Bag of Words Meets Bags of Popcorn

This is the code from the first part of the tutorial from kaggle.

Note: This very code scored a 0.84524 in accuracy without any modifications.


In [13]:
import pandas as pd
from bs4 import BeautifulSoup 
import re
import nltk
from nltk.corpus import stopwords
import numpy as np
from sklearn.ensemble import RandomForestClassifier

In [4]:
# the training data
train = pd.read_csv("labeledTrainData.tsv", header=0, \
                    delimiter="\t", quoting=3)

def review_to_words( raw_review ):
    # Function to convert a raw review to a string of words
    # The input is a single string (a raw movie review), and 
    # the output is a single string (a preprocessed movie review)
    #
    # 1. Remove HTML
    review_text = BeautifulSoup(raw_review).get_text() 
    #
    # 2. Remove non-letters        
    letters_only = re.sub("[^a-zA-Z]", " ", review_text) 
    #
    # 3. Convert to lower case, split into individual words
    words = letters_only.lower().split()                             
    #
    # 4. In Python, searching a set is much faster than searching
    #   a list, so convert the stop words to a set
    stops = set(stopwords.words("english"))                  
    # 
    # 5. Remove stop words
    meaningful_words = [w for w in words if not w in stops]   
    #
    # 6. Join the words back into one string separated by space, 
    # and return the result.
    return( " ".join( meaningful_words ))

In [7]:
# Get the number of reviews based on the dataframe column size
num_reviews = train["review"].size

# Initialize an empty list to hold the clean reviews
clean_train_reviews = []

# Loop over each review; create an index i that goes from 0 to the length
# of the movie review list 
print "Cleaning and parsing the training set movie reviews...\n"
for i in xrange( 0, num_reviews ):
    # If the index is evenly divisible by 1000, print a message
    if( (i+1)%1000 == 0 ):
        print "Review %d of %d\n" % ( i+1, num_reviews )                                                                    
    clean_train_reviews.append( review_to_words( train["review"][i] ))


Cleaning and parsing the training set movie reviews...

Review 1000 of 25000

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Review 25000 of 25000


In [8]:
print "Creating the bag of words...\n"
from sklearn.feature_extraction.text import CountVectorizer

# Initialize the "CountVectorizer" object, which is scikit-learn's
# bag of words tool.  
vectorizer = CountVectorizer(analyzer = "word",   \
                             tokenizer = None,    \
                             preprocessor = None, \
                             stop_words = None,   \
                             max_features = 5000) 

# fit_transform() does two functions: First, it fits the model
# and learns the vocabulary; second, it transforms our training data
# into feature vectors. The input to fit_transform should be a list of 
# strings.
train_data_features = vectorizer.fit_transform(clean_train_reviews)

# Numpy arrays are easy to work with, so convert the result to an 
# array
train_data_features = train_data_features.toarray()


Creating the bag of words...


