", \"juice\", \"juice\", \"juice\", \"kcup\", \"kcup\", \"kcup\", \"kernel\", \"kettle\", \"kid\", \"kid\", \"kid\", \"kid\", \"kid\", \"kid\", \"kid\", \"kitten\", \"kitten\", \"kitty\", \"large\", \"large\", \"large\", \"large\", \"large\", \"large\", \"large\", \"large\", \"large\", \"last\", \"last\", \"last\", \"last\", \"last\", \"last\", \"last\", \"last\", \"last\", \"lavender\", \"leaf\", \"leftover\", \"lemon\", \"lemon\", \"lemon\", \"lemon\", \"licorice\", \"licorice\", \"lime\", \"lime\", \"lime\", \"liquid\", \"liquid\", \"liquid\", \"liquid\", \"liquid\", \"liquid\", \"liquid\", \"litter\", \"little\", \"little\", \"little\", \"little\", \"little\", \"little\", \"little\", \"little\", \"little\", \"liver\", \"loaf\", \"local\", \"local\", \"local\", \"local\", \"local\", \"local\", \"local\", \"local\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"lover\", \"lover\", \"lover\", \"lover\", \"lover\", \"lowfat\", \"machine\", \"machine\", \"machine\", \"magic\", \"maker\", \"maker\", \"maker\", \"maker\", \"many\", \"many\", \"many\", \"many\", \"many\", \"many\", \"many\", \"many\", \"many\", \"maple\", \"maple\", \"marinade\", \"mate\", \"meal\", \"meal\", \"meal\", \"meal\", \"meal\", \"meal\", \"meat\", \"meat\", \"meat\", \"meat\", \"meat\", \"meat\", \"meat\", \"medication\", \"medium\", \"medium\", \"medium\", \"medium\", \"mellow\", \"melt\", \"memory\", \"mg\", \"microwave\", \"microwave\", \"mild\", \"mild\", \"mile\", \"milk\", \"milk\", \"milk\", \"milk\", \"milk\", \"milk\", \"milk\", \"milk\", \"minute\", \"minute\", \"minute\", \"minute\", \"minute\", \"minute\", \"minute\", \"minute\", \"minute\", \"miracle\", \"mocha\", \"moist\", \"moist\", \"moist\", \"moist\", \"month\", \"month\", \"month\", \"month\", \"month\", \"month\", \"month\", \"month\", \"month\", \"monthly\", \"morning\", \"morning\", \"morning\", \"morning\", \"morning\", \"morning\", \"morning\", \"mountain\", \"mountain\", \"mountain\", \"movie\", \"movie\", \"mustard\", \"natural\", \"natural\", \"natural\", \"natural\", \"natural\", \"natural\", \"natural\", \"nice\", \"nice\", \"nice\", \"nice\", \"nice\", \"nice\", \"nice\", \"nice\", \"nice\", \"night\", \"night\", \"night\", \"night\", \"night\", \"night\", \"night\", \"noodle\", \"noodle\", \"nut\", \"nut\", \"nut\", \"nut\", \"nutty\", \"oatmeal\", \"oatmeal\", \"oatmeal\", \"oats\", \"occasional\", \"oily\", \"olive\", \"olive\", \"olive\", \"one\", \"one\", \"one\", \"one\", \"one\", \"one\", \"one\", \"one\", \"one\", \"online\", \"online\", \"online\", \"online\", \"online\", \"online\", \"oolong\", \"opportunity\", \"order\", \"order\", \"order\", \"order\", \"order\", \"order\", \"order\", \"order\", \"order\", \"organic\", \"organic\", \"organic\", \"organic\", \"organic\", \"organic\", \"organic\", \"organic\", \"organic\", \"original\", \"original\", \"original\", \"original\", \"original\", \"original\", \"original\", \"original\", \"pack\", \"pack\", \"pack\", \"pack\", \"pack\", \"pack\", \"pack\", \"pack\", \"pack\", \"package\", \"package\", \"package\", \"package\", \"package\", \"package\", \"package\", \"package\", \"package\", \"packet\", \"packet\", \"packet\", \"packet\", \"packet\", \"packet\", \"packet\", \"pain\", \"pain\", \"pain\", \"pain\", \"pancake\", \"pasta\", \"pasta\", \"pasta\", \"pea\", \"pea\", \"peanut\", \"pear\", \"pecan\", \"pepper\", \"pepper\", \"pepper\", \"pepper\", \"peppermint\", \"perfect\", \"perfect\", \"perfect\", \"perfect\", \"perfect\", \"perfect\", \"pickle\", \"picky\", \"picky\", \"piece\", \"piece\", 