

In [1]:

    
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
import time
import json
import statsmodels.api as sm
from statsmodels.formula.api import logit, glm, ols

pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
import seaborn as sns
sns.set_style("whitegrid")
sns.set_context("poster")
import math
from sklearn.svm import LinearSVC

First import our dataset & dictionary



In [2]:

    
# with open("IMDB_dftouse_dict.json", "r") as fd:
#     IMDB_dftouse_dict = json.load(fd)
# maas_IMDB = pd.DataFrame(IMDB_dftouse_dict)

with open("complete_valence.json", "r") as fp:
    complete_valence = json.load(fp)
maas_IMDB = pd.DataFrame(complete_valence)



In [3]:

    
maas_IMDB.head()









    Out[3]:






  
    
      
      close_date
      close_year
      gross
      n_scorables
      open_date
      open_year
      opening_gross
      opening_theaters
      pct_scorables
      rank
      review_title
      stars
      text
      theaters
      title
      valence_avg
      valence_sum
      year
    
  
  
    
      0
      NaN
       1990
       285761243
       144
       632448000000
       1990
       17081997
       1202
       0.701389
         1
                           My favourite Christmas movie!
        10
        it just isn't christmas if i don't watch home...
       2173
                Home Alone
       0.758622
       76.620792
       1990
    
    
      1
      NaN
       1990
       285761243
        65
       632448000000
       1990
       17081997
       1202
       0.738462
         1
                                 Very funny family movie
         8
        this is a very funny movie for kids and peopl...
       2173
                Home Alone
       1.408094
       67.588495
       1990
    
    
      10
      NaN
       2001
           55835
        82
       978739200000
       2001
           9134
          7
       0.719512
       304
       The worst book to screen adaption I have ever ...
         1
        i've read all of these books and perhaps that...
         12
       Beautiful Creatures
       0.446455
       26.340859
       2001
    
    
      100
      NaN
       1996
         2491989
        23
       822960000000
       1996
        1593929
       1292
       0.695652
       174
       Cheesy! Cameos! And learn how to be your own g...
         8
         this is one of those movies which is so bad ...
       1292
               The Stupids
       0.292260
        4.676165
       1996
    
    
      1000
      NaN
       1996
         1408575
        30
       821318400000
       1996
          33843
          2
       0.533333
       189
                    Not a good induction to Shakespeare.
       N/A
         the film would have been better if the actor...
         81
       Looking for Richard
       1.055385
       16.886165
       1996



In [4]:

    
maas_IMDB.shape









    Out[4]:





(537550, 18)



In [5]:

    
# maas_IMDB[maas_IMDB.movie_id==10030].text[3]



In [6]:

    
with open("happiness.json", "r") as fp:
    happiness = json.load(fp)



In [7]:

    
len(happiness)









    Out[7]:





10222

Now we begin the Natural Language processing

This will take the form of a few steps.

Punctuation: we will remove punctuation from reviews, so that all words can be recognized
Lemmatization: we will reduce words to lemmas
Removing stop words: we will remove common words from our vocabulary, because they are unlikely to have been used to express sentiment -- which is what we are interested in

Emulating HW5

For each review create a list of nouns, and a list of adjectives
For the nouns, perform LDA to separate them into 2 topics to see if there are 2 topics that are relevant i.e. actors vs. plot or whether it's sequel or a stand alone movie
Run the sentiment analysis on the adjectives (N.B. At this point it is still not on an external dataset)
Putting the topics and sentiment analysis together (marrying the nouns & the adjectives)
Estimate the overall mean for each movie on each topic



In [8]:

    
from pattern.en import parse
from pattern.en import pprint
from pattern.vector import stem, PORTER, LEMMA
punctuation = list('.,;:!?()[]{}`''\"@#$^&*+-|=~_')



In [9]:

    
from sklearn.feature_extraction import text 
stopwords=text.ENGLISH_STOP_WORDS

1. For each review create a list of nouns, and a list of adjectives



In [10]:

    
import re
regex1=re.compile(r"\.{2,}")
regex2=re.compile(r"\-{2,}")



In [11]:

    
def get_parts(thetext):
    thetext=re.sub(regex1, ' ', thetext)
    thetext=re.sub(regex2, ' ', thetext)
    nouns=[]
    descriptives=[]
    for i,sentence in enumerate(parse(thetext, tokenize=True, lemmata=True).split()):
        nouns.append([])
        descriptives.append([])
        for token in sentence:
            #print token
            if len(token[4]) >0:
                if token[1] in ['JJ', 'JJR', 'JJS']:
                    if token[4] in stopwords or token[4][0] in punctuation or token[4][-1] in punctuation or len(token[4])==1:
                        continue
                    descriptives[i].append(token[4])
                elif token[1] in ['NN', 'NNS']:
                    if token[4] in stopwords or token[4][0] in punctuation or token[4][-1] in punctuation or len(token[4])==1:
                        continue
                    nouns[i].append(token[4])
    out=zip(nouns, descriptives)
    nouns2=[]
    descriptives2=[]
    for n,d in out:
        if len(n)!=0 and len(d)!=0:
            nouns2.append(n)
            descriptives2.append(d)
    return nouns2, descriptives2



In [13]:

    
from random import random



In [21]:

    
%%time
# review_parts = maas_IMDB.map(lambda x: get_parts(x.text))
review_parts = []
for index, row in maas_IMDB[:5].iterrows():
    review_parts.append(get_parts(row.text))
    if random() < 0.1: 
        str(int(index)*100./537550) + "%"









    



<type 'unicode'>
<type 'unicode'>
<type 'unicode'>
<type 'unicode'>
<type 'unicode'>
CPU times: user 129 ms, sys: 26.5 ms, total: 156 ms
Wall time: 133 ms



In [14]:

    
review_parts[1]









    Out[14]:





([[u'movie', u'movie'],
  [u'movie', u'rating'],
  [u'movie', u'acting'],
  [u'job'],
  [u'<br', u'day'],
  [u'effect', u'more.<br', u'movie', u'rent']],
 [[u'great'],
  [u'star'],
  [u'superb'],
  [u'better'],
  [u'rainy'],
  [u'acting', u'special']])

2. For the nouns, perform LDA to separate them into 2 topics



In [15]:

    
ldadata = [ele[0] for ele in review_parts]



In [16]:

    
ldadata









    Out[16]:





