CS109 Final Project: Take One

<img src="dogfail.jpg", width="500px">

The following notebook contains our original attempt to predict box office gross incomes using text sentiment analysis. It represents a large portion of our work and many hours of gathering, cleaning, and doing preliminary analysis on data, which is why we felt it necessary to include here. This notebook contains our original line of thought and shows data science process. We consider it a fail not in the sense that we were doing poor statistics, but in the sense that the data we obtained was not fit for the task at hand. Our preliminary analysis helped to illuminate this. We ultimately decided that we needed more data, and have included the process for obtaining and analysis this better data in another notebook, Final_Project_vFINAL. Please see that notebook after this one.

Background & Motivation

Social media and entertainment are such pervasive parts of millennials' lives. We want to study the intersection of these two. Is it possible to predict box office success of film through sentiments expressed on social media? Stay tuned for more!


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

Scrape Box Office Mojo and Prepare IMDB Data

For our project we will be using data from 3 different sources

    Box Office Mojo (BOM) (http://www.boxofficemojo.com) is a website that aggregates, in a table, a list of all movies released in a year and attributes such as how much it grossed in the opening week, how much it grossed in total and how long it aired for
    Large Movie Review Dataset (http://ai.stanford.edu/~amaas/data/sentiment/) is a polarized dataset of movie reviews from IMDB prepared by Maas et al from Stanford. The dataset contains 25,000 entries in the training set and 25,000 entries in the test set.
    AFINN-111 Dictionary (http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=60100) is a dictionary list of 2477 english words and phrases rated for valence with an integer from -5 to 5. Originally prepared by Finn Årup Nielsen

In this first milestone, we will get all the data into a format that we can start running analysis on!

Scraping and Loading Our Data

Scraping Box Office Mojo

First we import the requests and BeautifulSoup libraries to make working with HTTP requests easier, and then easily transfer HTML content to Python data structures.


In [2]:
from bs4 import BeautifulSoup
# The "requests" library makes working with HTTP requests easier
# than the built-in urllib libraries.
import requests

Secondly, we prepare the data frame movie_df to store the data that we will scrape from BOM. We give this dataframe 9 columns:

* ranking: the ranking of the movie in its release year by gross
* title: movie title
* gross: how much the movie grossed while in theatres
* Total_theaters: the total number of theaters that showed this movie
* opening_gross: how much the movie grossed in the opening weekend (Fri-Sun)
* opening_theaters: the total number of theaters that showed this movie in the opening weekend (Fri-Sun)
* open_date: date of opening
* close_date: date of closing
* year: year of release

In [3]:
bom_df = pd.DataFrame(columns=['close_date', 'gross', 'open_date', 'opening_gross', 'opening_theaters','ranking','title','total_theaters','year'])

Now we write a function rowInfoGrabber that we will call in a loop over the table on the BOM webpage to grab the attributes and save them into the corresponding columns in movie_df.


In [4]:
def rowInfoGrabber(r):
    info = []
    # Ranking
    info.append(int(r.find("font").get_text()))
    # Title
    info.append(r.find("a").get_text())
    # Gross
    info.append(int(r.find("td", attrs={"align":"right"}).find("b").get_text().strip("$").replace(",","")))
    '''
    For the next 3 categories, we need to deal with the 2000 Anomaly "Fantasia" where there are missing numbers.
    In this case I have chosen to replace the missing values 'N/A' with the values from 'Final Destination', which
    if right above it in the movie table and differs in gross income by about $1 million, which is a small 
    difference. See the picture below for a snapshot of the anomaly in the movie table from 2000.
    '''
    # Total number of theaters
    if r.find_all("td",attrs={"align":"right"})[1].find("font").get_text().replace(",","") == 'N/A':
        info.append(2587)
    else:
        info.append(int(r.find_all("td",attrs={"align":"right"})[1].find("font").get_text().replace(",","")))
    # Opening Gross
    if r.find_all("td", attrs={"align":"right"})[2].find("font").get_text().strip("$").replace(",","") == 'N/A':
        info.append(10015822)
    else: 
        info.append(int(r.find_all("td", attrs={"align":"right"})[2].find("font").get_text().strip("$").replace(",","")))
    # Opening Number of Theaters
    if r.find_all("td", attrs={"align":"right"})[3].find("font").get_text().replace(",","") == 'N/A':
        info.append(2587)
    else:
        info.append(int(r.find_all("td", attrs={"align":"right"})[3].find("font").get_text().replace(",","")))
    # Date of Opening
    info.append(r.find_all("td", attrs={"align":"right"})[4].find("a").get_text())
    # Date of Closing: Before 2002 they didn't have a "closing" date in their tables. We must account for this.
    if (len(r.find_all("td", attrs={"align":"right"})) <= 5):
        info.append('-')
    else:
        info.append(r.find_all("td", attrs={"align":"right"})[5].find("font").get_text())
    return info

<img src="fantasia.png", width="700px">


In [5]:
fields = ["ranking", "title", "gross", "total_theaters", "opening_gross", "opening_theaters", "open_date", "close_date"]

Finally we're ready to scrape!
Because IMDB was created in 1990, we will scrape that far back in BOM. So we're scraping the past 26 years (1990 - 2015). Also note that because the HTML was changed starting in 2001, our scraping will be a little different before and after then.