In [9]:
vocab = vectorizer.get_feature_names()
print vocab


[u'abandoned', u'abc', u'abilities', u'ability', u'able', u'abraham', u'absence', u'absent', u'absolute', u'absolutely', u'absurd', u'abuse', u'abusive', u'abysmal', u'academy', u'accent', u'accents', u'accept', u'acceptable', u'accepted', u'access', u'accident', u'accidentally', u'accompanied', u'accomplished', u'according', u'account', u'accuracy', u'accurate', u'accused', u'achieve', u'achieved', u'achievement', u'acid', u'across', u'act', u'acted', u'acting', u'action', u'actions', u'activities', u'actor', u'actors', u'actress', u'actresses', u'acts', u'actual', u'actually', u'ad', u'adam', u'adams', u'adaptation', u'adaptations', u'adapted', u'add', u'added', u'adding', u'addition', u'adds', u'adequate', u'admire', u'admit', u'admittedly', u'adorable', u'adult', u'adults', u'advance', u'advanced', u'advantage', u'adventure', u'adventures', u'advertising', u'advice', u'advise', u'affair', u'affect', u'affected', u'afford', u'aforementioned', u'afraid', u'africa', u'african', u'afternoon', u'afterwards', u'age', u'aged', u'agent', u'agents', u'ages', u'aging', u'ago', u'agree', u'agreed', u'agrees', u'ah', u'ahead', u'aid', u'aids', u'aim', u'aimed', u'ain', u'air', u'aired', u'airplane', u'airport', u'aka', u'akshay', u'al', u'alan', u'alas', u'albeit', u'albert', u'album', u'alcohol', u'alcoholic', u'alec', u'alert', u'alex', u'alexander', u'alfred', u'alice', u'alicia', u'alien', u'aliens', u'alike', u'alison', u'alive', u'allen', u'allow', u'allowed', u'allowing', u'allows', u'almost', u'alone', u'along', u'alongside', u'already', u'alright', u'also', u'alternate', u'although', u'altman', u'altogether', u'always', u'amanda', u'amateur', u'amateurish', u'amazed', u'amazing', u'amazingly', u'ambiguous', u'ambitious', u'america', u'american', u'americans', u'amitabh', u'among', u'amongst', u'amount', u'amounts', u'amusing', u'amy', u'analysis', u'ancient', u'anderson', u'andre', u'andrew', u'andrews', u'andy', u'angel', u'angela', u'angeles', u'angels', u'anger', u'angle', u'angles', u'angry', u'animal', u'animals', u'animated', u'animation', u'anime', u'ann', u'anna', u'anne', u'annie', u'annoyed', u'annoying', u'another', u'answer', u'answers', u'anthony', u'anti', u'antics', u'antonioni', u'antwone', u'anybody', u'anymore', u'anyone', u'anything', u'anyway', u'anyways', u'anywhere', u'apart', u'apartment', u'ape', u'apes', u'appalling', u'apparent', u'apparently', u'appeal', u'appealing', u'appear', u'appearance', u'appearances', u'appeared', u'appearing', u'appears', u'appreciate', u'appreciated', u'appreciation', u'approach', u'appropriate', u'april', u'area', u'areas', u'aren', u'arguably', u'argue', u'argument', u'arm', u'armed', u'arms', u'army', u'arnold', u'around', u'arrested', u'arrival', u'arrive', u'arrived', u'arrives', u'arrogant', u'art', u'arthur', u'artificial', u'artist', u'artistic', u'artists', u'arts', u'ashamed', u'ashley', u'asian', u'aside', u'ask', u'asked', u'asking', u'asks', u'asleep', u'aspect', u'aspects', u'ass', u'assassin', u'assault', u'assigned', u'assistant', u'associated', u'assume', u'assumed', u'astaire', u'astonishing', u'atlantis', u'atmosphere', u'atmospheric', u'atrocious', u'attached', u'attack', u'attacked', u'attacks', u'attempt', u'attempted', u'attempting', u'attempts', u'attend', u'attention', u'attitude', u'attitudes', u'attorney', u'attracted', u'attraction', u'attractive', u'audience', u'audiences', u'audio', u'aunt', u'austen', u'austin', u'australia', u'australian', u'authentic', u'author', u'authority', u'available', u'average', u'avoid', u'avoided', u'awake', u'award', u'awards', u'aware', u'away', u'awe', u'awesome', u'awful', u'awfully', u'awkward', u'babe', u'baby', u'bacall', u'back', u'backdrop', u'background', u'backgrounds', u'bad', u'badly', u'bag', u'baker', u'bakshi', u'balance', u'baldwin', u'ball', u'ballet', u'balls', u'band', u'bands', u'bang', u'bank', u'banned', u'bar', u'barbara', u'bare', u'barely', u'bargain', u'barry', u'barrymore', u'base', u'baseball', u'based', u'basement', u'basic', u'basically', u'basis', u'basketball', u'bat', u'bath', u'bathroom', u'batman', u'battle', u'battles', u'bay', u'bbc', u'beach', u'bear', u'bears', u'beast', u'beat', u'beaten', u'beating', u'beats', u'beatty', u'beautiful', u'beautifully', u'beauty', u'became', u'become', u'becomes', u'becoming', u'bed', u'bedroom', u'beer', u'began', u'begin', u'beginning', u'begins', u'behave', u'behavior', u'behind', u'beings', u'bela', u'belief', u'beliefs', u'believable', u'believe', u'believed', u'believes', u'believing', u'bell', u'belong', u'belongs', u'beloved', u'belushi', u'ben', u'beneath', u'benefit', u'bergman', u'berlin', u'besides', u'best', u'bet', u'bette', u'better', u'bettie', u'betty', u'beyond', u'bible', u'big', u'bigger', u'biggest', u'biko', u'bill', u'billed', u'billy', u'bin', u'biography', u'bird', u'birds', u'birth', u'birthday', u'bit', u'bite', u'bits', u'bitter', u'bizarre', u'black', u'blade', u'blah', u'blair', u'blake', u'blame', u'bland', u'blank', u'blast', u'blatant', u'bleak', u'blend', u'blew', u'blind', u'blob', u'block', u'blockbuster', u'blond', u'blonde', u'blood', u'bloody', u'blow', u'blowing', u'blown', u'blows', u'blue', u'blues', u'blunt', u'bo', u'board', u'boat', u'bob', u'bobby', u'bodies', u'body', u'bold', u'boll', u'bollywood', u'bomb', u'bond', u'bone', u'bonus', u'book', u'books', u'boom', u'boot', u'border', u'bore', u'bored', u'boredom', u'boring', u'born', u'borrowed', u'boss', u'bother', u'bothered', u'bottle', u'bottom', u'bought', u'bound', u'bourne', u'box', u'boxing', u'boy', u'boyfriend', u'boyle', u'boys', u'brad', u'brady', u'brain', u'brains', u'branagh', u'brand', u'brando', u'brave', u'brazil', u'break', u'breaking', u'breaks', u'breasts', u'breath', u'breathtaking', u'brenda', u'brian', u'bride', u'bridge', u'brief', u'briefly', u'bright', u'brilliance', u'brilliant', u'brilliantly', u'bring', u'bringing', u'brings', u'britain', u'british', u'broad', u'broadcast', u'broadway', u'broke', u'broken', u'brooklyn', u'brooks', u'brosnan', u'brother', u'brothers', u'brought', u'brown', u'bruce', u'brutal', u'brutality', u'brutally', u'buck', u'bucks', u'bud', u'buddies', u'buddy', u'budget', u'buff', u'buffalo', u'buffs', u'bug', u'bugs', u'build', u'building', u'buildings', u'builds', u'built', u'bull', u'bullet', u'bullets', u'bumbling', u'bunch', u'buried', u'burn', u'burned', u'burning', u'burns', u'burt', u'burton', u'bus', u'bush', u'business', u'businessman', u'buster', u'busy', u'butler', u'butt', u'button', u'buy', u'buying', u'cabin', u'cable', u'cage', u'cagney', u'caine', u'cake', u'caliber', u'california', u'call', u'called', u'calling', u'calls', u'calm', u'came', u'cameo', u'cameos', u'camera', u'cameras', u'cameron', u'camp', u'campbell', u'campy', u'canada', u'canadian', u'candy', u'cannibal', u'cannot', u'cant', u'capable', u'capital', u'captain', u'captivating', u'capture', u'captured', u'captures', u'capturing', u'car', u'card', u'cardboard', u'cards', u'care', u'cared', u'career', u'careers', u'careful', u'carefully', u'carell', u'cares', u'caring', u'carl', u'carla', u'carol', u'carpenter', u'carradine', u'carrey', u'carrie', u'carried', u'carries', u'carry', u'carrying', u'cars', u'carter', u'cartoon', u'cartoons', u'cary', u'case', u'cases', u'cash', u'cassidy', u'cast', u'casting', u'castle', u'cat', u'catch', u'catches', u'catching', u'catchy', u'category', u'catherine', u'catholic', u'cats', u'caught', u'cause', u'caused', u'causes', u'causing', u'cave', u'cd', u'celebrity', u'cell', u'celluloid', u'center', u'centered', u'centers', u'central', u'century', u'certain', u'certainly', u'cg', u'cgi', u'chain', u'chair', u'challenge', u'challenging', u'championship', u'chan', u'chance', u'chances', u'change', u'changed', u'changes', u'changing', u'channel', u'channels', u'chaos', u'chaplin', u'chapter', u'character', u'characterization', u'characters', u'charge', u'charisma', u'charismatic', u'charles', u'charlie', u'charlotte', u'charm', u'charming', u'chase', u'chased', u'chases', u'chasing', u'che', u'cheap', u'cheated', u'cheating', u'check', u'checked', u'checking', u'cheek', u'cheese', u'cheesy', u'chemistry', u'chess', u'chest', u'chicago', u'chick', u'chicken', u'chicks', u'chief', u'child', u'childhood', u'childish', u'children', u'chilling', u'china', u'chinese', u'choice', u'choices', u'choose', u'chooses', u'choreographed', u'choreography', u'chorus', u'chose', u'chosen', u'chris', u'christ', u'christian', u'christianity', u'christians', u'christmas', u'christopher', u'christy', u'chuck', u'church', u'cia', u'cinderella', u'cinema', u'cinematic', u'cinematographer', u'cinematography', u'circle', u'circumstances', u'cities', u'citizen', u'city', u'civil', u'civilization', u'claim', u'claimed', u'claims', u'claire', u'clark', u'class', u'classes', u'classic', u'classical', u'classics', u'claus', u'clean', u'clear', u'clearly', u'clever', u'cleverly', u'clich', u'cliche', u'cliff', u'climactic', u'climax', u'clint', u'clip', u'clips', u'clock', u'close', u'closed', u'closely', u'closer', u'closest', u'closet', u'closing', u'clothes', u'clothing', u'clown', u'club', u'clue', u'clues', u'clumsy', u'co', u'coach', u'coast', u'code', u'coffee', u'coherent', u'cold', u'cole', u'collection', u'college', u'colonel', u'color', u'colorful', u'colors', u'colour', u'columbo', u'com', u'combat', u'combination', u'combine', u'combined', u'come', u'comedian', u'comedic', u'comedies', u'comedy', u'comes', u'comfort', u'comfortable', u'comic', u'comical', u'comics', u'coming', u'command', u'comment', u'commentary', u'commented', u'comments', u'commercial', u'commercials', u'commit', u'committed', u'common', u'communist', u'community', u'companies', u'companion', u'company', u'compare', u'compared', u'comparing', u'comparison', u'compassion', u'compelled', u'compelling', u'competent', u'competition', u'complain', u'complaint', u'complete', u'completely', u'complex', u'complexity', u'complicated', u'composed', u'composer', u'computer', u'con', u'conceived', u'concept', u'concern', u'concerned', u'concerning', u'concerns', u'concert', u'conclusion', u'condition', u'conditions', u'confess', u'confidence', u'conflict', u'conflicts', u'confrontation', u'confused', u'confusing', u'confusion', u'connect', u'connected', u'connection', u'connery', u'consequences', u'conservative', u'consider', u'considerable', u'considered', u'considering', u'consistent', u'consistently', u'consists', u'conspiracy', u'constant', u'constantly', u'constructed', u'construction', u'contact', u'contain', u'contained', u'contains', u'contemporary', u'content', u'contest', u'context', u'continue', u'continued', u'continues', u'continuity', u'contract', u'contrary', u'contrast', u'contrived', u'control', u'controversial', u'conventional', u'conversation', u'conversations', u'convey', u'convince', u'convinced', u'convincing', u'convincingly', u'convoluted', u'cook', u'cool', u'cooper', u'cop', u'copies', u'cops', u'copy', u'core', u'corner', u'corny', u'corporate', u'corpse', u'correct', u'correctly', u'corrupt', u'corruption', u'cost', u'costs', u'costume', u'costumes', u'could', u'couldn', u'count', u'counter', u'countless', u'countries', u'country', u'countryside', u'couple', u'couples', u'courage', u'course', u'court', u'cousin', u'cover', u'covered', u'covers', u'cowboy', u'cox', u'crack', u'cracking', u'craft', u'crafted', u'craig', u'crap', u'crappy', u'crash', u'craven', u'crawford', u'crazed', u'crazy', u'create', u'created', u'creates', u'creating', u'creation', u'creative', u'creativity', u'creator', u'creators', u'creature', u'creatures', u'credibility', u'credible', u'credit', u'credits', u'creep', u'creepy', u'crew', u'cried', u'crime', u'crimes', u'criminal', u'criminals', u'cringe', u'crisis', u'critic', u'critical', u'criticism', u'critics', u'crocodile', u'cross', u'crowd', u'crucial', u'crude', u'cruel', u'cruise', u'crush', u'cry', u'crying', u'crystal', u'cuba', u'cube', u'cult', u'cultural', u'culture', u'cup', u'cure', u'curiosity', u'curious', u'current', u'currently', u'curse', u'curtis', u'cusack', u'cut', u'cute', u'cuts', u'cutting', u'cynical', u'da', u'dad', u'daddy', u'daily', u'dalton', u'damage', u'damme', u'damn', u'damon', u'dan', u'dana', u'dance', u'dancer', u'dancers', u'dances', u'dancing', u'danes', u'danger', u'dangerous', u'daniel', u'danny', u'dare', u'daring', u'dark', u'darker', u'darkness', u'darren', u'date', u'dated', u'dating', u'daughter', u'daughters', u'dave', u'david', u'davies', u'davis', u'dawn', u'dawson', u'day', u'days', u'de', u'dead', u'deadly', u'deaf', u'deal', u'dealing', u'deals', u'dealt', u'dean', u'dear', u'death', u'deaths', u'debut', u'decade', u'decades', u'deceased', u'decent', u'decide', u'decided', u'decides', u'decision', u'decisions', u'dedicated', u'dee', u'deep', u'deeper', u'deeply', u'defeat', u'defend', u'defense', u'defined', u'definite', u'definitely', u'definition', u'degree', u'del', u'deliberately', u'delight', u'delightful', u'deliver', u'delivered', u'delivering', u'delivers', u'delivery', u'demand', u'demands', u'demented', u'demise', u'demon', u'demons', u'deniro', u'dennis', u'dentist', u'denzel', u'department', u'depicted', u'depicting', u'depiction', u'depicts', u'depressed', u'depressing', u'depression', u'depth', u'der', u'derek', u'descent', u'describe', u'described', u'describes', u'description', u'desert', u'deserve', u'deserved', u'deserves', u'design', u'designed', u'designs', u'desire', u'desired', u'despair', u'desperate', u'desperately', u'desperation', u'despite', u'destiny', u'destroy', u'destroyed', u'destroying', u'destruction', u'detail', u'detailed', u'details', u'detective', u'determined', u'develop', u'developed', u'developing', u'development', u'develops', u'device', u'devil', u'devoid', u'devoted', u'dialog', u'dialogs', u'dialogue', u'dialogues', u'diamond', u'diana', u'diane', u'dick', u'dickens', u'didn', u'die', u'died', u'dies', u'difference', u'differences', u'different', u'difficult', u'dig', u'digital', u'dignity', u'dimension', u'dimensional', u'din', u'dinner', u'dinosaur', u'dinosaurs', u'dire', u'direct', u'directed', u'directing', u'direction', u'directions', u'directly', u'director', u'directorial', u'directors', u'directs', u'dirty', u'disagree', u'disappear', u'disappeared', u'disappoint', u'disappointed', u'disappointing', u'disappointment', u'disaster', u'disbelief', u'disc', u'discover', u'discovered', u'discovers', u'discovery', u'discuss', u'discussion', u'disease', u'disgusting', u'disjointed', u'dislike', u'disliked', u'disney', u'display', u'displayed', u'displays', u'distance', u'distant', u'distinct', u'distracting', u'distribution', u'disturbed', u'disturbing', u'divorce', u'dixon', u'doc', u'doctor', u'documentaries', u'documentary', u'doesn', u'dog', u'dogs', u'doll', u'dollar', u'dollars', u'dolls', 