[[[u'sure', u'movie', u'time', u'time', u'opinion', u'film', u'change'],
  [u'time', u'movie', u'ooh'],
  [u'time', u'movie', u'thinking', u'd**n', u'ship'],
  [u'time'],
  [u'acting', u'film'],
  [u'oscar', u'film'],
  [u'movie', u'film'],
  [u'film'],
  [u'eye', u'character', u'film'],
  [u'performance', u'hand'],
  [u'director', u'film', u'magnitude'],
  [u'lesson', u'movie', u'love-story', u'filmmaker', u'romance', u'movie'],
  [u'film']],
 [[u'movie', u'movie'],
  [u'movie', u'rating'],
  [u'movie', u'acting'],
  [u'job'],
  [u'<br', u'day'],
  [u'effect', u'more.<br', u'movie', u'rent']],
 [[u'picture'],
  [u'scene', u'dinner', u'table', u'scene', u'family', u'friend'],
  [u'look',
   u'face',
   u'woman',
   u'room',
   u'future',
   u'husband.<br',
   u'connection',
   u'movie',
   u'movie',
   u'stuff'],
  [u'mom', u'associate', u'husband', u'wealth'],
  [u'risk.<br', u'movie'],
  [u'story', u'flow', u'actor']],
 [[u'movie', u'miracle'],
  [u'mountain', u'terrorist'],
  [u'mountain',
   u'place',
   u'action',
   u'movie',
   u'movie',
   u'snow',
   u'ice',
   u'cold',
   u'weather',
   u'man'],
  [u'film'],
  [u'guy', u'star', u'portrait', u'serialkiller', u'medicine'],
  [u'place', u'movie'],
  [u'movie', u'\xe4\xe4li\xf6t', u'collector', u'edition', u'extra']],
 [[u'childhood', u'memory', u'movie', u'touch'],
  [u'scene', u'person', u'villain', u'movie'],
  [u'cop',
   u'clich\xe9s',
   u'script',
   u'hole',
   u'truck',
   u'twist',
   u'story',
   u'run'],
  [u'ass',
   u'cop',
   u'serial',
   u'killer',
   u'gas',
   u'chamber',
   u'minion',
   u'power',
   u'resurrection',
   u'power',
   u'person'],
  [u'mix', u'case.<br', u'trash'],
  [u'memory'],
  [u'kid', u'scene', u'person'],
  [u'kind',
   u'logic',
   u'scenario',
   u'measure',
   u'cross',
   u'action',
   u'flick',
   u'flick'],
  [u'idea', u'lead'],
  [u'best', u'problem', u'guy', u'reviewer'],
  [u'killer', u'doctor', u'day', u'work', u'brain', u'switch-off'],
  [u'beer', u'experience', u'load', u'time', u'movie', u'spin'],
  [u'flick', u'pleasure', u'fan', u'kid']],
 [[u'comedian', u'movie'],
  [u'writer', u'comedy'],
  [u'preview', u'movie'],
  [u'movie'],
  [u'kind', u'stuff', u'person'],
  [u'child', u'child']],
 [[u'premise', u'cast', u'movie'],
  [u'excitement'],
  [u'person', u'lot', u'movie', u'character', u'movie'],
  [u'interaction', u'character', u'comedy', u'drama', u'element'],
  [u'movie', u'actress', u'way', u'scenario', u'riot']],
 [[u'closing', u'stage', u'character', u'outline', u'mess'],
  [u'feature', u'ray', u'hope', u'majority', u'time'],
  [u'cast',
   u'script',
   u'caricature',
   u'character',
   u'noggin',
   u'sense',
   u'humour'],
  [u'clich\xe9',
   u'joke',
   u'couple',
   u'baby',
   u'world',
   u'film',
   u'laugh',
   u'demographic.<br',
   u'story',
   u'love',
   u'plot',
   u'line',
   u'side-story',
   u'sort',
   u'couple',
   u'characterisation',
   u'romance',
   u'bubble'],
  [u'character',
   u'figure',
   u'space',
   u'emptiness',
   u'character',
   u'interaction',
   u'time',
   u'comedy',
   u'branch',
   u'chuckle',
   u'character'],
  [u'work',
   u'persona',
   u'confine',
   u'formula.<br',
   u'business',
   u'woman',
   u'class',
   u'surrogate',
   u'husband',
   u'difference'],
  [u'odd-couple',
   u'premise',
   u'year',
   u'movie',
   u'chemistry',
   u'performer',
   u'character'],
  [u'personality',
   u'outline',
   u'performer',
   u'role',
   u'demand',
   u'chemistry',
   u'air'],
  [u'fact',
   u'movie',
   u'engaging',
   u'performance',
   u'character',
   u'talent',
   u'door-man'],
  [u'time',
   u'moment',
   u'man',
   u'remainder',
   u'thing',
   u'course',
   u'movie',
   u'script',
   u'theme',
   u'way',
   u'world',
   u'camera',
   u'director',
   u'page',
   u'screen'],
  [u'film', u'baby', u'dollar', u'business', u'stereotyping', u'thing'],
  [u'hammy', u'plastic', u'tinsel-town', u'capital', u'bore', u'sugar'],
  [u'character', u'nature', u'world'],
  [u'dialogue',
   u'set',
   u'costume',
   u'script',
   u'theme',
   u'blue',
   u'pink',
   u'shade',
   u'humanity',
   u'director',
   u'need',
   u'movie',
   u'feel',
   u'fantasy',
   u'character',
   u'world'],
  [u'end',
   u'day',
   u'comedy',
   u'gauge',
   u'success',
   u'failure',
   u'chemistry',
   u'love',
   u'frequency',
   u'laugh',
   u'department'],
  [u'course', u'film', u'value'],
  [u'audience', u'boat', u'character', u'kick', u'proceedings'],
  [u'respect',
   u'look',
   u'outside',
   u'audience',
   u'dud',
   u'mismatch',
   u'review']],
 [[u'star', u'dot', u'marker', u'something.<br', u'total', u'scene', u'movie'],
  [u'trafficking', u'stereotype', u'speed', u'soundtrack', u'soundtrack'],
  [u'<br', u'scene', u'kind', u'way'],
  [u'mixture',
   u'ape',
   u'man',
   u'skit',
   u'money',
   u'birth',
   u'parody',
   u'guy',
   u'servant',
   u'man',
   u'role',
   u'human.<br'],
  [u'girl'],
  [u'mom',
   u'half',
   u'men',
   u'movie',
   u'kind',
   u'surgery',
   u'mouth',
   u'mouth',
   u'year',
   u'scene',
   u'face',
   u'mind',
   u'question'],
  [u'surgery'],
  [u'viewer', u'surgery']],
 [[u'time'],
  [u'laugh'],
  [u'movie'],
  [u'scene', u'heartwarming', u'team'],
  [u'movie'],
  [u'ending', u'smile', u'face'],
  [u'dull.<br', u'comedy', u'year'],
  [u'comedy']],
 [[u'president', u'chain', u'health', u'food', u'store'],
  [u'slew', u'work', u'whoring', u'movie'],
  [u'coach', u'lisp'],
  [u'motion', u'minute', u'skit'],
  [u'character'],
  [u'matter', u'attempt', u'laugh']],
 [[u'person',
   u'brain',
   u're-writes."<br',
   u'bastardization',
   u'work',
   u'character'],
  [u'book', u'female', u'protagonist', u'way', u'problem'],
  [u'damsel',
   u'distress',
   u'reference',
   u'law',
   u'afterthought',
   u'action',
   u'scenes.<br',
   u'film',
   u'story']],
 [[u'theathre',
   u'ticket',
   u'hand',
   u'fantasy',
   u'movie',
   u'appetite',
   u'movie',
   u'robot-technology',
   u'aspect',
   u'effect',
   u'plot',
   u'opinion',
   u'year'],
  [u'acting', u'movie', u'movie', u'men', u'success', u'opinion']],
 [[u'feature', u'rendering', u'art', u'direction'],
  [u'product', u'placement', u'disaster', u'performance', u'plot-hole'],
  [u'mixture',
   u'production',
   u'value',
   u'dialogue',
   u'children.<br',
   u'shame',
   u'extent',
   u'director']],
 [[u'action'],
  [u'place', u'mountain', u'scenery'],
  [u'scene', u'film', u'horror'],
  [u'picture']],
 [[u'comment', u'movie', u'kind', u'science', u'fiction', u'movie-goer'],
  [u'history', u'genre', u'height', u'hand', u'author'],
  [u'critic',
   u'story',
   u'ray',
   u'gun',
   u'alien',
   u'pre-pubescent',
   u'teenager'],
  [u'today',
   u'fan',
   u'history',
   u'author',
   u'science',
   u'fiction',
   u'basement'],
  [u'thought', u'provoking', u'story', u'boundary', u'condition'],
  [u'fan'],
  [u'sake', u'eye'],
  [u'record'],
  [u'computer', u'graphic', u'action', u'sequence'],
  [u'emperor',
   u'clothe',
   u'loudest.<br',
   u'type',
   u'science',
   u'fiction',
   u'movie',
   u'goer',
   u'knowledge',
   u'aspect',
   u'genre'],
  [u'plot',
   u'hole',
   u'premise',
   u'story',
   u'line',
   u'dose',
   u'wiz',
   u'bang',
   u'action',
   u'effect'],
  [u'effect', u'thinking', u'skill'],
  [u'story', u'novel', u'humanity', u'creation'],
  [u'<br', u'film', u'plot', u'hole', u'point', u'limit', u'credulity'],
  [u'character', u'character', u'intelligence', u'hallmark', u'story'],
  [u'belief',
   u'humanity',
   u'capacity',
   u'problem',
   u'mind',
   u'fist',
   u'vision',
   u'future'],
  [u'movie'],
  [u'state', u'movie', u'book'],
  [u'work', u'title'],
  [u'question', u'body', u'work', u'title', u'piece', u'crap'],
  [u'fact', u'title', u'moviegoer'],
  [u'light'],
  [u'audience', u'dredge']],
 [[u'robot',
   u'community',
   u'rise',
   u'lot',
   u'insult',
   u'name-calling',
   u'too).<br',
   u'film'],
  [u'population', u'clich\xe9d', u're-heat', u'work'],
  [u'film',
   u'retread',
   u'plot',
   u'line',
   u'mix',
   u'uber-action',
   u'character',
   u'cynic',
   u'backbone',
   u'titanium',
   u'effect',
   u'others).<br',
   u'robot',
   u'action',
   u'movie',
   u'today',
   u'audience',
   u'plot',
   u'dialog',
   u'lot',
   u'lot',
   u'adrenaline'],
  [u'attempt'],
  [u'blockbuster', u'today', u'standard', u'b-movie', u'winner']],
 [[u'film', u'year'],
  [u'plot',
   u'aspect',
   u'copy',
   u'matrix',
   u'plenty',
   u'clich\xe9',
   u'wolf',
   u'cop',
   u'gun',
   u'worker',
   u'shy',
   u'she-scientist',
   u'cent',
   u'plot',
   u'robot'],
  [u'advertising',
   u'brand',
   u'movie',
   u'commercialization',
   u'ad',
   u'spot',
   u'sec'],
  [u'enemy', u'time', u'tv', u'program', u'book']],
 [[u'spite',
   u'future-design',
   u'touch',
   u'premise',
   u'cool',
   u'performance',
   u'movie',
   u'expectation'],
  [u'nightmare',
   u'maverick',
   u'cop',
   u'badge',
   u'hardass',
   u'lieutenant',
   u'list',
   u'end',
   u'mile'],
  [u'movie', u'disaster'],
  [u'robot', u'crowd', u'scene'],
  [u'movie', u'question', u'start', u'climax'],
  [u'sum', u'plot', u'action', u'film', u'year']],
 [[u'film', u'culmination', u'film'],
  [u'motion', u'flipping', u'character', u'dialogue', u'unnecessity'],
  [u'car',
   u'garage',
   u'skin',
   u'spray',
   u'tool',
   u'combination',
   u'function',
   u'card',
   u'swipe',
   u'coffee',
   u'shop.<br',
   u'respect',
   u'woman'],
  [u'character', u'doctor', u'film'],
  [u'look', u'admiration', u'ground)<br', u'detective'],
  [u'feel', u'sorry', u'person', u'thought'],
  [u'way', u'story', u'point', u'b-c-d-etc', u'time', u'row'],
  [u'scene?)<br', u'movie', u'tool', u'rescue', u'scene'],
  [u'bike',
   u'air',
   u'doctor',
   u'distance',
   u'matter',
   u'second',
   u'foot.<br',
   u'spectacle'],
  [u'shred', u'realism', u'meaning.<br', u'film', u'time'],
  [u'<br',
   u'swing',
   u'pendulum',
   u'technique',
   u'story',
   u'film',
   u'techniques.<br',
   u'film']],
 [[u'movie'],
  [u'rival', u'number', u'moment'],
  [u'couple', u'line', u'film', u'crap'],
  [u'weekend', u'resort'],
  [u'sideline']],
 [[u'wife', u'it.<br', u'spades.<br', u'character', u'film'],
  [u'sure', u'couple', u'joke', u'character', u'space.<br'],
  [u'past', u'lady'],
  [u'flick'],
  [u'wife', u'thing'],
  [u'character.<br', u'night', u'laugh', u'value'],
  [u'comedy', u'film']],
 [[u'movie'], [u'storyline'], [u'joke'], [u'movie']],
 [[u'role']],
 [[u'film', u'dissapointment'],
  [u'talent',
   u'director',
   u'lead',
   u'story',
   u'conclusion',
   u'dire',
   u'character'],
  [u'role',
   u'fidelity',
   u'humour',
   u'hyper-active',
   u'alter-ego',
   u'project.<br',
   u'comedy',
   u'romance',
   u'character',
   u'chemistry',
   u'comedy',
   u'moment',
   u'humour',
   u'role',
   u'relationship',
   u'scriptwriter',
   u'originality'],
  [u'lover',
   u'turn',
   u'parody',
   u'type',
   u'director',
   u'screen',
   u'time',
   u'groin',
   u'dog'],
  [u'genius.<br',
   u'fan',
   u'majority',
   u'work',
   u'rest',
   u'cast',
   u'film',
   u'plenty',
   u'idea',
   u'time',
   u'genre',
   u'clich',
   u'depth',
   u'gross-out',
   u'teen-movie',
   u'humour',
   u'times.<br']],
 [[u'movie',
   u'set',
   u'action',
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   u'sarcasm']],
 [[u'video',
   u'tape',
   u'year',
   u'watch',
   u'year.<br',
   u'wit',
   u'movie',
   u'success'],
  [u'formula'],
  [u'ratings.<br', u'comedy'],
  [u'fun', u'flick']],
 [[u'actor', u'movie', u'way'],
  [u'detective', u'mob', u'boss', u'city'],
  [u'time', u'boy', u'man', u'city', u'singer', u'girlfriend', u'eye'],
  [u'face'],
  [u'villian', u'book', u'collection.<br', u'movie'],
  [u'movie', u'age', u'fun', u'movie', u'family']],
 [[u'role', u'sexpot', u'brain'],
  [u'line', u'catch', u'phrase'],
  [u'movie', u'plot', u'script', u'surprise', u'time'],
  [u'phrase',
   u'term',
   u'head',
   u'obstacle',
   u'sheer',
   u'accuracy',
   u'realism',
   u'movie',
   u'character']],
 [[u'movie', u'cast', u'character', u'script', u'synergy'],
  [u'character', u'truth', u'person', u'team', u'goal', u'morality'],
  [u'situation', u'movie', u'opening', u'premise'],
  [u'tension', u'humor'],
  [u'movie', u'movie', u'term', u'entertaining'],
  [u'music',
   u'copy',
   u'music',
   u'post',
   u'war',
   u'navy',
   u'movie',
   u'humor',
   u'point',
   u'greatness',
   u'eye',
   u'beholder'],
  [u'scene', u'credit', u'music', u'video', u'screen', u'feature'],
  [u'scene', u'cut', u'casting', u'acting', u'movie', u'pretension'],
  [u'guy', u'gal.<br', u'day', u'thought', u'film'],
  [u'fortune'],
  [u'wife', u'night', u'screen'],
  [u'movie', u'level', u'budget', u'film', u'winner'],
  [u'home',
   u'video',
   u'movie',
   u'vault',
   u'shame',
   u'shot',
   u'color',
   u'year',
   u'release'],
  [u'copy'],
  [u'eye',
   u'candy',
   u'film',
   u'credit',
   u'sequence',
   u'screen',
   u'music',
   u'video',
   u'feature']],
 [[u'woman',
   u'enchanted',
   u'castle',
   u'coast',
   u'novel',
   u'film',
   u'virtue',
   u'script',
   u'ensemble'],
  [u'element',
   u'movie',
   u'feeling',
   u'appreciation',
   u'story',
   u'resolution.<br',
   u'actress'],
  [u'wife', u'longing', u'wisteria', u'tranquillity'],
  [u'housewife', u'responsibility', u'hostess', u'castle'],
  [u'noblewoman', u'throng', u'male', u'admirer'],
  [u'scene', u'lady', u'memory', u'past.<br', u'man', u'story'],
  [u'husband', u'role', u'researcher', u'personality', u'writer'],
  [u'spouse',
   u'man',
   u'attempt',
   u'bath',
   u'bathtub',
   u'assistance',
   u'castle',
   u'servant'],
  [u'decade',
   u'actor',
   u'lady',
   u'landlord.<br',
   u'maven',
   u'proprietress',
   u'housewife']],
 [[u'kind', u'movie', u'poster', u'trailer'],
  [u'kind',
   u'movie',
   u'waitress',
   u'cross',
   u'country',
   u'adventure',
   u'crook',
   u'fact',
   u'film',
   u'tale',
   u'person',
   u'dream'],
  [u'unpredictability'],
  [u'violence', u'dream', u'reality', u'drama', u'comedy']],
 [[u'summer', u'retread', u'disappointment', u'breath', u'air'],
  [u'picture', u'man', u'woman', u'sex', u'film', u'brillant', u'company'],
  [u'performance',
   u'career',
   u'waitress',
   u'death',
   u'husband',
   u'mainstay',
   u'fantasy',
   u'world'],
  [u'trick', u'film', u'character'],
  [u'love', u'image', u'soap', u'opera'],
  [u'film', u'life', u'humor', u'love', u'violence']],
 [[u'movie'],
  [u'moment'],
  [u'rest', u'cast', u'character'],
  [u'story', u'twist'],
  [u'formula', u'movie']],
 [[u'film', u'trip', u'town'],
  [u'soap', u'groupie'],
  [u'character', u'forgiveness', u'goof'],
  [u'fantasy', u'fact', u'speak', u'devil', u'role'],
  [u'wish', u'wish', u'chair'],
  [u'friendship'],
  [u'cold', u'winter', u'day']],
 [[u'writing', u'acting', u'plot-twister', u'film', u'year'],
  [u'casting', u'direction', u'film', u'performance', u'soul'],
  [u'time'],
  [u'tongue', u'cheek', u'performance'],
  [u'spoof', u'soap', u'opera', u'movie']],
 [[u'question',
   u'film',
   u'people?<br',
   u'answer',
   u'film',
   u'whiney',
   u'note',
   u'voice',
   u'voice'],
  [u'fact', u'reality', u'time', u'film', u'task.<br'],
  [u'sublimate',
   u'energy',
   u'surface',
   u'most.<br',
   u'allusion',
   u'time',
   u'film'],
  [u'point',
   u'song',
   u'sera',
   u'sera',
   u'soundtrack',
   u'thing',
   u'film',
   u'performance',
   u'cast.<br',
   u'predictable.<br',
   u'reason',
   u'film',
   u'master',
   u'voice']],
 [[u'goof', u'error<br', u'room', u'suicide', u'gun', u'silencer'],
  [u'second', u'bang', u'gun']],
 [[u'book',
   u'movie',
   u'villain',
   u'life',
   u'screen',
   u'smile',
   u'line',
   u'dialog'],
  [u'manner'],
  [u'homage',
   u'gangster',
   u'picture',
   u'time',
   u'meant',
   u'kid',
   u'archetype',
   u'color',
   u'criminal',
   u'tommy',
   u'gun',
   u'personality'],
  [u'film',
   u'continuation',
   u'sight',
   u'gag',
   u'seriously.<br',
   u'time',
   u'movie',
   u'depiction',
   u'reality',
   u'comic-book',
   u'outline',
   u'old-school'],
  [u'story',
   u'cop',
   u'crook',
   u'cop',
   u'detective',
   u'bust',
   u'boy',
   u'performance',
   u'date',
   u'sense',
   u'sense',
   u'style',
   u'luck'],
  [u'kid', u'spunk', u'kid'],
  [u'nightclub',
   u'dame',
   u'kind',
   u'performance',
   u'suit',
   u'role',
   u'fine',
   u'feeling'],
  [u'dealing',
   u'figure',
   u'voice.<br',
   u'impersonation',
   u'film',
   u'splash',
   u'effect',
   u'explosion',
   u'comedy',
   u'action',
   u'syrup',
   u'stack',
   u'pancake'],
  [u'world',
   u'shot',
   u'thrust',
   u'plot',
   u'line',
   u'editing',
   u'montage',
   u'camera',
   u'angle',
   u'panel',
   u'comic'],
  [u'sequence', u'story', u'music', u'punch', u'gun-shot', u'way'],
  [u'heap-load',
   u'dialog',
   u'script',
   u'favorite',
   u'enemy',
   u'enemy',
   u'enemy',
   u'reference',
   u'figure',
   u'quotes).<br',
   u'time',
   u'movie',
   u'theater',
   u'bit',
   u'fantasy',
   u'degree'],
  [u'cheer',
   u'kid',
   u'action',
   u'intent',
   u'cousin',
   u'comic-book',
   u'movie',
   u'pg-13',
   u'fare',
   u'adult',
   u'throw-back',
   u'panache',
   u'feeling'],
  [u'ham', u'word']],
 [[u'nurse',
   u'september',
   u'film',
   u'festival',
   u'film',
   u'sea',
   u'vlissingen'],
  [u'day', u'time', u'evening', u'movie', u'taste', u'festival'],
  [u'nurse', u'ren\xe9e', u'zellweger', u'girl', u'love', u'soap-opera-star'],
  [u'role', u'freeman', u'rock', u'\xe0nd', u'ren\xe9e', u'zellweger'],
  [u'mix', u'romance', u'violence', u'roadmovie'],
  [u'soap-opera-lover', u'nurse', u'movie', u'character'],
  [u'friend',
   u'cause',
   u'story',
   u'line',
   u'freeman',
   u'rock',
   u'scene',
   u'thriller-aspect',
   u'picture'],
  [u'end', u'nurse', u'charm']],
 [[u'combination', u'plot', u'character'],
  [u'way', u'job', u'work', u'villain'],
  [u'standout'],
  [u'head'],
  [u'sort',
   u'sparkle',
   u'performance',
   u'part.<br',
   u'road',
   u'movie',
   u'synopsis'],
  [u'road']],
 [[u'film'],
  [u'profanity', u'violence', u'innuendo', u'movie', u'child'],
  [u'waitress', u'trance', u'life', u'character', u'soap', u'opera'],
  [u'life', u'hitman', u'drug'],
  [u'situation', u'hospital', u'set', u'soap', u'opera'],
  [u'problem'],
  [u'vote', u'actress', u'oscar', u'nominee'],
  [u'performance', u'biography'],
  [u'diagnosis']],
 [[u'movie', u'crime', u'drama', u'comedy', u'way', u'movie', u'end'],
  [u'theater', u'scalping', u'scene'],
  [u'soap', u'opera', u'actor'],
  [u'ability', u'soap', u'scene', u'expression', u'voice', u'intonation'],
  [u'scene', u'sleepless', u'comedy'],
  [u'fascination', u'grandfather', u'scene', u'photograph', u'laugh'],
  [u'performance', u'moment'],
  [u'waitress', u'crude', u'husband', u'dirt'],
  [u'job', u'hospital', u'credential', u'movie'],
  [u'fan', u'movie'],
  [u'style', u'argument', u'sheriff', u'soap', u'opera'],
  [u'blood', u'violence', u'lot', u'language', u'watch', u'plenty', u'laugh']],
 [[u'spoiler',
   u'spoiler',
   u'spoiler',
   u'film',
   u'amusing.it',
   u'lot',
   u'line',
   u'film.however',
   u'problem',
   u'plot',
   u'simple.betty(renee',
   u'life',
   u'house',
   u'wife',
   u'husband',
   u'love',
   u'soap',
   u'series',
   u'character,dr'],
  [u'minute', u'film'],
  [u'film'],
  [u'reference',
   u'end',
   u'film',
   u'conclusion',
   u'place',
   u'home',
   u'conclusion',
   u'end',
   u'film',
   u'wonder',
   u'experience',
   u'answer',
   u'all.<br',
   u'film',
   u'reality',
   u'fantasy'],
  [u'scene',
   u'subject',
   u'joke',
   u'plot',
   u'line',
   u'film',
   u'way',
   u'subject',
   u'way).<br',
   u'film',
   u'soap',
   u'series',
   u'obsession',
   u'form',
   u'sympathy',
   u'obsession',
   u'one.<br',
   u'film',
   u'scene',
   u'subject',
   u'impact',
   u'characters.<br',
   u'make',
   u'before.it',
   u'shame',
   u'film',
   u'concentrate',
   u'subjects.as',
   u'lot',
   u'film,amusing']],
 [[u'comedy',
   u'year.<br',
   u'soap',
   u'opera',
   u'waitress',
   u'witness',
   u'husband',
   u'murder',
   u'hitman',
   u'member'],
  [u'heart',
   u'specialist',
   u'character',
   u'show).<br',
   u'hitman',
   u'fun',
   u'chemistry',
   u'fun',
   u'actor',
   u'persona'],
  [u'performance'],
  [u'goodness', u'warmth'],
  [u'script',
   u'sit-com',
   u'character',
   u'situation',
   u'together.<br',
   u'comedy',
   u'thing',
   u'winning',
   u'performance',
   u'script',
   u'delightful.<br']],
 [[u'way'],
  [u'ensemble',
   u'character',
   u'plot',
   u'heart',
   u'strings.<br',
   u'thing',
   u'movie',
   u'interaction'],
  [u'heart', u'fare', u'rest', u'life'],
  [u'it.<br',
   u'role',
   u'movie',
   u'touch',
   u'violence',
   u'bloodlust',
   u'male']],
 [[u'poster', u'pole'],
  [u'buzz', u'noise', u'try'],
  [u'movie', u'plot'],
  [u'role', u'woman', u'episode', u'experience'],
  [u'surprise', u'comedian'],
  [u'identity', u'crisis'],
  [u'cast'],
  [u'<br',
   u'complaint',
   u'director',
   u'splash',
   u'year',
   u'\x91in',
   u'company',
   u'convention'],
  [u'mood-creating', u'effect', u'throat', u'scene'],
  [u'character', u'end', u'romance', u'trick', u'home', u'smile', u'face'],
  [u'director',
   u'writer',
   u'audience',
   u'idiots??<br',
   u'movie',
   u'irritation'],
  [u'year', u'movie', u'rank', u'surprise']],
 [[u'story', u'guy', u'picture'],
  [u'story', u'female-hero', u'male-villain'],
  [u'person', u'face', u'face', u'inside']],
 ...]