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\"purchase\", \"purchase\", \"purchase\", \"purchase\", \"purse\", \"quality\", \"quality\", \"quality\", \"quality\", \"quality\", \"quality\", \"quality\", \"quality\", \"quality\", \"quantity\", \"quantity\", \"quantity\", \"question\", \"question\", \"raisin\", \"raman\", \"ratio\", \"rawhide\", \"reasonable\", \"reasonable\", \"reasonable\", \"reasonable\", \"reasonable\", \"reasonable\", \"recipe\", \"recipe\", \"recipe\", \"recipe\", \"recipe\", \"recipe\", \"recipe\", \"refreshing\", \"refreshing\", \"refreshing\", \"refrigerated\", \"regular\", \"regular\", \"regular\", \"regular\", \"regular\", \"regular\", \"regular\", \"regular\", \"regular\", \"resealable\", \"restaurant\", \"restaurant\", \"restaurant\", \"restaurant\", \"reward\", \"rice\", \"rice\", \"rice\", \"rice\", \"rice\", \"rice\", \"rice\", \"rich\", \"rich\", \"rich\", \"rich\", \"roast\", \"robust\", \"robust\", \"roll\", \"roll\", \"roll\", \"roll\", \"roll\", \"rush\", \"salad\", \"salad\", \"salad\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"salmon\", \"salmon\", \"salsa\", \"salt\", \"salt\", \"salt\", \"salt\", \"salt\", \"salty\", \"salty\", \"salty\", \"sardine\", \"satisfie\", \"sauce\", \"sauce\", \"sauce\", \"sausage\", \"saver\", \"saving\", \"saving\", \"saving\", \"savory\", \"science\", \"seafood\", \"secret\", \"seed\", \"seed\", \"seed\", \"seed\", \"seed\", \"seller\", \"seller\", \"seller\", \"seller\", \"seller\", \"service\", \"service\", \"service\", \"service\", \"service\", \"sesame\", \"sesame\", \"setting\", \"shake\", \"shake\", \"shampoo\", \"share\", \"share\", \"share\", \"shelf\", \"shelf\", \"shelf\", \"shelf\", \"shelf\", \"shelf\", \"shelf\", \"shiny\", \"shiny\", \"shiny\", \"ship\", \"ship\", \"shipping\", \"shipping\", \"shipping\", \"shopping\", \"size\", \"size\", \"size\", \"size\", \"size\", \"size\", \"size\", \"size\", \"size\", \"sized\", \"sized\", \"skeptical\", \"skeptical\", \"skin\", \"sleep\", \"sleep\", \"sleep\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"smoke\", \"smooth\", \"smooth\", \"smooth\", \"smooth\", \"smoothie\", \"snack\", \"snack\", \"snack\", \"snack\", \"snack\", \"snack\", \"snack\", \"snacking\", \"soda\", \"soda\", \"soda\", \"soda\", \"sodium\", \"sodium\", \"sodium\", \"soft\", \"soft\", \"soft\", \"soft\", \"soft\", \"soft\", \"soft\", \"soft\", \"sore\", \"soup\", \"soup\", \"soup\", \"spaghetti\", \"specialty\", \"specific\", \"spicy\", \"spicy\", \"spicy\", \"spicy\", \"spinach\", \"splenda\", \"sport\", \"squeeze\", \"stale\", \"stale\", \"stale\", \"stale\", \"stale\", \"stale\", \"staple\", \"steep\", \"steep\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"storage\", \"store\", \"store\", \"store\", \"store\", \"store\", \"store\", \"store\", \"store\", \"store\", \"strawberry\", \"strong\", \"strong\", \"strong\", \"strong\", \"strong\", \"strong\", 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\"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"theater\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"toast\", \"toddler\", \"toddler\", \"tomato\", \"tomato\", \"tomato\", \"tooth\", \"tooth\", \"tooth\", \"tooth\", \"tooth\", \"tooth\", \"tooth\", \"toy\", \"trade\", \"trail\", \"training\", \"training\", \"trap\", \"travel\", \"treat\", \"treat\", \"tree\", \"trick\", \"truffle\", \"tuna\", \"tuna\", \"unique\", \"useful\", \"vacation\", \"vacuum\", \"value\", \"value\", \"value\", \"value\", \"value\", \"value\", \"vanilla\", \"vanilla\", \"vegan\", \"vegetarian\", \"vegetarian\", \"vendor\", \"vendor\", \"versatile\", \"vinegar\", \"vinegar\", \"vinegar\", \"visit\", \"wafer\", \"waffle\", \"want\", \"warehouse\", \"wasabi\", \"water\", \"water\", \"water\", \"water\", \"water\", \"weak\", \"weak\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"wellness\", \"wine\", \"wine\", \"winner\", \"wonderful\", \"wonderful\", \"wonderful\", \"wonderful\", \"wonderful\", \"wonderful\", \"wonderful\", \"wonderful\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"workout\", \"worry\", \"worth\", \"worth\", \"worth\", \"worth\", \"worth\", \"worth\", \"worth\", \"worth\", \"worth\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"yogurt\", \"yogurt\", \"yummy\", \"yummy\", \"yummy\", \"yummy\", \"yummy\", \"zuke\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [9, 6, 4, 2, 3, 7, 8, 5, 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_el1051623523118350484869475138\", ldavis_el1051623523118350484869475138_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_el1051623523118350484869475138\", ldavis_el1051623523118350484869475138_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_el1051623523118350484869475138\", ldavis_el1051623523118350484869475138_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "8      0.151518 -0.061432       1        1  15.582293\n",
       "5     -0.102664  0.148708       2        1  12.897932\n",
       "3     -0.173452 -0.177889       3        1  12.457369\n",
       "1      0.048342 -0.018348       4        1  12.130062\n",
       "2      0.210840 -0.026275       5        1  11.123561\n",
       "6     -0.005811 -0.101921       6        1  10.470415\n",
       "7      0.126547  0.158344       7        1  10.009680\n",
       "4     -0.078110 -0.065762       8        1   8.501475\n",
       "0     -0.177210  0.144577       9        1   6.827212, topic_info=          Term          Freq         Total Category  logprob  loglift\n",
       "211     coffee  30666.000000  30666.000000  Default  30.0000  30.0000\n",
       "384       food  22000.000000  22000.000000  Default  29.0000  29.0000\n",
       "458  chocolate  10373.000000  10373.000000  Default  28.0000  28.0000\n",
       "28       price  14366.000000  14366.000000  Default  27.0000  27.0000\n",
       "229      water   8868.000000   8868.000000  Default  26.0000  26.0000\n",
       "..         ...           ...           ...      ...      ...      ...\n",
       "3         good   2027.261394  45320.222605   Topic9  -4.1538  -0.4228\n",
       "24        love   1072.239594  13833.032820   Topic9  -4.7907   0.1269\n",
       "462      sweet    932.429657   9482.645531   Topic9  -4.9304   0.3648\n",
       "26        nice    885.644819   8066.596487   Topic9  -4.9819   0.4751\n",
       "144      tasty    821.488988   5226.316256   Topic9  -5.0571   0.8339\n",
       "\n",
       "[655 rows x 6 columns], token_table=      Topic      Freq        Term\n",
       "term                             \n",
       "2666      1  0.996344    addicted\n",
       "3196      5  0.999092   addictive\n",
       "3581      2  0.997750  affordable\n",
       "3824      2  0.998462       agave\n",
       "2793      8  0.996450     alcohol\n",
       "...     ...       ...         ...\n",
       "1137      3  0.018325       yummy\n",
       "1137      4  0.286923       yummy\n",
       "1137      6  0.043457       yummy\n",
       "1137      9  0.370172       yummy\n",
       "3227      7  0.997568        zuke\n",
       "\n",
       "[1676 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[9, 6, 4, 2, 3, 7, 8, 5, 1])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the topics\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(optimal_model, doc_term_matrix, dictionary)\n",
    "vis"
   ]
  }
 ],
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