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u'told', u'tom', u'tomatoes', u'tommy', u'tone', u'tongue', u'tonight', u'tons', u'tony', u'took', u'tooth', u'top', u'topic', u'topless', u'torn', u'torture', u'tortured', u'total', u'totally', u'touch', u'touched', u'touches', u'touching', u'tough', u'tour', u'toward', u'towards', u'town', u'toy', u'toys', u'track', u'tracks', u'tracy', u'trade', u'trademark', u'tradition', u'traditional', u'tragedy', u'tragic', u'trail', u'trailer', u'trailers', u'train', u'trained', u'training', u'transfer', u'transformation', u'transition', u'translation', u'trap', u'trapped', u'trash', u'trashy', u'travel', u'traveling', u'travels', u'travesty', u'treasure', u'treat', u'treated', u'treatment', u'treats', u'tree', u'trees', u'trek', u'tremendous', u'trial', u'tribe', u'tribute', u'trick', u'tricks', u'tried', u'tries', u'trilogy', u'trio', u'trip', u'trite', u'triumph', u'troops', u'trouble', u'troubled', u'troubles', u'truck', u'true', u'truly', u'trust', u'truth', u'try', u'trying', u'tube', u'tune', u'tunes', u'turkey', u'turn', u'turned', u'turner', u'turning', u'turns', u'tv', u'twelve', u'twenty', u'twice', u'twilight', u'twin', u'twins', u'twist', u'twisted', u'twists', u'two', u'type', u'types', u'typical', u'typically', u'ugly', u'uk', u'ultimate', u'ultimately', u'ultra', u'un', u'unable', u'unaware', u'unbearable', u'unbelievable', u'unbelievably', u'uncle', u'uncomfortable', u'unconvincing', u'underground', u'underlying', u'underrated', u'understand', u'understandable', u'understanding', u'understated', u'understood', u'undoubtedly', u'uneven', u'unexpected', u'unexpectedly', u'unfair', u'unfolds', u'unforgettable', u'unfortunate', u'unfortunately', u'unfunny', u'unhappy', u'uninspired', u'unintentional', u'unintentionally', u'uninteresting', u'union', u'unique', u'unit', u'united', u'universal', u'universe', u'university', u'unknown', u'unless', u'unlike', u'unlikely', u'unnecessary', u'unoriginal', u'unpleasant', u'unpredictable', u'unreal', u'unrealistic', u'unseen', u'unsettling', u'unusual', u'unwatchable', u'uplifting', u'upon', u'upper', u'ups', u'upset', u'urban', u'urge', u'us', u'usa', u'use', u'used', u'useful', u'useless', u'user', u'uses', u'using', u'ustinov', u'usual', u'usually', u'utter', u'utterly', u'uwe', u'vacation', u'vague', u'vaguely', u'valentine', u'valley', u'valuable', u'value', u'values', u'vampire', u'vampires', u'van', u'vance', u'variety', u'various', u'vast', u've', u'vegas', u'vehicle', u'vengeance', u'verhoeven', u'version', u'versions', u'versus', u'veteran', u'vhs', u'via', u'vice', u'vicious', u'victim', u'victims', u'victor', u'victoria', u'victory', u'video', u'videos', u'vietnam', u'view', u'viewed', u'viewer', u'viewers', u'viewing', u'viewings', u'views', u'village', u'villain', u'villains', u'vincent', u'violence', u'violent', u'virgin', u'virginia', u'virtually', u'virus', u'visible', u'vision', u'visit', u'visits', u'visual', u'visually', u'visuals', u'vivid', u'voice', u'voiced', u'voices', u'voight', u'von', u'vote', u'vs', u'vulnerable', u'wacky', u'wait', u'waited', u'waiting', u'waitress', u'wake', u'walk', u'walked', u'walken', u'walker', u'walking', u'walks', u'wall', u'wallace', u'walls', u'walsh', u'walter', u'wandering', u'wang', u'wanna', u'wannabe', u'want', u'wanted', u'wanting', u'wants', u'war', u'ward', u'warm', u'warming', u'warmth', u'warn', u'warned', u'warner', u'warning', u'warren', u'warrior', u'warriors', u'wars', u'washington', u'wasn', u'waste', u'wasted', u'wasting', u'watch', u'watchable', u'watched', u'watches', u'watching', u'water', u'waters', u'watson', u'wave', u'waves', u'way', u'wayne', u'ways', u'weak', u'weakest', u'wealth', u'wealthy', u'weapon', u'weapons', u'wear', u'wearing', u'wears', u'web', u'website', u'wedding', u'week', u'weekend', u'weeks', u'weight', u'weird', u'welcome', u'well', u'welles', u'wells', u'wendy', u'went', u'weren', u'werewolf', u'wes', u'west', u'western', u'westerns', u'wet', u'whale', u'whatever', u'whats', u'whatsoever', u'whenever', u'whereas', u'whether', u'whilst', u'white', u'whoever', u'whole', u'wholly', u'whoopi', u'whose', u'wicked', u'wide', u'widely', u'widmark', u'widow', u'wife', u'wild', u'wilderness', u'william', u'williams', u'willie', u'willing', u'willis', u'wilson', u'win', u'wind', u'window', u'winds', u'wing', u'winner', u'winning', u'wins', u'winter', u'winters', u'wisdom', u'wise', u'wish', u'wished', u'wishes', u'wishing', u'wit', u'witch', u'witches', u'within', u'without', u'witness', u'witnessed', u'witnesses', u'witty', u'wives', u'wizard', u'wolf', u'woman', u'women', u'won', u'wonder', u'wondered', u'wonderful', u'wonderfully', u'wondering', u'wonders', u'wont', u'woo', u'wood', u'wooden', u'woods', u'woody', u'word', u'words', u'wore', u'work', u'worked', u'worker', u'workers', u'working', u'works', u'world', u'worlds', u'worn', u'worried', u'worry', u'worse', u'worst', u'worth', u'worthless', u'worthwhile', u'worthy', u'would', u'wouldn', u'wound', u'wounded', u'wow', u'wrap', u'wrapped', u'wreck', u'wrestling', u'wretched', u'write', u'writer', u'writers', u'writes', u'writing', u'written', u'wrong', u'wrote', u'ww', u'wwii', u'ya', u'yard', u'yeah', u'year', u'years', u'yelling', u'yellow', u'yes', u'yesterday', u'yet', u'york', u'young', u'younger', u'youth', u'zero', u'zizek', u'zombie', u'zombies', u'zone']