In [17]:

    
import itertools as IT
import collections

def flatten_iter(iterable, ltypes=collections.Iterable):
    remainder = iter(iterable)
    while True:
        first = next(remainder)
        if isinstance(first, ltypes) and not isinstance(first, basestring):
            remainder = IT.chain(first, remainder)
        else:
            yield first



In [18]:

    
vocab_raw = list(flatten_iter(ldadata))
#removing duplicates
vocab_unique = []
for i in vocab_raw:
    if i not in vocab_unique:
        vocab_unique.append(i)



In [19]:

    
vocab = {}
for i in range(len(vocab_unique)):
    vocab[vocab_unique[i]] = i
    
id2word = {}
for i in range(len(vocab_unique)):
    id2word[i] = vocab_unique[i]



In [20]:

    
id2word[0], vocab.keys()[5], vocab[vocab.keys()[5]]









    Out[20]:





(u'sure', u'yellow', 5224)



In [21]:

    
from collections import defaultdict

def auxillary_function(ldadata, vocab):
    d = defaultdict(int)
    for i in ldadata: 
        index = vocab[i]
        d[index] += 1
    return d.items()



In [89]:

    
all_sentences = []
for review in ldadata: # into a review
    for sentence in review:
        this_sentence = []
        for word in sentence: 
            if word not in this_sentence: 
                this_sentence.append(word)
        all_sentences.append(this_sentence)

corpus = []
for sentence in all_sentences: 
    corpus.append(auxillary_function(sentence, vocab))



In [90]:

    
corpus









    Out[90]:





[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1)],
 [(1, 1), (2, 1), (6, 1)],
 [(8, 1), (1, 1), (2, 1), (9, 1), (7, 1)],
 [(2, 1)],
 [(10, 1), (4, 1)],
 [(11, 1), (4, 1)],
 [(1, 1), (4, 1)],
 [(4, 1)],
 [(4, 1), (12, 1), (13, 1)],
 [(14, 1), (15, 1)],
 [(16, 1), (17, 1), (4, 1)],
 [(1, 1), (18, 1), (19, 1), (20, 1), (21, 1)],
 [(4, 1)],
 [(1, 1)],
 [(1, 1), (22, 1)],
 [(1, 1), (10, 1)],
 [(23, 1)],
 [(24, 1), (25, 1)],
 [(1, 1), (26, 1), (27, 1), (28, 1)],
 [(29, 1)],
 [(32, 1), (33, 1), (34, 1), (30, 1), (31, 1)],
 [(1, 1),
  (35, 1),
  (36, 1),
  (37, 1),
  (38, 1),
  (39, 1),
  (40, 1),
  (41, 1),
  (42, 1)],
 [(43, 1), (44, 1), (45, 1), (46, 1)],
 [(1, 1), (47, 1)],
 [(48, 1), (49, 1), (50, 1)],
 [(1, 1), (51, 1)],
 [(52, 1), (53, 1)],
 [(1, 1),
  (52, 1),
  (54, 1),
  (55, 1),
  (56, 1),
  (57, 1),
  (58, 1),
  (59, 1),
  (60, 1)],
 [(4, 1)],
 [(64, 1), (65, 1), (61, 1), (62, 1), (63, 1)],
 [(1, 1), (54, 1)],
 [(1, 1), (66, 1), (67, 1), (68, 1), (69, 1)],
 [(72, 1), (1, 1), (70, 1), (71, 1)],
 [(73, 1), (74, 1), (30, 1), (1, 1)],
 [(48, 1), (75, 1), (76, 1), (77, 1), (78, 1), (79, 1), (80, 1), (81, 1)],
 [(73, 1),
  (75, 1),
  (82, 1),
  (83, 1),
  (84, 1),
  (85, 1),
  (86, 1),
  (87, 1),
  (88, 1),
  (89, 1)],
 [(90, 1), (91, 1), (92, 1)],
 [(71, 1)],
 [(73, 1), (93, 1), (30, 1)],
 [(96, 1), (97, 1), (98, 1), (99, 1), (55, 1), (94, 1), (95, 1)],
 [(100, 1), (101, 1)],
 [(104, 1), (61, 1), (102, 1), (103, 1)],
 [(105, 1), (106, 1), (107, 1), (108, 1), (84, 1), (25, 1)],
 [(1, 1), (2, 1), (109, 1), (110, 1), (111, 1), (112, 1)],
 [(113, 1), (114, 1), (99, 1), (93, 1)],
 [(1, 1), (115, 1)],
 [(116, 1), (117, 1)],
 [(1, 1), (118, 1)],
 [(1, 1)],
 [(73, 1), (42, 1), (94, 1)],
 [(119, 1)],
 [(120, 1), (121, 1), (1, 1)],
 [(122, 1)],
 [(73, 1), (123, 1), (13, 1), (1, 1)],
 [(125, 1), (124, 1), (13, 1), (126, 1), (117, 1)],
 [(128, 1), (1, 1), (129, 1), (96, 1), (127, 1)],
 [(130, 1), (131, 1), (132, 1), (13, 1), (133, 1)],
 [(136, 1), (137, 1), (2, 1), (134, 1), (135, 1)],
 [(138, 1), (139, 1), (140, 1), (77, 1), (13, 1), (141, 1), (121, 1)],
 [(48, 1),
  (4, 1),
  (142, 1),
  (143, 1),
  (144, 1),
  (145, 1),
  (146, 1),
  (147, 1),
  (148, 1),
  (149, 1),
  (150, 1),
  (151, 1),
  (152, 1),
  (153, 1),
  (154, 1),
  (155, 1),
  (21, 1)],
 [(160, 1),
  (2, 1),
  (156, 1),
  (13, 1),
  (117, 1),
  (124, 1),
  (157, 1),
  (158, 1),
  (159, 1)],
 [(161, 1),
  (162, 1),
  (163, 1),
  (164, 1),
  (37, 1),
  (166, 1),
  (167, 1),
  (106, 1),
  (45, 1),
  (165, 1)],
 [(1, 1), (168, 1), (169, 1), (170, 1), (171, 1), (13, 1), (120, 1)],
 [(132, 1), (170, 1), (171, 1), (172, 1), (173, 1), (174, 1), (175, 1)],
 [(1, 1), (13, 1), (14, 1), (176, 1), (177, 1), (178, 1), (179, 1)],
 [(128, 1),
  (1, 1),
  (2, 1),
  (77, 1),
  (16, 1),
  (146, 1),
  (180, 1),
  (181, 1),
  (182, 1),
  (183, 1),
  (184, 1),
  (185, 1),
  (186, 1),
  (187, 1),
  (60, 1)],
 [(164, 1), (145, 1), (182, 1), (4, 1), (188, 1), (189, 1)],
 [(192, 1), (193, 1), (194, 1), (195, 1), (190, 1), (191, 1)],
 [(146, 1), (196, 1), (13, 1)],
 [(1, 1),
  (197, 1),
  (198, 1),
  (199, 1),
  (200, 1),
  (201, 1),
  (202, 1),
  (203, 1),
  (204, 1),
  (77, 1),
  (206, 1),
  (205, 1),
  (16, 1),
  (146, 1),
  (184, 1),
  (13, 1)],
 [(147, 1),
  (170, 1),
  (207, 1),
  (208, 1),
  (209, 1),
  (210, 1),
  (211, 1),
  (212, 1),
  (117, 1),
  (25, 1),
  (149, 1)],
 [(4, 1), (213, 1), (183, 1)],
 [(216, 1), (217, 1), (13, 1), (214, 1), (215, 1)],
 [(35, 1), (214, 1), (218, 1), (219, 1), (220, 1), (221, 1), (222, 1)],
 [(224, 1), (225, 1), (226, 1), (1, 1), (62, 1), (30, 1), (223, 1)],
 [(227, 1), (228, 1), (229, 1), (230, 1)],
 [(24, 1), (128, 1), (94, 1), (30, 1)],
 [(231, 1),
  (232, 1),
  (233, 1),
  (234, 1),
  (235, 1),
  (236, 1),
  (237, 1),
  (238, 1),
  (173, 1),
  (60, 1),
  (61, 1)],
 [(239, 1)],
 [(1, 1),
  (36, 1),
  (169, 1),
  (43, 1),
  (240, 1),
  (241, 1),
  (242, 1),
  (243, 1),
  (244, 1),
  (30, 1),
  (94, 1),
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 [(4, 1), (630, 1), (631, 1)],
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 [(632, 1), (1, 1)],
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 [(1, 1), (538, 1), (633, 1)],
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 [(330, 1), (635, 1), (4, 1), (634, 1)],
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 [(677, 1), (678, 1), (679, 1), (563, 1), (667, 1), (93, 1)],
 [(680, 1), (237, 1), (93, 1), (589, 1)],
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 [(688, 1), (298, 1), (4, 1), (93, 1), (687, 1)],
 [(689, 1), (107, 1), (567, 1)],
 [(116, 1), (362, 1), (4, 1)],
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 [(1, 1), (2, 1), (616, 1), (55, 1), (698, 1), (699, 1), (317, 1)],
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 [(584, 1), (169, 1), (589, 1), (599, 1), (187, 1), (701, 1)],
 [(704, 1), (705, 1), (78, 1), (48, 1), (702, 1), (703, 1)],
 [(147, 1)],
 [(1, 1), (114, 1), (10, 1)],
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 [(128, 1), (1, 1), (93, 1)],
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 [(1, 1), (707, 1), (613, 1)],
 [(584, 1)],
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 [(608, 1), (88, 1), (563, 1), (708, 1), (557, 1)],
 [(26, 1), (550, 1)],
 [(306, 1), (315, 1), (26, 1)],
 [(480, 1), (1, 1), (562, 1), (672, 1), (389, 1)],
 [(1, 1), (2, 1), (709, 1), (710, 1), (73, 1), (267, 1)],
 [(1, 1), (4, 1), (711, 1), (584, 1), (712, 1), (563, 1), (632, 1), (573, 1)],
 [(50, 1), (4, 1), (93, 1)],
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 [(716, 1), (34, 1), (4, 1), (262, 1)],
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 [(1, 1), (435, 1), (719, 1)],
 [(1, 1), (13, 1), (563, 1), (435, 1), (182, 1), (30, 1)],
 [(48, 1), (10, 1)],
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 [(720, 1), (649, 1), (2, 1)],
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 [(264, 1), (1, 1), (2, 1)],
 [(16, 1), (4, 1), (725, 1)],
 [(16, 1), (20, 1), (30, 1), (726, 1)],
 [(20, 1), (4, 1)],
 [(298, 1), (13, 1), (180, 1), (727, 1), (571, 1), (30, 1)],
 [(728, 1), (116, 1), (4, 1), (729, 1)],
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 [(128, 1), (263, 1), (144, 1), (731, 1), (732, 1), (733, 1)],
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 [(13, 1), (734, 1), (447, 1)],
 [(2, 1), (499, 1), (735, 1)],
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 [(737, 1), (2, 1), (739, 1), (738, 1), (77, 1), (243, 1)],
 [(740, 1), (230, 1)],
 [(435, 1), (4, 1), (741, 1), (742, 1), (397, 1)],
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 [(750, 1), (749, 1), (694, 1), (751, 1)],
 [(752, 1), (753, 1), (53, 1), (198, 1), (271, 1)],
 [(754, 1), (755, 1), (188, 1)],
 [(694, 1), (756, 1), (757, 1), (758, 1), (60, 1), (421, 1)],
 [(757, 1), (694, 1), (759, 1)],
 [(234, 1)],
 [(760, 1), (58, 1), (300, 1)],
 [(761, 1), (29, 1), (55, 1)],
 [(141, 1), (370, 1), (4, 1), (29, 1), (55, 1)],
 [(478, 1)],
 [(4, 1)],
 [(762, 1), (74, 1)],
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 [(1160, 1), (4, 1), (234, 1), (28, 1), (1146, 1)],
 [(648, 1), (808, 1), (114, 1), (30, 1), (561, 1)],
 [(173, 1)],
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 [(721, 1), (1163, 1), (1012, 1), (262, 1), (553, 1)],
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 [(93, 1)],
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 [(29, 1)],
 [(721, 1), (4, 1)],
 [(48, 1), (240, 1), (204, 1), (29, 1)],
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 [(1, 1), (557, 1), (1489, 1), (1490, 1), (822, 1), (61, 1)],
 [(996, 1), (1130, 1), (1491, 1), (150, 1), (153, 1), (30, 1)],
 [(24, 1), (896, 1), (99, 1), (1492, 1), (55, 1)],
 [(327, 1), (112, 1), (1493, 1), (1494, 1), (216, 1), (1051, 1)],
 [(1, 1), (10, 1), (479, 1)],
 [(240, 1), (150, 1)],
 [(1, 1), (282, 1), (215, 1)],
 [(128, 1), (1, 1), (50, 1), (69, 1), (54, 1)],
 [(1130, 1), (30, 1), (366, 1), (150, 1)],
 [(709, 1), (780, 1), (180, 1), (1495, 1), (1496, 1), (383, 1)],
 [(1497, 1), (709, 1), (30, 1)],
 [(1498, 1), (4, 1), (949, 1)],
 [(2, 1), (1193, 1), (48, 1), (691, 1), (1499, 1), (830, 1)],
 [(48, 1), (499, 1), (4, 1)],
 [(1281, 1), (1338, 1), (1500, 1), (573, 1)],
 [(4, 1), (23, 1)],
 [(808, 1), (185, 1), (106, 1), (4, 1), (1501, 1)],
 [(4, 1)],
 [(601, 1), (234, 1), (4, 1), (1502, 1)],
 [(4, 1)],
 [(1504, 1), (106, 1), (4, 1), (234, 1), (1503, 1)],
 [(1505, 1), (1506, 1), (499, 1)],
 [(1507, 1), (4, 1), (743, 1), (48, 1), (50, 1), (60, 1)],
 [(602, 1), (1507, 1), (60, 1)],
 [(4, 1),
  (1509, 1),
  (1362, 1),
  (850, 1),
  (50, 1),
  (725, 1),
  (1508, 1),
  (571, 1),
  (1502, 1),
  (149, 1)],
 [(1509, 1), (1510, 1), (1511, 1)],
 [(1512, 1)],
 [(1147, 1), (1051, 1), (4, 1), (1342, 1), (1513, 1)],
 [(48, 1), (1514, 1)],
 [(234, 1), (4, 1), (178, 1)],
 [(1, 1), (1515, 1), (4, 1), (30, 1), (207, 1)],
 [(1, 1), (1516, 1)],
 [(1517, 1), (30, 1)],
 [(128, 1), (4, 1), (30, 1)],
 [(601, 1), (602, 1), (283, 1), (13, 1), (1518, 1)],
 [(4, 1), (1519, 1), (180, 1), (463, 1)],
 [(296, 1), (1134, 1), (1520, 1), (1521, 1), (1522, 1), (1523, 1), (182, 1)],
 [(131, 1), (613, 1), (103, 1), (1393, 1), (1524, 1), (1525, 1), (1526, 1)],
 [(602, 1), (557, 1), (149, 1), (1527, 1)],
 [(103, 1), (1524, 1), (1528, 1), (1529, 1), (1530, 1), (1531, 1), (884, 1)],
 [(1536, 1), (1353, 1), (1522, 1), (1532, 1), (1533, 1), (1534, 1), (1535, 1)],
 [(1537, 1), (1538, 1), (1539, 1), (282, 1), (61, 1), (543, 1)],
 [(197, 1), (1540, 1), (1541, 1), (173, 1), (243, 1), (1525, 1), (613, 1)],
 [(1352, 1), (808, 1), (283, 1), (14, 1), (1542, 1)],
 [(180, 1), (1543, 1)],
 [(1544, 1),
  (1545, 1),
  (1546, 1),
  (1547, 1),
  (77, 1),
  (754, 1),
  (440, 1),
  (350, 1)],
 [(1548, 1), (486, 1)],
 [(1552, 1),
  (198, 1),
  (1549, 1),
  (1550, 1),
  (1551, 1),
  (1520, 1),
  (1553, 1),
  (52, 1),
  (149, 1),
  (758, 1)],
 [(4, 1),
  (101, 1),
  (550, 1),
  (807, 1),
  (682, 1),
  (14, 1),
  (1554, 1),
  (822, 1)],
 [(1555, 1)],
 [(1556, 1)],
 [(73, 1), (1, 1)],
 [(4, 1), (117, 1)],
 [(1552, 1), (10, 1), (117, 1), (21, 1)],
 [(13, 1), (1557, 1)],
 [(1187, 1), (13, 1), (1206, 1), (1511, 1)],
 [(1, 1),
  (484, 1),
  (182, 1),
  (1558, 1),
  (1559, 1),
  (1560, 1),
  (1561, 1),
  (1434, 1),
  (30, 1)],
 [(1, 1),
  (1206, 1),
  (73, 1),
  (459, 1),
  (13, 1),
  (80, 1),
  (210, 1),
  (1558, 1),
  (1562, 1)],
 [(1, 1),
  (73, 1),
  (1106, 1),
  (1563, 1),
  (1564, 1),
  (1565, 1),
  (1566, 1),
  (1567, 1)],
 [(1568, 1), (1, 1), (1569, 1)],
 [(1, 1),
  (1570, 1),
  (1571, 1),
  (1569, 1),
  (1193, 1),
  (307, 1),
  (21, 1),
  (117, 1)],
 [(968, 1)],
 [(2, 1)],
 [(1572, 1), (690, 1), (52, 1), (1367, 1), (932, 1), (123, 1), (447, 1)],
 [(74, 1), (126, 1)],
 [(1, 1), (55, 1)],
 [(1, 1), (1573, 1), (342, 1), (169, 1)],
 [(1574, 1),
  (1575, 1),
  (1576, 1),
  (1577, 1),
  (1578, 1),
  (499, 1),
  (240, 1),
  (562, 1),
  (691, 1),
  (602, 1)],
 [(1, 1), (562, 1), (1579, 1), (1580, 1), (13, 1)],
 [(1, 1),
  (34, 1),
  (262, 1),
  (13, 1),
  (1582, 1),
  (1581, 1),
  (335, 1),
  (759, 1),
  (1583, 1)],
 [(240, 1), (133, 1), (13, 1), (45, 1), (1584, 1), (1585, 1), (333, 1)],
 [(1281, 1),
  (935, 1),
  (269, 1),
  (815, 1),
  (1586, 1),
  (1587, 1),
  (1588, 1),
  (602, 1)],
 [(1, 1), (602, 1), (1564, 1), (1589, 1), (1281, 1)],
 [(1, 1),
  (34, 1),
  (932, 1),
  (1409, 1),
  (73, 1),
  (1590, 1),
  (1591, 1),
  (1592, 1),
  (1593, 1),
  (602, 1),
  (1594, 1)],
 [(48, 1), (106, 1), (869, 1), (117, 1), (151, 1)],
 [(515, 1), (601, 1), (1595, 1), (1596, 1), (1597, 1), (1598, 1)],
 [(149, 1), (54, 1)],
 ...]