In [11]:
# Sum up the counts of each vocabulary word
dist = np.sum(train_data_features, axis=0)

# For each, print the vocabulary word and the number of times it 
# appears in the training set
for tag, count in zip(vocab, dist):
    print count, tag


187 abandoned
125 abc
108 abilities
454 ability
1259 able
85 abraham
116 absence
83 absent
352 absolute
1485 absolutely
306 absurd
192 abuse
91 abusive
98 abysmal
297 academy
485 accent
203 accents
300 accept
130 acceptable
144 accepted
92 access
318 accident
200 accidentally
88 accompanied
124 accomplished
296 according
186 account
81 accuracy
284 accurate
123 accused
179 achieve
139 achieved
124 achievement
90 acid
971 across
1251 act
658 acted
6490 acting
3354 action
311 actions
83 activities
2389 actor
4486 actors
1219 actress
369 actresses
394 acts
793 actual
4237 actually
148 ad
302 adam
98 adams
453 adaptation
80 adaptations
154 adapted
810 add
439 added
166 adding
347 addition
337 adds
113 adequate
124 admire
621 admit
134 admittedly
101 adorable
510 adult
376 adults
100 advance
90 advanced
153 advantage
510 adventure
204 adventures
91 advertising
259 advice
90 advise
346 affair
93 affect
113 affected
104 afford
126 aforementioned
343 afraid
212 africa
255 african
187 afternoon
128 afterwards
1121 age
233 aged
361 agent
94 agents
249 ages
111 aging
1033 ago
572 agree
88 agreed
95 agrees
119 ah
396 ahead
106 aid
96 aids
81 aim
120 aimed
176 ain
639 air
146 aired
92 airplane
93 airport
194 aka
100 akshay
376 al
351 alan
163 alas
157 albeit
265 albert
84 album
84 alcohol
93 alcoholic
81 alec
94 alert
231 alex
121 alexander
85 alfred
199 alice
79 alicia
373 alien
199 aliens
152 alike
86 alison
463 alive
407 allen
308 allow
325 allowed
128 allowing
252 allows
3138 almost
1061 alone
1776 along
90 alongside
1381 already
185 alright
9155 also
88 alternate
2537 although
114 altman
112 altogether
3239 always
101 amanda
215 amateur
216 amateurish
183 amazed
1320 amazing
174 amazingly
80 ambiguous
126 ambitious
728 america
2228 american
365 americans
92 amitabh
783 among
160 amongst
495 amount
90 amounts
509 amusing
104 amy
88 analysis
233 ancient
223 anderson
79 andre
147 andrew
151 andrews
318 andy
230 angel
85 angela
101 angeles
161 angels
191 anger
185 angle
206 angles
336 angry
342 animal
410 animals
516 animated
826 animation
240 anime
288 ann
251 anna
254 anne
117 annie
143 annoyed
998 annoying
4325 another
361 answer
176 answers
263 anthony
480 anti
113 antics
82 antonioni
88 antwone
310 anybody
333 anymore
2630 anyone
2949 anything
1117 anyway
113 anyways
304 anywhere
623 apart
339 apartment
105 ape
113 apes
125 appalling
309 apparent
917 apparently
448 appeal
225 appealing
619 appear
451 appearance
139 appearances
371 appeared
141 appearing
841 appears
507 appreciate
196 appreciated
88 appreciation
372 approach
221 appropriate
101 april
337 area
116 areas
886 aren
90 arguably
111 argue
118 argument
152 arm
95 armed
175 arms
454 army
148 arnold
3616 around
126 arrested
87 arrival
114 arrive
93 arrived
162 arrives
99 arrogant
1293 art
373 arthur
94 artificial
331 artist
339 artistic
182 artists
310 arts
158 ashamed
89 ashley
237 asian
473 aside
648 ask
295 asked
228 asking
329 asks
213 asleep
454 aspect
398 aspects
267 ass
85 assassin
88 assault
83 assigned
148 assistant
131 associated
229 assume
82 assumed
132 astaire
79 astonishing
111 atlantis
735 atmosphere
148 atmospheric
197 atrocious
132 attached
449 attack
158 attacked
158 attacks
1050 attempt
136 attempted
163 attempting
583 attempts
83 attend
906 attention
237 attitude
90 attitudes
89 attorney
123 attracted
143 attraction
352 attractive
2199 audience
476 audiences
113 audio
173 aunt
91 austen
82 austin
126 australia
206 australian
166 authentic
275 author
101 authority
388 available
720 average
775 avoid
102 avoided
106 awake
419 award
245 awards
277 aware
2775 away
101 awe
485 awesome
1724 awful
84 awfully
248 awkward
100 babe
693 baby
90 bacall
4972 back
105 backdrop
619 background
101 backgrounds
9301 bad
662 badly
150 bag
155 baker
130 bakshi
161 balance
80 baldwin
290 ball
94 ballet
93 balls
529 band
93 bands
108 bang
265 bank
108 banned
387 bar
243 barbara
114 bare
483 barely
86 bargain
134 barry
88 barrymore
173 base
215 baseball
1430 based
143 basement
519 basic
906 basically
169 basis
88 basketball
168 bat
93 bath
114 bathroom
432 batman
614 battle
136 battles
89 bay
189 bbc
191 beach
251 bear
140 bears
187 beast
354 beat
124 beaten
138 beating
111 beats
113 beatty
2177 beautiful
436 beautifully
655 beauty
697 became
1544 become
1380 becomes
348 becoming
384 bed
107 bedroom
123 beer
318 began
678 begin
1401 beginning
795 begins
94 behave
261 behavior
1280 behind
128 beings
94 bela
188 belief
92 beliefs
711 believable
2505 believe
208 believed
228 believes
137 believing
112 bell
85 belong
142 belongs
178 beloved
87 belushi
616 ben
104 beneath
102 benefit
96 bergman
99 berlin
410 besides
6414 best
244 bet
155 bette
5737 better
163 bettie
89 betty
866 beyond
134 bible
3477 big
268 bigger
515 biggest
89 biko
633 bill
79 billed
375 billy
110 bin
82 biography
113 bird
116 birds
196 birth
137 birthday
3054 bit
114 bite
295 bits
164 bitter
499 bizarre
2033 black
95 blade
195 blah
167 blair
110 blake
290 blame
273 bland
122 blank
79 blast
104 blatant
119 bleak
113 blend
101 blew
262 blind
122 blob
113 block
187 blockbuster
97 blond
270 blonde
1185 blood
302 bloody
202 blow
108 blowing
184 blown
122 blows
431 blue
100 blues
94 blunt
156 bo
250 board
254 boat
266 bob
138 bobby
217 bodies
972 body
102 bold
143 boll
169 bollywood
235 bomb
340 bond
114 bone
97 bonus
2421 book
505 books
93 boom
95 boot
124 border
181 bore
529 bored
141 boredom
1809 boring
385 born
84 borrowed
415 boss
395 bother
187 bothered
91 bottle
421 bottom
458 bought
179 bound
190 bourne
637 box
126 boxing
1560 boy
399 boyfriend
79 boyle
618 boys
175 brad
109 brady
474 brain
109 brains
186 branagh
135 brand
158 brando
202 brave
116 brazil
611 break
231 breaking
259 breaks
128 breasts
177 breath
168 breathtaking
84 brenda
352 brian
136 bride
157 bridge
395 brief
135 briefly
273 bright
129 brilliance
1195 brilliant
248 brilliantly
869 bring
216 bringing
630 brings
149 britain
898 british
115 broad
114 broadcast
242 broadway
151 broke
278 broken
105 brooklyn
169 brooks
128 brosnan
1107 brother
557 brothers
737 brought
271 brown
393 bruce
303 brutal
81 brutality
90 brutally
175 buck
124 bucks
83 bud
109 buddies
258 buddy
1836 budget
102 buff
82 buffalo
98 buffs
119 bug
152 bugs
318 build
395 building
94 buildings
114 builds
236 built
102 bull
109 bullet
124 bullets
85 bumbling
813 bunch
126 buried
127 burn
114 burned
144 burning
183 burns
167 burt
152 burton
186 bus
136 bush
624 business
83 businessman
81 buster
162 busy
86 butler
123 butt
96 button
763 buy
184 buying
175 cabin
279 cable
296 cage
124 cagney
204 caine
98 cake
84 caliber
189 california
923 call
1433 called
176 calling
260 calls
86 calm
1673 came
259 cameo
146 cameos
1778 camera
110 cameras
123 cameron
463 camp
97 campbell
179 campy
139 canada
226 canadian
289 candy
84 cannibal
1096 cannot
202 cant
228 capable
89 capital
326 captain
119 captivating
287 capture
261 captured
216 captures
88 capturing
1224 car
150 card
131 cardboard
99 cards
1385 care
121 cared
1007 career
107 careers
90 careful
134 carefully
88 carell
228 cares
166 caring
109 carl
85 carla
138 carol
161 carpenter
94 carradine
131 carrey
117 carrie
163 carried
163 carries
326 carry
165 carrying
279 cars
151 carter
545 cartoon
205 cartoons
108 cary
1533 case
163 cases
236 cash
85 cassidy
3827 cast
621 casting
339 castle
547 cat
447 catch
106 catches
92 catching
91 catchy
211 category
142 catherine
152 catholic
110 cats
555 caught
534 cause
237 caused
166 causes
107 causing
98 cave
97 cd
102 celebrity
176 cell
107 celluloid
232 center
109 centered
91 centers
411 central
528 century
764 certain
1462 certainly
96 cg
325 cgi
122 chain
157 chair
165 challenge
88 challenging
81 championship
207 chan
1067 chance
133 chances
959 change
484 changed
386 changes
194 changing
442 channel
86 channels
105 chaos
150 chaplin
88 chapter
7022 character
123 characterization
7154 characters
168 charge
138 charisma
135 charismatic
408 charles
439 charlie
98 charlotte
407 charm
471 charming
438 chase
98 chased
143 chases
145 chasing
217 che
892 cheap
92 cheated
103 cheating
762 check
80 checked
139 checking
114 cheek
158 cheese
634 cheesy
490 chemistry
96 chess
93 chest
92 chicago
233 chick
80 chicken
89 chicks
229 chief
1320 child
356 childhood
117 childish
1510 children
169 chilling
188 china
337 chinese
528 choice
171 choices
226 choose
85 chooses
99 choreographed
115 choreography
99 chorus
202 chose
232 chosen
421 chris
183 christ
373 christian
79 christianity
92 christians
623 christmas
415 christopher
97 christy
142 chuck
406 church
123 cia
233 cinderella
1491 cinema
412 cinematic
101 cinematographer
983 cinematography
103 circle
218 circumstances
88 cities
125 citizen
1195 city
140 civil
93 civilization
222 claim
100 claimed
205 claims
173 claire
204 clark
893 class
106 classes
1828 classic
88 classical
233 classics
80 claus
243 clean
786 clear
899 clearly
533 clever
94 cleverly
840 clich
96 cliche
112 cliff
93 climactic
422 climax
110 clint
83 clip
162 clips
96 clock
1296 close
92 closed
140 closely
206 closer
94 closest
103 closet
177 closing
328 clothes
110 clothing
107 clown
434 club
224 clue
123 clues
105 clumsy
603 co
102 coach
80 coast
241 code
106 coffee
109 coherent
571 cold
132 cole
343 collection
496 college
106 colonel
402 color
144 colorful
199 colors
138 colour
206 columbo
218 com
109 combat
231 combination
94 combine
198 combined
3189 come
159 comedian
315 comedic
439 comedies
3243 comedy
2483 comes
80 comfort
110 comfortable
901 comic
171 comical
133 comics
1062 coming
99 command
653 comment
318 commentary
100 commented
779 comments
239 commercial
116 commercials
127 commit
195 committed
509 common
103 communist
290 community
80 companies
104 companion
515 company
325 compare
538 compared
96 comparing
251 comparison
81 compassion
87 compelled
385 compelling
140 competent
127 competition
115 complain
139 complaint
1035 complete
1889 completely
427 complex
90 complexity
178 complicated
111 composed
82 composer
468 computer
180 con
120 conceived
520 concept
97 concern
269 concerned
115 concerning
140 concerns
143 concert
490 conclusion
143 condition
80 conditions
81 confess
95 confidence
273 conflict
93 conflicts
79 confrontation
377 confused
368 confusing
163 confusion
117 connect
147 connected
258 connection
94 connery
123 consequences
95 conservative
513 consider
97 considerable
483 considered
545 considering
96 consistent
104 consistently
144 consists
126 conspiracy
291 constant
416 constantly
95 constructed
91 construction
151 contact
156 contain
113 contained
411 contains
211 contemporary
366 content
83 contest
260 context
307 continue
128 continued
261 continues
212 continuity
130 contract
116 contrary
224 contrast
227 contrived
510 control
152 controversial
109 conventional
188 conversation
110 conversations
171 convey
197 convince
216 convinced
538 convincing
93 convincingly
121 convoluted
159 cook
971 cool
166 cooper
620 cop
90 copies
301 cops
575 copy
259 core
153 corner
258 corny
96 corporate
127 corpse
223 correct
82 correctly
133 corrupt
100 corruption
220 cost
233 costs
223 costume
418 costumes
7921 could
1499 couldn
352 count
87 counter
136 countless
151 countries
935 country
103 countryside
1719 couple
93 couples
148 courage
2506 course
193 court
147 cousin
525 cover
212 covered
135 covers
212 cowboy
132 cox
106 crack
79 cracking
113 craft
167 crafted
122 craig
1039 crap
242 crappy
201 crash
118 craven
82 crawford
79 crazed
657 crazy
612 create
542 created
245 creates
284 creating
129 creation
361 creative
85 creativity
91 creator
127 creators
340 creature
214 creatures
153 credibility
153 credible
530 credit
674 credits
91 creep
638 creepy
569 crew
117 cried
757 crime
139 crimes
325 criminal
174 criminals
107 cringe
152 crisis
124 critic
175 critical
173 criticism
398 critics
94 crocodile
325 cross
226 crowd
86 crucial
187 crude
181 cruel
128 cruise
102 crush
399 cry
191 crying
83 crystal
109 cuba
147 cube
483 cult
184 cultural
495 culture
124 cup
101 cure
124 curiosity
262 curious
257 current
113 currently
147 curse
142 curtis
137 cusack
1035 cut
581 cute
274 cuts
211 cutting
154 cynical
80 da
491 dad
107 daddy
163 daily
97 dalton
108 damage
84 damme
356 damn
89 damon
264 dan
83 dana
744 dance
149 dancer
116 dancers
103 dances
529 dancing
116 danes
215 danger
291 dangerous
237 daniel
330 danny
138 dare
114 daring
1380 dark
117 darker
193 darkness
83 darren
435 date
267 dated
107 dating
1138 daughter
174 daughters
117 dave
1024 david
129 davies
354 davis
136 dawn
130 dawson
2746 day
1268 days
730 de
1881 dead
201 deadly
82 deaf
717 deal
267 dealing
258 deals
137 dealt
188 dean
146 dear
1908 death
205 deaths
260 debut
255 decade
177 decades
82 deceased
1157 decent
482 decide
705 decided
546 decides
240 decision
105 decisions
99 dedicated
82 dee
653 deep
177 deeper
320 deeply
107 defeat
95 defend
86 defense
87 defined
117 definite
1580 definitely
83 definition
204 degree
97 del
108 deliberately
157 delight
274 delightful
328 deliver
243 delivered
94 delivering
356 delivers
184 delivery
90 demand
113 demands
89 demented
79 demise
183 demon
192 demons
82 deniro
176 dennis
152 dentist
141 denzel
194 department
210 depicted
82 depicting
172 depiction
90 depicts
99 depressed
226 depressing
129 depression
511 depth
86 der
151 derek
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126 wondered
1656 wonderful
324 wonderfully
358 wondering
125 wonders
101 wont
80 woo
271 wood
330 wooden
400 woods
240 woody
926 word
885 words
98 wore
4371 work
635 worked
129 worker
165 workers
794 working
1278 works
3835 world
143 worlds
88 worn
117 worried
142 worry
1468 worse
2731 worst
2277 worth
126 worthless
187 worthwhile
362 worthy
12436 would
1059 wouldn
94 wound
84 wounded
431 wow
87 wrap
92 wrapped
127 wreck
103 wrestling
79 wretched
670 write
1153 writer
661 writers
94 writes
1304 writing
1616 written
1821 wrong
573 wrote
85 ww
158 wwii
103 ya
82 yard
462 yeah
2362 year
4516 years
92 yelling
106 yellow
1535 yes
116 yesterday
2753 yet
809 york
3659 young
503 younger
275 youth
384 zero
85 zizek
740 zombie
518 zombies
147 zone