In [91]:

    
len(corpus)









    Out[91]:





29597



In [92]:

    
import gensim



In [93]:

    
lda2 = gensim.models.ldamodel.LdaModel(corpus, num_topics=2,id2word = id2word,update_every=1,chunksize=20000,passes=1)









    



WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy



In [94]:

    
lda2.print_topics()









    Out[94]:





[u'0.039*movie + 0.028*film + 0.014*time + 0.012*person + 0.010*story + 0.010*scene + 0.009*thing + 0.008*actor + 0.008*character + 0.008*role',
 u'0.056*movie + 0.040*film + 0.021*character + 0.013*story + 0.011*time + 0.010*way + 0.009*scene + 0.009*plot + 0.008*life + 0.008*year']



In [95]:

    
for bow in corpus[0:900:15]:
    print bow
    print lda2.get_document_topics(bow)
    print " ".join([id2word[e[0]] for e in bow])
    print "=========================================="









    



[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1)]
[(0, 0.33062558805117243), (1, 0.66937441194882763)]
sure movie time opinion film change
==========================================
[(1, 1), (10, 1)]
[(0, 0.72861190362799855), (1, 0.2713880963720014)]
movie acting
==========================================
[(1, 1), (54, 1)]
[(0, 0.40955450583762837), (1, 0.59044549416237169)]
movie place
==========================================
[(1, 1), (115, 1)]
[(0, 0.71593161433662245), (1, 0.28406838566337744)]
movie comedian
==========================================
[(160, 1), (2, 1), (156, 1), (13, 1), (117, 1), (124, 1), (157, 1), (158, 1), (159, 1)]
[(0, 0.45350407414180205), (1, 0.54649592585819795)]
chuckle time figure character comedy interaction space emptiness branch
==========================================
[(227, 1), (228, 1), (229, 1), (230, 1)]
[(0, 0.2138283123986878), (1, 0.78617168760131217)]
trafficking stereotype speed soundtrack
==========================================
[(256, 1), (252, 1), (253, 1), (254, 1), (255, 1)]
[(0, 0.12566417305590061), (1, 0.87433582694409939)]
store president chain health food
==========================================
[(292, 1), (52, 1), (54, 1)]
[(0, 0.83207331693668529), (1, 0.16792668306331465)]
scenery mountain place
==========================================
[(48, 1), (328, 1), (203, 1), (329, 1)]
[(0, 0.63856264333437895), (1, 0.36143735666562105)]
story novel humanity creation
==========================================
[(307, 1), (364, 1), (365, 1), (366, 1), (367, 1)]
[(0, 0.57674521008692181), (1, 0.42325478991307813)]
today blockbuster standard b-movie winner
==========================================
[(416, 1), (35, 1), (414, 1), (415, 1)]
[(0, 0.34287423645144532), (1, 0.65712576354855456)]
detective look admiration ground)<br
==========================================
[(99, 1)]
[(0, 0.64753095771200342), (1, 0.35246904228799664)]
flick
==========================================
[(117, 1)]
[(0, 0.69041053141286168), (1, 0.30958946858713832)]
comedy
==========================================
[(21, 1)]
[(0, 0.48337736757030791), (1, 0.51662263242969209)]
romance
==========================================
[(1, 1), (283, 1)]
[(0, 0.735057815809259), (1, 0.26494218419074106)]
movie direction
==========================================
[(1, 1), (2, 1)]
[(0, 0.39382251964091625), (1, 0.60617748035908381)]
movie time
==========================================
[(545, 1), (589, 1), (590, 1), (13, 1), (529, 1), (123, 1), (93, 1), (255, 1)]
[(0, 0.077058079496353349), (1, 0.92294192050364665)]
junk wish sky character bit lot kid food
==========================================
[(4, 1), (560, 1), (563, 1), (628, 1), (629, 1), (88, 1), (30, 1), (383, 1)]
[(0, 0.074829090550731309), (1, 0.92517090944926872)]
film shaq genie thrill terribleness.<br power scene tv
==========================================
[(1, 1), (2, 1), (646, 1), (647, 1), (648, 1), (606, 1)]
[(0, 0.17919262943213707), (1, 0.8208073705678629)]
movie time skills).<br athlete sport rap
==========================================
[(672, 1), (4, 1), (263, 1), (207, 1), (667, 1), (93, 1), (671, 1)]
[(0, 0.68856397885075604), (1, 0.31143602114924407)]
word film matter end brat kid punk
==========================================
[(4, 1), (93, 1)]
[(0, 0.2039720234975948), (1, 0.7960279765024052)]
film kid
==========================================
[(128, 1), (1, 1), (93, 1)]
[(0, 0.15012838552380939), (1, 0.84987161447619064)]
way movie kid
==========================================
[(716, 1), (34, 1), (4, 1), (262, 1)]
[(0, 0.13686839106987705), (1, 0.86313160893012297)]
seen.i friend film minute
==========================================
[(298, 1), (13, 1), (180, 1), (727, 1), (571, 1), (30, 1)]
[(0, 0.58120196775803112), (1, 0.41879803224196888)]
history character moment singing cinema scene
==========================================
[(694, 1), (756, 1), (757, 1), (758, 1), (60, 1), (421, 1)]
[(0, 0.17703709301162424), (1, 0.82296290698837571)]
climber helicopter pilot trap man rescue
==========================================
[(773, 1), (774, 1)]
[(0, 0.81611268982761243), (1, 0.18388731017238755)]
sound editing
==========================================
[(557, 1), (207, 1), (733, 1), (827, 1), (828, 1), (829, 1), (830, 1)]
[(0, 0.74535019653285406), (1, 0.25464980346714589)]
music end plague field cadre devotee genius
==========================================
[(1, 1)]
[(0, 0.34472037327493082), (1, 0.65527962672506912)]
movie
==========================================
[(73, 1), (13, 1), (856, 1), (342, 1), (184, 1), (857, 1), (123, 1)]
[(0, 0.20365519133927171), (1, 0.79634480866072832)]
person character yell piece theme jerk lot
==========================================
[(1, 1), (2, 1), (867, 1), (868, 1), (869, 1), (520, 1), (209, 1), (54, 1), (61, 1)]
[(0, 0.18110416319762951), (1, 0.81889583680237055)]
movie time sequel glut concept surprise success place guy
==========================================
[(928, 1), (929, 1), (930, 1), (14, 1), (115, 1), (927, 1)]
[(0, 0.90681652139904223), (1, 0.093183478600957811)]
movement facial human performance comedian cause
==========================================
[(143, 1)]
[(0, 0.70360414209488242), (1, 0.29639585790511763)]
joke
==========================================
[(562, 1), (963, 1), (4, 1), (461, 1)]
[(0, 0.46081500340915849), (1, 0.53918499659084163)]
level bestiality film dog
==========================================
[(73, 1), (978, 1), (979, 1), (980, 1), (981, 1), (183, 1), (798, 1)]
[(0, 0.19001006916488142), (1, 0.80998993083511861)]
person habit eating cow price course goat
==========================================
[(128, 1), (94, 1)]
[(0, 0.50750675475580109), (1, 0.49249324524419885)]
way kind
==========================================
[(128, 1), (1034, 1), (18, 1), (602, 1)]
[(0, 0.32706276171212589), (1, 0.67293723828787422)]
way schmo lesson life
==========================================
[(33, 1), (838, 1), (1057, 1)]
[(0, 0.85412631502234326), (1, 0.14587368497765668)]
family hill cliche
==========================================
[(1088, 1), (1, 1), (23, 1), (73, 1)]
[(0, 0.86915388973716523), (1, 0.13084611026283482)]
hype movie job person
==========================================
[(1120, 1), (1, 1), (1121, 1), (169, 1), (12, 1), (1076, 1), (73, 1), (1116, 1), (125, 1), (1119, 1)]
[(0, 0.33790303537219185), (1, 0.6620969646278082)]
end.<br movie feast year eye murder person force drama adventure
==========================================
[(1, 1), (13, 1)]
[(0, 0.20771860644176451), (1, 0.79228139355823546)]
movie character
==========================================
[(184, 1), (121, 1), (50, 1), (1, 1), (561, 1)]
[(0, 0.15552247879106376), (1, 0.84447752120893627)]
theme cast actor movie basketball
==========================================
[(1168, 1), (94, 1)]
[(0, 0.81369702044034364), (1, 0.18630297955965636)]
saccharine kind
==========================================
[(1192, 1), (1193, 1), (1011, 1), (150, 1), (1, 1)]
[(0, 0.39013701784568322), (1, 0.60986298215431678)]
displeasure heart humor plot movie
==========================================
[(106, 1), (573, 1)]
[(0, 0.72349678208264967), (1, 0.27650321791735027)]
work reason
==========================================
[(1225, 1)]
[(0, 0.6304912709921302), (1, 0.36950872900786974)]
appearance
==========================================
[(262, 1)]
[(0, 0.27230167513219589), (1, 0.72769832486780417)]
minute
==========================================
[(182, 1)]
[(0, 0.67832446252029843), (1, 0.32167553747970162)]
thing
==========================================
[(171, 1)]
[(0, 0.28823925808170847), (1, 0.71176074191829164)]
performer
==========================================
[(26, 1), (479, 1), (1287, 1)]
[(0, 0.63037863433184926), (1, 0.36962136566815074)]
effect quality color
==========================================
[(13, 1)]
[(0, 0.28478126922733454), (1, 0.7152187307726654)]
character
==========================================
[(123, 1), (14, 1)]
[(0, 0.67789050707661513), (1, 0.32210949292338481)]
lot performance
==========================================
[(73, 1), (55, 1), (1, 1)]
[(0, 0.40903762174020591), (1, 0.59096237825979414)]
person action movie
==========================================
[(968, 1), (4, 1)]
[(0, 0.22715596482008574), (1, 0.77284403517991418)]
theater film
==========================================
[(180, 1), (1364, 1), (1365, 1), (30, 1), (13, 1)]
[(0, 0.17303254125360015), (1, 0.82696745874639987)]
moment explosion dogfight scene character
==========================================
[(48, 1), (151, 1)]
[(0, 0.23849987533471317), (1, 0.76150012466528683)]
story line
==========================================
[(1396, 1), (1338, 1), (83, 1), (4, 1)]
[(0, 0.85113378983500243), (1, 0.14886621016499743)]
yesteryear feeling serial film
==========================================
[(99, 1), (13, 1), (631, 1)]
[(0, 0.18968879584623738), (1, 0.81031120415376268)]
flick character example
==========================================
[(1430, 1)]
[(0, 0.7172742202964566), (1, 0.2827257797035434)]
creature
==========================================
[(1448, 1), (432, 1), (1267, 1)]
[(0, 0.16825291067158121), (1, 0.8317470893284189)]
excercise technique battle
==========================================
[(362, 1), (55, 1)]
[(0, 0.69453008067450384), (1, 0.30546991932549611)]
dialog action
==========================================

It looks like topic 2 is about the external factors of a movie -- who the actors are and how production went, and topic 1 is about the intrinsic factors of a movie -- the character development and plot. Examples:

* words like "movie" "actor" "film" or "budget" "film" have higher topic 2 ratings. 
* words like "relationship" "scenery" "personality" have a higher topic 1 rating

3. Run the sentiment analysis on the adjectives



In [96]:

    
nbdata = [ele[1] for ele in review_parts]



In [97]:

    
adj_raw = list(flatten_iter(nbdata))
#removinc duplicates
adj_unique = []
for i in adj_raw:
    if i not in adj_unique:
        adj_unique.append(i)



In [98]:

    
adjvocab = {}
for i in range(len(adj_unique)):
    adjvocab[adj_unique[i]] = i



In [99]:

    
adjvocab['great']









    Out[99]:





7



In [100]:

    
len(adjvocab)









    Out[100]:





7335



In [101]:

    
import itertools
Xarray = []
for i in nbdata: # into a review
    Xarraypre = " ".join(list(itertools.chain.from_iterable(i)))
    Xarray.append(Xarraypre)



In [102]:

    
# making the response array based off of the sentiment dictionary 'happiness' there is valence_avg and valence_sum
resparray = []
for index, row in maas_IMDB.iterrows():
    if row.valence_avg > 0: # need to make it binomial because that's how the calibration/cv score works
        resparray.append(1)
    else: 
        resparray.append(0)



In [103]:

    
print  len(Xarray), len(resparray)
print Xarray[0]









    



4111 4111
good second love-story romantic second annoying impressive bad great glad half-good suprised worst wonderful talented crummy single possible mere



In [104]:

    
from sklearn.cross_validation import train_test_split
itrain, itest = train_test_split(xrange(len(Xarray)), train_size=0.7)
mask=np.ones(len(Xarray), dtype='int')
mask[itrain]=1
mask[itest]=0
mask = (mask==1)



In [105]:

    
X=np.array(Xarray)
y=np.array(resparray)



In [106]:

    
y









    Out[106]:





array([1, 1, 1, ..., 1, 0, 1])



In [107]:

    
def make_xy(X_col, y_col, vectorizer):
    X = vectorizer.fit_transform(X_col)
    y = y_col
    return X, y



In [108]:

    
"""
Function
--------
log_likelihood

Compute the log likelihood of a dataset according to 
a Naive Bayes classifier. 
The Log Likelihood is defined by

L = Sum_positive(logP(positive)) + Sum_negative(logP(negative))

Where Sum_positive indicates a sum over all positive reviews, 
and Sum_negative indicates a sum over negative reviews
    
Parameters
----------
clf : Naive Bayes classifier
x : (nexample, nfeature) array
    The input data
y : (nexample) integer array
    Whether each review is positive
"""
def log_likelihood(clf, x, y):
    prob = clf.predict_log_proba(x)
    neg = y == 0
    pos = ~neg
    return prob[neg, 0].sum() + prob[pos, 1].sum()



In [109]:

    
from sklearn.cross_validation import KFold

def cv_score(clf, x, y, score_func, nfold=5):
    """
    Uses 5-fold cross validation to estimate a score of a classifier
    
    Inputs
    ------
    clf : Classifier object
    x : Input feature vector
    y : Input class labels
    score_func : Function like log_likelihood, that takes (clf, x, y) as input,
                 and returns a score
                 
    Returns
    -------
    The average score obtained by splitting (x, y) into 5 folds of training and 
    test sets, fitting on the training set, and evaluating score_func on the test set
    
    Examples
    cv_score(clf, x, y, log_likelihood)
    """
    result = 0
    for train, test in KFold(y.size, nfold): # split data into train/test groups, 5 times
        clf.fit(x[train], y[train]) # fit
        result += score_func(clf, x[test], y[test]) # evaluate score function on held-out data
    return result / nfold # average



In [110]:

    
def calibration_plot(clf, xtest, ytest):
    prob = clf.predict_proba(xtest)[:, 1]
    outcome = ytest
    data = pd.DataFrame(dict(prob=prob, outcome=outcome))

    #group outcomes into bins of similar probability
    bins = np.linspace(0, 1, 20)
    cuts = pd.cut(prob, bins)
    binwidth = bins[1] - bins[0]
    
    #freshness ratio and number of examples in each bin
    cal = data.groupby(cuts).outcome.agg(['mean', 'count'])
    cal['pmid'] = (bins[:-1] + bins[1:]) / 2
    cal['sig'] = np.sqrt(cal.pmid * (1 - cal.pmid) / cal['count'])
        
    #the calibration plot
    ax = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
    p = plt.errorbar(cal.pmid, cal['mean'], cal['sig'])
    plt.plot(cal.pmid, cal.pmid, linestyle='--', lw=1, color='k')
    plt.ylabel("Empirical P(+)")
    
    #the distribution of P(+)
    ax = plt.subplot2grid((3, 1), (2, 0), sharex=ax)
    
    plt.bar(left=cal.pmid - binwidth / 2, height=cal['count'],
            width=.95 * (bins[1] - bins[0]),
            fc=p[0].get_color())
    
    plt.xlabel("Predicted P(+)")
    plt.ylabel("Number")



In [111]:

    
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB



In [112]:

    
#the grid of parameters to search over
alphas = [0, .1, 1, 5, 10, 50]
min_dfs = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]

#Find the best value for alpha and min_df, and the best classifier
best_alpha = None
best_min_df = None
maxscore = -np.inf

for alpha in alphas:
    for min_df in min_dfs:         
        vectorizer = CountVectorizer(vocabulary =adjvocab, min_df = min_df)       
        Xthis, Ythis = make_xy(X[mask], y[mask], vectorizer)
        clf = MultinomialNB(alpha=alpha)
        cvscore = cv_score(clf, Xthis, Ythis, log_likelihood)
        if cvscore > maxscore:
            maxscore = cvscore
            best_alpha, best_min_df = alpha, min_df



In [113]:

    
print "alpha: %f" % best_alpha
print "min_df: %f" % best_min_df









    



alpha: 0.100000
min_df: 0.000010



In [114]:

    
vectorizer = CountVectorizer(vocabulary =adjvocab, min_df=best_min_df)
Xnew, Ynew = make_xy(X,y, vectorizer)
xtrain, xtest, ytrain, ytest = train_test_split(Xnew, Ynew)
clf = MultinomialNB(alpha=best_alpha).fit(xtrain, ytrain)

training_accuracy = clf.score(xtrain, ytrain)
test_accuracy = clf.score(xtest, ytest)

print "Accuracy on training data: %0.2f" % (training_accuracy)
print "Accuracy on test data:     %0.2f" % (test_accuracy)

calibration_plot(clf, xtest, ytest)









    



Accuracy on training data: 0.97
Accuracy on test data:     0.91

4. Putting the topics and sentiment analyses together



In [115]:

    
vectorizer_alladj = CountVectorizer(min_df=best_min_df,vocabulary = adjvocab)
X_alladj, Y_alladj = make_xy(X[mask],y[mask], vectorizer_alladj)
clf_alladj = MultinomialNB(alpha=best_alpha).fit(X_alladj, Y_alladj)

logpositives = dict(zip(vectorizer_alladj.get_feature_names(),clf_alladj.feature_log_prob_[0]))
lognegatives = dict(zip(vectorizer_alladj.get_feature_names(),clf_alladj.feature_log_prob_[1]))



In [116]:

    
def calc_pplus(adjlist, lp, ln, pp,pn):
    sgivenpos = 0
    sgivenneg = 0
    for adj in adjlist:
        sgivenpos += lp[adj]
        sgivenneg += ln[adj]
    return(np.exp(sgivenpos)*pp)/(np.exp(sgivenpos)*pp + np.exp(sgivenneg)*pn)



In [117]:

    
reviews=[]
for index, row in maas_IMDB.iterrows():
    reviews.append(row.movie_id)



In [118]:

    
reviews









    Out[118]:





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In [119]:

    
len(review_parts),len(reviews)









    Out[119]:





(4111, 4111)



In [120]:

    
def choose_topic(ldamodel, bow):
    tee = lda2.get_document_topics(bow)
    if len(tee)==2:
        t1,t2=tee
        if t2[1] >= t1[1]:#get higher probability topic
            topicis=t2[0]
        else:
            topicis=t1[0]
    elif len(tee)==1:#if only one was provided its very high probability. Take it
        teetuple=tee[0]
        topicis=teetuple[0]
    return topicis



In [121]:

    
priorp = np.mean(resparray)
priorn = 1 - priorp
priorp, priorn









    Out[121]:





(0.92897105327171003, 0.071028946728289966)



In [122]:

    
counter=0
reviewdict={}
for i, rid in enumerate(reviews):
    rlist=[]
    nlist, alist = review_parts[i]
    ln=len(alist)
    localbow=corpus[counter:counter+ln]
    for bow, adj, noun in zip(localbow, alist, nlist):
#         doc=" ".join([id2word[e[0]] for e in bow])
        pplus=calc_pplus(adj, logpositives, lognegatives, priorp, priorn)
        topicis=choose_topic(lda2, bow)
        ldict={"topic": topicis, 'pplus':pplus}
        rlist.append(ldict)
    reviewdict[str(int(rid))]=rlist
    counter=counter+ln
    print i, rid, ln, counter









    