In [14]:
print "Training the random forest..."

# Initialize a Random Forest classifier with 100 trees
forest = RandomForestClassifier(n_estimators = 100) 

# Fit the forest to the training set, using the bag of words as 
# features and the sentiment labels as the response variable
#
# This may take a few minutes to run
forest = forest.fit( train_data_features, train["sentiment"] )


Training the random forest...

In [18]:
# Read the test data
test = pd.read_csv("testData.tsv", header=0, delimiter="\t", \
                   quoting=3 )

# Verify that there are 25,000 rows and 2 columns
print test.shape

# Create an empty list and append the clean reviews one by one
num_reviews = len(test["review"])
clean_test_reviews = [] 

print "Cleaning and parsing the test set movie reviews...\n"
for i in xrange(0,num_reviews):
    if( (i+1) % 1000 == 0 ):
        print "Review %d of %d\n" % (i+1, num_reviews)
    clean_review = review_to_words( test["review"][i] )
    clean_test_reviews.append( clean_review )

# Get a bag of words for the test set, and convert to a numpy array
test_data_features = vectorizer.transform(clean_test_reviews)
test_data_features = test_data_features.toarray()

# Use the random forest to make sentiment label predictions
result = forest.predict(test_data_features)

# Copy the results to a pandas dataframe with an "id" column and
# a "sentiment" column
output = pd.DataFrame( data={"id":test["id"], "sentiment":result} )

# Use pandas to write the comma-separated output file
output.to_csv( "Bag_of_Words_model.csv", index=False, quoting=3 )


(25000, 2)
Cleaning and parsing the test set movie reviews...

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