0 10027.0 13 13
1 10028.0 6 19
2 10037.0 6 25
3 11015.0 7 32
4 10544.0 13 45
5 10714.0 6 51
6 10715.0 5 56
7 10716.0 18 74
8 10717.0 8 82
9 10718.0 8 90
10 10719.0 6 96
11 10753.0 3 99
12 10754.0 2 101
13 10755.0 3 104
14 11016.0 4 108
15 10756.0 23 131
16 10757.0 5 136
17 10758.0 4 140
18 10759.0 6 146
19 10760.0 11 157
20 10775.0 5 162
21 10776.0 7 169
22 10777.0 4 173
23 10778.0 1 174
24 10779.0 5 179
25 11017.0 1 180
26 10780.0 0 180
27 10781.0 6 186
28 10782.0 2 188
29 10783.0 5 193
30 10784.0 3 196
31 10785.0 7 203
32 10786.0 6 209
33 10787.0 5 214
34 10788.0 11 225
35 10794.0 2 227
36 11018.0 1 228
37 10795.0 5 233
38 10796.0 1 234
39 10797.0 4 238
40 10798.0 13 251
41 10799.0 7 258
42 10800.0 3 261
43 10801.0 7 268
44 10802.0 8 276
45 10803.0 1 277
46 10804.0 29 306
47 11019.0 2 308
48 10805.0 4 312
49 10806.0 5 317
50 10807.0 8 325
51 10808.0 5 330
52 10809.0 3 333
53 10810.0 3 336
54 10811.0 2 338
55 10812.0 2 340
56 10826.0 6 346
57 10827.0 11 357
58 11020.0 19 376
59 10828.0 8 384
60 10829.0 8 392
61 10830.0 4 396
62 10831.0 3 399
63 10832.0 3 402
64 10833.0 4 406
65 10834.0 5 411
66 10835.0 6 417
67 10836.0 12 429
68 10837.0 6 435
69 11021.0 4 439
70 10838.0 6 445
71 10902.0 5 450
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73 10904.0 2 463
74 10905.0 3 466
75 10906.0 2 468
76 10907.0 8 476
77 10908.0 10 486
78 10909.0 6 492
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80 11022.0 2 505
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83 10913.0 12 525
84 10914.0 9 534
85 11035.0 13 547
86 11088.0 1 548
87 11089.0 4 552
88 11090.0 1 553
89 11091.0 2 555
90 11092.0 12 567
91 11023.0 5 572
92 11093.0 5 577
93 11118.0 5 582
94 11119.0 10 592
95 11120.0 10 602
96 11158.0 9 611
97 11159.0 4 615
98 11160.0 4 619
99 11161.0 28 647
100 11162.0 3 650
101 11163.0 8 658
102 11024.0 4 662
103 11164.0 5 667
104 11165.0 4 671
105 11166.0 8 679
106 11167.0 7 686
107 11168.0 5 691
108 11240.0 9 700
109 11241.0 11 711
110 11242.0 6 717
111 11243.0 21 738
112 11244.0 18 756
113 10038.0 13 769
114 11025.0 11 780
115 11245.0 12 792
116 11246.0 13 805
117 11247.0 9 814
118 11248.0 5 819
119 11249.0 14 833
120 11250.0 2 835
121 11251.0 13 848
122 11252.0 21 869
123 11253.0 8 877
124 11254.0 3 880
125 11026.0 17 897
126 11255.0 5 902
127 11256.0 9 911
128 11257.0 4 915
129 11258.0 8 923
130 11307.0 2 925
131 11308.0 6 931
132 11309.0 4 935
133 11337.0 24 959
134 11338.0 15 974
135 11393.0 10 984
136 11027.0 5 989
137 11394.0 8 997
138 11395.0 8 1005
139 11527.0 4 1009
140 11532.0 15 1024
141 11649.0 1 1025
142 11650.0 9 1034
143 11651.0 6 1040
144 11652.0 9 1049
145 11653.0 2 1051
146 11654.0 4 1055
147 11028.0 6 1061
148 11655.0 13 1074
149 11656.0 8 1082
150 11657.0 3 1085
151 11658.0 5 1090
152 11659.0 11 1101
153 11660.0 6 1107
154 11661.0 9 1116
155 11662.0 6 1122
156 11663.0 1 1123
157 11677.0 3 1126
158 11029.0 9 1135
159 11678.0 11 1146
160 11679.0 5 1151
161 11680.0 9 1160
162 11681.0 5 1165
163 11682.0 3 1168
164 11683.0 7 1175
165 11684.0 6 1181
166 11685.0 20 1201
167 11686.0 18 1219
168 11687.0 10 1229
169 11030.0 14 1243
170 11688.0 11 1254
171 11689.0 22 1276
172 11690.0 6 1282
173 11744.0 11 1293
174 11767.0 7 1300
175 11768.0 13 1313
176 11769.0 19 1332
177 11827.0 7 1339
178 11828.0 5 1344
179 11829.0 8 1352
180 11031.0 8 1360
181 11830.0 11 1371
182 11831.0 9 1380
183 11832.0 3 1383
184 11833.0 4 1387
185 11834.0 3 1390
186 11835.0 8 1398
187 11836.0 4 1402
188 11837.0 4 1406
189 11861.0 14 1420
190 11862.0 9 1429
191 11032.0 1 1430
192 11863.0 12 1442
193 11864.0 8 1450
194 11920.0 5 1455
195 11968.0 10 1465
196 11969.0 4 1469
197 11970.0 5 1474
198 11971.0 10 1484
199 12040.0 7 1491
200 12041.0 7 1498
201 12042.0 4 1502
202 11033.0 11 1513
203 12043.0 5 1518
204 12044.0 7 1525
205 12045.0 12 1537
206 12046.0 7 1544
207 12047.0 8 1552
208 12051.0 9 1561
209 12052.0 8 1569
210 12053.0 8 1577
211 12054.0 5 1582
212 12136.0 6 1588
213 11195.0 10 1598
214 12137.0 3 1601
215 12138.0 3 1604
216 12139.0 9 1613
217 12184.0 2 1615
218 12185.0 3 1618
219 12186.0 5 1623
220 12187.0 7 1630
221 12188.0 14 1644
222 12189.0 4 1648
223 12190.0 4 1652
224 10039.0 10 1662
225 11196.0 10 1672
226 12191.0 18 1690
227 12192.0 21 1711
228 12193.0 21 1732
229 12234.0 2 1734
230 12235.0 3 1737
231 12236.0 7 1744
232 12264.0 5 1749
233 12265.0 1 1750
234 12306.0 4 1754
235 12307.0 2 1756
236 11197.0 5 1761
237 12308.0 4 1765
238 12309.0 4 1769
239 12310.0 8 1777
240 12311.0 4 1781
241 12312.0 13 1794
242 12313.0 5 1799
243 12314.0 9 1808
244 12315.0 3 1811
245 12316.0 4 1815
246 12332.0 4 1819
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248 12333.0 2 1831
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250 12335.0 2 1836
251 12346.0 8 1844
252 12347.0 7 1851
253 12348.0 4 1855
254 12349.0 9 1864
255 12350.0 4 1868
256 12351.0 5 1873
257 12352.0 3 1876
258 11199.0 7 1883
259 12353.0 0 1883
260 12354.0 3 1886
261 12355.0 7 1893
262 12356.0 3 1896
263 12357.0 6 1902
264 12358.0 5 1907
265 12377.0 11 1918
266 12378.0 8 1926
267 12379.0 9 1935
268 12380.0 4 1939
269 11200.0 8 1947
270 12405.0 5 1952
271 12406.0 12 1964
272 12425.0 12 1976
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275 12428.0 3 1987
276 12429.0 3 1990
277 12430.0 11 2001
278 12431.0 15 2016
279 12432.0 4 2020
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300 12469.0 6 2150
301 12470.0 17 2167
302 11203.0 17 2184
303 12471.0 2 2186
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305 12499.0 4 2197
306 7455.0 2 2199
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309 7458.0 21 2231
310 7459.0 3 2234
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312 7461.0 18 2254
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315 7463.0 3 2265
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321 7477.0 2 2306
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324 11418.0 8 2321
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329 7484.0 1 2349
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331 7486.0 1 2357
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334 7496.0 2 2372
335 10040.0 12 2384
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531 9844.0 6 3951
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540 9976.0 5 4000
541 9977.0 10 4010
542 0.0 4 4014
543 10113.0 7 4021
544 10114.0 6 4027
545 10115.0 2 4029
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622 10.0 7 4561
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632 11382.0 4 4616
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645 11394.0 6 4749
646 11631.0 15 4764
647 11395.0 5 4769
648 11396.0 3 4772
649 11397.0 10 4782
650 11398.0 4 4786
651 11399.0 4 4790
652 11400.0 4 4794
653 11401.0 17 4811
654 11402.0 15 4826
655 11403.0 1 4827
656 11404.0 7 4834
657 11632.0 8 4842
658 11405.0 7 4849
659 11406.0 4 4853
660 11407.0 7 4860
661 11408.0 12 4872
662 11409.0 7 4879
663 11410.0 3 4882
664 11411.0 6 4888
665 11412.0 7 4895
666 11413.0 9 4904
667 11414.0 9 4913
668 10043.0 3 4916
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671 11416.0 4 4933
672 11417.0 4 4937
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680 11634.0 4 4973
681 11471.0 16 4989
682 11472.0 8 4997
683 11640.0 1 4998
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688 11645.0 6 5028
689 11646.0 9 5037
690 11647.0 2 5039
691 11635.0 19 5058
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694 11650.0 5 5073
695 11651.0 3 5076
696 11652.0 4 5080
697 11653.0 5 5085
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699 11655.0 10 5102
700 11656.0 4 5106
701 11657.0 3 5109
702 11636.0 7 5116
703 11658.0 4 5120
704 11659.0 3 5123
705 11660.0 2 5125
706 11661.0 9 5134
707 11662.0 6 5140
708 11663.0 3 5143
709 11664.0 5 5148
710 11665.0 3 5151
711 11666.0 23 5174
712 11667.0 1 5175
713 11637.0 15 5190
714 11668.0 3 5193
715 11669.0 2 5195
716 1170.0 2 5197
717 1171.0 9 5206
718 1172.0 3 5209
719 1173.0 1 5210
720 1174.0 9 5219
721 1175.0 3 5222
722 1176.0 4 5226
723 1177.0 6 5232
724 11638.0 6 5238
725 1178.0 3 5241
726 1179.0 2 5243
727 1180.0 6 5249
728 1181.0 7 5256
729 1182.0 5 5261
730 11909.0 4 5265
731 11910.0 4 5269
732 11911.0 21 5290
733 11912.0 5 5295
734 11913.0 7 5302
735 11639.0 5 5307
736 11914.0 6 5313
737 11915.0 4 5317
738 11916.0 5 5322
739 11917.0 4 5326
740 11918.0 5 5331
741 11919.0 3 5334
742 11920.0 2 5336
743 11921.0 2 5338
744 11922.0 3 5341
745 11923.0 3 5344
746 11640.0 7 5351
747 11924.0 3 5354
748 11925.0 14 5368
749 11926.0 6 5374
750 11927.0 5 5379
751 11928.0 6 5385
752 11929.0 7 5392
753 11930.0 3 5395
754 11931.0 5 5400
755 11932.0 4 5404
756 11933.0 2 5406
757 11685.0 3 5409
758 11934.0 1 5410
759 11935.0 9 5419
760 11936.0 7 5426
761 11937.0 5 5431
762 11938.0 4 5435
763 11.0 16 5451
764 12177.0 5 5456
765 12178.0 18 5474
766 12179.0 3 5477
767 12180.0 2 5479
768 11686.0 2 5481
769 12181.0 10 5491
770 12182.0 4 5495
771 12183.0 2 5497
772 12184.0 5 5502
773 12185.0 1 5503
774 12186.0 6 5509
775 12187.0 4 5513
776 12188.0 3 5516
777 12189.0 7 5523
778 12190.0 4 5527
779 10044.0 50 5577
780 11687.0 2 5579
781 12191.0 12 5591
782 12192.0 7 5598
783 12193.0 2 5600
784 12194.0 7 5607
785 12195.0 4 5611
786 12196.0 12 5623
787 12197.0 18 5641
788 1291.0 5 5646
789 1292.0 3 5649
790 12.0 6 5655
791 11688.0 4 5659
792 1327.0 5 5664
793 1328.0 7 5671
794 1329.0 15 5686
795 1330.0 6 5692
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1873 8.0 6 13405
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1880 9075.0 3 13465
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1900 9093.0 19 13639
1901 9165.0 6 13645
1902 12253.0 6 13651
1903 9166.0 5 13656
1904 9167.0 7 13663
1905 9168.0 4 13667
1906 9169.0 8 13675
1907 9170.0 6 13681
1908 9171.0 11 13692
1909 9172.0 11 13703
1910 9173.0 11 13714
1911 9174.0 6 13720
1912 9175.0 8 13728
1913 12254.0 2 13730
1914 9176.0 9 13739
1915 9177.0 13 13752
1916 9178.0 6 13758
1917 9179.0 6 13764
1918 9180.0 14 13778
1919 9181.0 7 13785
1920 9182.0 3 13788
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1923 9185.0 7 13806
1924 12255.0 6 13812
1925 9186.0 6 13818
1926 9187.0 4 13822
1927 9188.0 8 13830
1928 9398.0 4 13834
1929 9399.0 4 13838
1930 9400.0 10 13848
1931 9401.0 9 13857
1932 9445.0 4 13861
1933 9446.0 5 13866
1934 9447.0 7 13873
1935 12256.0 5 13878
1936 9448.0 4 13882
1937 9449.0 3 13885
1938 9450.0 3 13888
1939 9451.0 3 13891
1940 9452.0 5 13896
1941 9453.0 3 13899
1942 9454.0 5 13904
1943 9455.0 5 13909
1944 9456.0 11 13920
1945 9457.0 7 13927
1946 12257.0 4 13931
1947 9458.0 4 13935
1948 9459.0 7 13942
1949 9460.0 7 13949
1950 9461.0 10 13959
1951 9462.0 10 13969
1952 9463.0 6 13975
1953 9464.0 5 13980
1954 9465.0 5 13985
1955 9466.0 5 13990
1956 9467.0 5 13995
1957 12258.0 6 14001
1958 9468.0 5 14006
1959 9469.0 7 14013
1960 9470.0 29 14042
1961 9471.0 4 14046
1962 9472.0 8 14054
1963 9473.0 5 14059
1964 9551.0 2 14061
1965 9552.0 1 14062
1966 9553.0 3 14065
1967 9554.0 3 14068
1968 12364.0 6 14074
1969 9555.0 5 14079
1970 9556.0 6 14085
1971 9557.0 2 14087
1972 9558.0 4 14091
1973 9559.0 3 14094
1974 9560.0 5 14099
1975 9561.0 3 14102
1976 9562.0 3 14105
1977 9563.0 2 14107
1978 9564.0 4 14111
1979 12365.0 6 14117
1980 9565.0 4 14121
1981 9566.0 5 14126
1982 9567.0 5 14131
1983 9568.0 6 14137
1984 9569.0 5 14142
1985 9570.0 14 14156
1986 9571.0 16 14172
1987 9572.0 6 14178
1988 9573.0 5 14183
1989 9574.0 5 14188
1990 12366.0 7 14195
1991 9575.0 8 14203
1992 9576.0 7 14210
1993 9577.0 5 14215
1994 9578.0 14 14229
1995 957.0 9 14238
1996 958.0 2 14240
1997 959.0 1 14241
1998 960.0 10 14251
1999 961.0 5 14256
2000 9629.0 7 14263
2001 10292.0 4 14267
2002 12367.0 6 14273
2003 962.0 2 14275
2004 9630.0 2 14277
2005 9631.0 7 14284
2006 9632.0 4 14288
2007 9633.0 7 14295
2008 9634.0 6 14301
2009 9635.0 4 14305
2010 9636.0 6 14311
2011 9637.0 1 14312
2012 9638.0 14 14326
2013 12368.0 6 14332
2014 963.0 3 14335
2015 964.0 2 14337
2016 965.0 1 14338
2017 966.0 1 14339
2018 967.0 8 14347
2019 968.0 4 14351
2020 969.0 6 14357
2021 970.0 5 14362
2022 971.0 4 14366
2023 972.0 5 14371
2024 12369.0 15 14386
2025 973.0 12 14398
2026 974.0 5 14403
2027 975.0 5 14408
2028 976.0 9 14417
2029 977.0 3 14420
2030 978.0 4 14424
2031 979.0 3 14427
2032 980.0 6 14433
2033 981.0 9 14442
2034 9821.0 15 14457
2035 12370.0 16 14473
2036 9822.0 2 14475
2037 9823.0 14 14489
2038 9824.0 22 14511
2039 9825.0 9 14520
2040 9826.0 6 14526
2041 9827.0 3 14529
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2045 9830.0 5 14543
2046 12371.0 12 14555
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2048 9832.0 6 14565
2049 9833.0 4 14569
2050 9834.0 8 14577
2051 983.0 4 14581
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2053 9866.0 20 14606
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2058 9870.0 4 14637
2059 9871.0 4 14641
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2063 9875.0 7 14672
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2066 9.0 9 14696
2067 0.0 6 14702
2068 12373.0 7 14709
2069 10000.0 7 14716
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2075 10069.0 7 14754
2076 10070.0 6 14760
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2098 1023.0 4 14901
2099 1024.0 12 14913
2100 1025.0 13 14926
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2105 105.0 2 14959
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2110 10718.0 5 14993
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2112 10293.0 10 15016
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In [123]:

    
reviews









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In [124]:

    
#review 10715
rlist_sample = []
nlist_sample, alist_sample = review_parts[6]
ln_sample=len(nlist_sample)
localbow_sample=corpus[51:56]
for bow, adj, noun in zip(localbow_sample, alist_sample, nlist_sample):
    doc=" ".join([ id2word[e[0]] for e in bow])
    pplus=calc_pplus(adj, logpositives, lognegatives, priorp, priorn)
    topicis=choose_topic(lda2, bow)
    ldict={"topic": topicis, 'pplus':pplus}
    rlist_sample.append(ldict)
print len(rlist_sample)



In [125]:

    
rlist_sample









    Out[125]:





[{'pplus': 0.70964073864955146, 'topic': 1},
 {'pplus': 0.96615975937878162, 'topic': 0},
 {'pplus': 0.40413085249289121, 'topic': 1},
 {'pplus': 0.19880577059630264, 'topic': 1},
 {'pplus': 0.45376542283443838, 'topic': 1}]



In [126]:

    
list_of_dicts=[]
counter = 0
sentence_counter = 0
for index, row in maas_IMDB.iterrows():
    counter += 1
    revs=reviewdict[str(int(row.movie_id))]
    print row.movie_id, len(revs)
    sentence_counter += len(revs)
    for r in revs:
        r2=r.copy()
        r2['movie_id']=row.movie_name
        r2['url']=row.url
        r2['review_id']=row.movie_id
        r2['stars']=row.stars
        r2['valence_avg']=row.valence_avg
        r2['valence_sum']=row.valence_sum
        list_of_dicts.append(r2)
print counter, sentence_counter



In [128]:

    
reviewdict['10715']









    Out[128]:





[{'pplus': 0.90217176279141065, 'topic': 0},
 {'pplus': 0.65892010883869923, 'topic': 0},
 {'pplus': 0.82406544561929362, 'topic': 0}]



In [129]:

    
review_parts[0]









    Out[129]:





([[u'sure', u'movie', u'time', u'time', u'opinion', u'film', u'change'],
  [u'time', u'movie', u'ooh'],
  [u'time', u'movie', u'thinking', u'd**n', u'ship'],
  [u'time'],
  [u'acting', u'film'],
  [u'oscar', u'film'],
  [u'movie', u'film'],
  [u'film'],
  [u'eye', u'character', u'film'],
  [u'performance', u'hand'],
  [u'director', u'film', u'magnitude'],
  [u'lesson', u'movie', u'love-story', u'filmmaker', u'romance', u'movie'],
  [u'film']],
 [[u'good', u'second'],
  [u'love-story', u'romantic'],
  [u'second', u'annoying'],
  [u'impressive'],
  [u'bad', u'great'],
  [u'glad'],
  [u'half-good'],
  [u'suprised'],
  [u'worst'],
  [u'wonderful'],
  [u'talented'],
  [u'crummy', u'single'],
  [u'possible', u'mere']])



In [131]:

    
def extendit(row):
    outlist=[]
    revs=reviewdict[row.review_id]
    for r in revs:
        r2=r.copy()
        r2['business_id']=row.business_id
        r2['user_id']=row.user_id
        r2['review_id']=row.review_id
        r2['stars']=row.stars
        r2['user_avg']=row.user_avg
        outlist.append(r2)
    return outlist



In [138]:

    
completedf=pd.DataFrame(list_of_dicts)
completedf.head()









    Out[138]:






  
    
      
      movie_id
      pplus
      review_id
      stars
      topic
      url
      valence_avg
      valence_sum
    
  
  
    
      0
       Titanic
       0.833386
       10027
       7
       1
       http://www.imdb.com/title/tt0120338/usercommen...
       0.47976
       38.380826
    
    
      1
       Titanic
       0.933069
       10027
       7
       1
       http://www.imdb.com/title/tt0120338/usercommen...
       0.47976
       38.380826
    
    
      2
       Titanic
       0.863956
       10027
       7
       0
       http://www.imdb.com/title/tt0120338/usercommen...
       0.47976
       38.380826
    
    
      3
       Titanic
       0.193702
       10027
       7
       0
       http://www.imdb.com/title/tt0120338/usercommen...
       0.47976
       38.380826
    
    
      4
       Titanic
       0.831243
       10027
       7
       0
       http://www.imdb.com/title/tt0120338/usercommen...
       0.47976
       38.380826



In [139]:

    
completedf.shape









    Out[139]:





(29402, 8)



In [148]:

    
completedf[completedf.review_id == 1]









    Out[148]:






  
    
      
      movie_id
      pplus
      review_id
      stars
      topic
      url
      valence_avg
      valence_sum
    
  
  
    
      6094 
       The Guardian
       0.767554
       1
       10
       0
       http://www.imdb.com/title/tt0406816/usercommen...
       0.463927
       22.268495
    
    
      6095 
       The Guardian
       0.959387
       1
       10
       1
       http://www.imdb.com/title/tt0406816/usercommen...
       0.463927
       22.268495
    
    
      6096 
       The Guardian
       0.590485
       1
       10
       0
       http://www.imdb.com/title/tt0406816/usercommen...
       0.463927
       22.268495
    
    
      6097 
       The Guardian
       0.991269
       1
       10
       1
       http://www.imdb.com/title/tt0406816/usercommen...
       0.463927
       22.268495
    
    
      6098 
       The Guardian
       0.878913
       1
       10
       1
       http://www.imdb.com/title/tt0406816/usercommen...
       0.463927
       22.268495
    
    
      6099 
       The Guardian
       0.198263
       1
       10
       1
       http://www.imdb.com/title/tt0406816/usercommen...
       0.463927
       22.268495
    
    
      6100 
       The Guardian
       0.987382
       1
       10
       0
       http://www.imdb.com/title/tt0406816/usercommen...
       0.463927
       22.268495
    
    
      6101 
       The Guardian
       0.890644
       1
       10
       0
       http://www.imdb.com/title/tt0406816/usercommen...
       0.463927
       22.268495
    
    
      6102 
       The Guardian
       0.945111
       1
       10
       1
       http://www.imdb.com/title/tt0406816/usercommen...
       0.463927
       22.268495
    
    
      16729
       The Guardian
       0.767554
       1
        3
       0
       http://www.imdb.com/title/tt0406816/usercommen...
       0.040237
        1.689933
    
    
      16730
       The Guardian
       0.959387
       1
        3
       1
       http://www.imdb.com/title/tt0406816/usercommen...
       0.040237
        1.689933
    
    
      16731
       The Guardian
       0.590485
       1
        3
       0
       http://www.imdb.com/title/tt0406816/usercommen...
       0.040237
        1.689933
    
    
      16732
       The Guardian
       0.991269
       1
        3
       1
       http://www.imdb.com/title/tt0406816/usercommen...
       0.040237
        1.689933
    
    
      16733
       The Guardian
       0.878913
       1
        3
       1
       http://www.imdb.com/title/tt0406816/usercommen...
       0.040237
        1.689933
    
    
      16734
       The Guardian
       0.198263
       1
        3
       1
       http://www.imdb.com/title/tt0406816/usercommen...
       0.040237
        1.689933
    
    
      16735
       The Guardian
       0.987382
       1
        3
       0
       http://www.imdb.com/title/tt0406816/usercommen...
       0.040237
        1.689933
    
    
      16736
       The Guardian
       0.890644
       1
        3
       0
       http://www.imdb.com/title/tt0406816/usercommen...
       0.040237
        1.689933
    
    
      16737
       The Guardian
       0.945111
       1
        3
       1
       http://www.imdb.com/title/tt0406816/usercommen...
       0.040237
        1.689933

5. Estimate the overall mean for each movie on each topic



In [140]:

    
def get_stats(group):
    min_pplus = group.pplus.min()
    max_pplus = group.pplus.max()
    rid = group.movie_id.unique()
    valence_avg = group.valence_avg.mean()
    valence_sum = group.valence_sum.mean()
    count = group.topic.count()
    if count == 1: 
        varj = 0
    else: 
        varj = group.pplus.var(ddof=1)
    mean_pplus = group.pplus.mean()
    stars = group.stars.mean()
    return pd.DataFrame({'min': min_pplus, 'max':max_pplus ,'valence_avg': valence_avg,'valence_sum':valence_sum, 
                         'count': count,'var': varj, 'mean': mean_pplus, 'stars': stars}, index=rid)



In [149]:

    
%%time
dftouse=completedf.groupby(['review_id', 'topic']).apply(get_stats).reset_index()









    



CPU times: user 9.42 s, sys: 70.9 ms, total: 9.5 s
Wall time: 9.5 s



In [150]:

    
print dftouse.shape
dftouse = dftouse.rename(columns={'level_2': 'movie_t', '$b': 'b'})
dftouse.head()









    



(7619, 11)






    Out[150]:






  
    
      
      review_id
      topic
      level_2
      count
      max
      mean
      min
      stars
      valence_avg
      valence_sum
      var
    
  
  
    
      0
       0
       0
       The Guardian
        4
       0.959577
       0.931161
       0.902744
       6.0
       0.332231
       16.730396
       0.001077
    
    
      1
       0
       1
       The Guardian
        8
       0.869453
       0.690997
       0.379703
       6.0
       0.332231
       16.730396
       0.039329
    
    
      2
       1
       0
       The Guardian
        8
       0.987382
       0.809016
       0.590485
       6.5
       0.252082
       11.979214
       0.025129
    
    
      3
       1
       1
       The Guardian
       10
       0.991269
       0.794589
       0.198263
       6.5
       0.252082
       11.979214
       0.100270
    
    
      4
       2
       0
       The Guardian
        4
       0.907921
       0.842568
       0.777215
       5.0
       0.318188
       28.525569
       0.005695



In [137]:

    
dftouse[dftouse.review_id==10037]









    Out[137]:






  
    
      
      review_id
      topic
      level_2
      count
      max
      mean
      min
      stars
      valence_avg
      valence_sum
      var
    
  
  
    
      5767
       10037
       0
       Titanic
       4
       0.868201
       0.704629
       0.552403
       9
       0.700832
       39.246578
       0.016687
    
    
      5768
       10037
       1
       Titanic
       2
       0.879267
       0.661613
       0.443958
       9
       0.700832
       39.246578
       0.094747

Using the NLTK library



In [130]:

    
! pip install nltk









    



Requirement already satisfied (use --upgrade to upgrade): nltk in /anaconda/lib/python2.7/site-packages



In [131]:

    
import nltk
from nltk.corpus import sentiwordnet as swn









    



---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-131-5d8c84d1c160> in <module>()
      1 import nltk
----> 2 from nltk.corpus import sentiwordnet as swn

ImportError: cannot import name sentiwordnet



In [30]:

    
happy = swn.senti_synset('happy.n.03')
>>> print(happy)









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-30-f3487f8e9748> in <module>()
----> 1 happy = swn.senti_synset('happy.n.03')
      2 print(happy)

NameError: name 'swn' is not defined



In [25]:

    
type(wn)









    Out[25]:





nltk.corpus.util.LazyCorpusLoader



In [ ]:

	close_date	close_year	gross	n_scorables	open_date	open_year	opening_gross	opening_theaters	pct_scorables	rank	review_title	stars	text	theaters	title	valence_avg	valence_sum	year
0	NaN	1990	285761243	144	632448000000	1990	17081997	1202	0.701389	1	My favourite Christmas movie!	10	it just isn't christmas if i don't watch home...	2173	Home Alone	0.758622	76.620792	1990
1	NaN	1990	285761243	65	632448000000	1990	17081997	1202	0.738462	1	Very funny family movie	8	this is a very funny movie for kids and peopl...	2173	Home Alone	1.408094	67.588495	1990
10	NaN	2001	55835	82	978739200000	2001	9134	7	0.719512	304	The worst book to screen adaption I have ever ...	1	i've read all of these books and perhaps that...	12	Beautiful Creatures	0.446455	26.340859	2001
100	NaN	1996	2491989	23	822960000000	1996	1593929	1292	0.695652	174	Cheesy! Cameos! And learn how to be your own g...	8	this is one of those movies which is so bad ...	1292	The Stupids	0.292260	4.676165	1996
1000	NaN	1996	1408575	30	821318400000	1996	33843	2	0.533333	189	Not a good induction to Shakespeare.	N/A	the film would have been better if the actor...	81	Looking for Richard	1.055385	16.886165	1996

	movie_id	pplus	review_id	stars	topic	url	valence_avg	valence_sum
0	Titanic	0.833386	10027	7	1	http://www.imdb.com/title/tt0120338/usercommen...	0.47976	38.380826
1	Titanic	0.933069	10027	7	1	http://www.imdb.com/title/tt0120338/usercommen...	0.47976	38.380826
2	Titanic	0.863956	10027	7	0	http://www.imdb.com/title/tt0120338/usercommen...	0.47976	38.380826
3	Titanic	0.193702	10027	7	0	http://www.imdb.com/title/tt0120338/usercommen...	0.47976	38.380826
4	Titanic	0.831243	10027	7	0	http://www.imdb.com/title/tt0120338/usercommen...	0.47976	38.380826

	movie_id	pplus	review_id	stars	topic	url	valence_avg	valence_sum
6094	The Guardian	0.767554	1	10	0	http://www.imdb.com/title/tt0406816/usercommen...	0.463927	22.268495
6095	The Guardian	0.959387	1	10	1	http://www.imdb.com/title/tt0406816/usercommen...	0.463927	22.268495
6096	The Guardian	0.590485	1	10	0	http://www.imdb.com/title/tt0406816/usercommen...	0.463927	22.268495
6097	The Guardian	0.991269	1	10	1	http://www.imdb.com/title/tt0406816/usercommen...	0.463927	22.268495
6098	The Guardian	0.878913	1	10	1	http://www.imdb.com/title/tt0406816/usercommen...	0.463927	22.268495
6099	The Guardian	0.198263	1	10	1	http://www.imdb.com/title/tt0406816/usercommen...	0.463927	22.268495
6100	The Guardian	0.987382	1	10	0	http://www.imdb.com/title/tt0406816/usercommen...	0.463927	22.268495
6101	The Guardian	0.890644	1	10	0	http://www.imdb.com/title/tt0406816/usercommen...	0.463927	22.268495
6102	The Guardian	0.945111	1	10	1	http://www.imdb.com/title/tt0406816/usercommen...	0.463927	22.268495
16729	The Guardian	0.767554	1	3	0	http://www.imdb.com/title/tt0406816/usercommen...	0.040237	1.689933
16730	The Guardian	0.959387	1	3	1	http://www.imdb.com/title/tt0406816/usercommen...	0.040237	1.689933
16731	The Guardian	0.590485	1	3	0	http://www.imdb.com/title/tt0406816/usercommen...	0.040237	1.689933
16732	The Guardian	0.991269	1	3	1	http://www.imdb.com/title/tt0406816/usercommen...	0.040237	1.689933
16733	The Guardian	0.878913	1	3	1	http://www.imdb.com/title/tt0406816/usercommen...	0.040237	1.689933
16734	The Guardian	0.198263	1	3	1	http://www.imdb.com/title/tt0406816/usercommen...	0.040237	1.689933
16735	The Guardian	0.987382	1	3	0	http://www.imdb.com/title/tt0406816/usercommen...	0.040237	1.689933
16736	The Guardian	0.890644	1	3	0	http://www.imdb.com/title/tt0406816/usercommen...	0.040237	1.689933
16737	The Guardian	0.945111	1	3	1	http://www.imdb.com/title/tt0406816/usercommen...	0.040237	1.689933

	review_id	topic	level_2	count	max	mean	min	stars	valence_avg	valence_sum	var
0	0	0	The Guardian	4	0.959577	0.931161	0.902744	6.0	0.332231	16.730396	0.001077
1	0	1	The Guardian	8	0.869453	0.690997	0.379703	6.0	0.332231	16.730396	0.039329
2	1	0	The Guardian	8	0.987382	0.809016	0.590485	6.5	0.252082	11.979214	0.025129
3	1	1	The Guardian	10	0.991269	0.794589	0.198263	6.5	0.252082	11.979214	0.100270
4	2	0	The Guardian	4	0.907921	0.842568	0.777215	5.0	0.318188	28.525569	0.005695

	review_id	topic	level_2	count	max	mean	min	stars	valence_avg	valence_sum	var
5767	10037	0	Titanic	4	0.868201	0.704629	0.552403	9	0.700832	39.246578	0.016687
5768	10037	1	Titanic	2	0.879267	0.661613	0.443958	9	0.700832	39.246578	0.094747