In [6]:
%%time
years = [1990 + i for i in range(26)]
for year in years:
    pageText = requests.get("http://www.boxofficemojo.com/yearly/chart/?yr=%(yr)d&p=.htm" % {'yr':year})
    soup = BeautifulSoup(pageText.text, "html.parser")
    movieTable = soup.find("td", attrs={"colspan":"3"})
    movieRows = movieTable.find("table").find_all("tr")[2:102]
    movie_dicts = [dict(zip(fields, rowInfoGrabber(row))) for row in movieRows]
    year_df = pd.DataFrame(movie_dicts)
    year_df['year'] = year
    bom_df = bom_df.append(year_df, ignore_index=True)
    time.sleep(1)


CPU times: user 12.9 s, sys: 210 ms, total: 13.1 s
Wall time: 46.4 s

In [43]:
print bom_df.shape
bom_df.head()


(2600, 9)
Out[43]:
close_date gross open_date opening_gross opening_theaters ranking title total_theaters year
0 - 285761243 11/16 17081997 1202 1 Home Alone 2173 1990
1 - 217631306 7/13 12191540 1101 2 Ghost 1766 1990
2 - 184208848 11/9 598257 14 3 Dances with Wolves 1636 1990
3 - 178406268 3/23 11280591 1325 4 Pretty Woman 1811 1990
4 - 135265915 3/30 25398367 2006 5 Teenage Mutant Ninja Turtles 2377 1990

Because some films do not have a close date, we will have to be careful with the close date! Next, we combine the close_date, open_date and year columns into two columns close_date and open_date that are time series. This will make it easier for us to work with the data in the future.


In [44]:
# splitting the close_date and open_date into the respective month and day
bom_df['close_month'] = bom_df['close_date'].map(lambda x: '0' if x=='-' else x[:x.find('/')])
bom_df['close_day'] = bom_df['close_date'].map(lambda x: '0' if x=='-' else x[x.find('/')+1:len(x)])
bom_df['open_month'] = bom_df['open_date'].map(lambda x: x[:x.find('/')])
bom_df['open_day'] = bom_df['open_date'].map(lambda x: x[x.find('/')+1:len(x)])

# dropping the old close_date and open_date
bom_df = bom_df.drop('close_date', 1)
bom_df = bom_df.drop('open_date', 1)

# creating an open_year by turning the year column into a string and getting rid of trailing bits
bom_df['open_year'] = bom_df.year.astype(str)
bom_df['open_year'] = bom_df.open_year.map(lambda x: x[:x.find('.')])

# creating a close_year column, by looking at whether the close month is earlier/later than the open month in the year
close_month = bom_df['close_month'].astype(int)
open_month = bom_df['open_month'].astype(int)
year = bom_df['year'].astype(int)
close_year=[]
for i in range (0, len(year)):
    if close_month[i] >= open_month[i]:
        close_year.append(year[i])
    else:
        close_year.append(year[i]+1) 
bom_df['close_year'] = close_year
bom_df['close_year'] = bom_df['close_year'].astype(str)

In [45]:
# making close_date and open_date by concatenating the year, month and day
import datetime
close_date = []
for index, row in bom_df.iterrows():
    if row.close_day != '0':
        close_date.append(datetime.datetime(int(row.close_year), int(row.close_month), int(row.close_day)))
    else: 
        close_date.append(None)
bom_df['close_date'] = close_date

bom_df['open_date']=bom_df.open_year + '-' + bom_df.open_month + '-' + bom_df.open_day
bom_df['open_date']=bom_df['open_date'].apply(pd.datetools.parse)

Let's take a look at the data, now!


In [46]:
bom_df.head()


Out[46]:
gross opening_gross opening_theaters ranking title total_theaters year close_month close_day open_month open_day open_year close_year close_date open_date
0 285761243 17081997 1202 1 Home Alone 2173 1990 0 0 11 16 1990 1991 None 1990-11-16
1 217631306 12191540 1101 2 Ghost 1766 1990 0 0 7 13 1990 1991 None 1990-07-13
2 184208848 598257 14 3 Dances with Wolves 1636 1990 0 0 11 9 1990 1991 None 1990-11-09
3 178406268 11280591 1325 4 Pretty Woman 1811 1990 0 0 3 23 1990 1991 None 1990-03-23
4 135265915 25398367 2006 5 Teenage Mutant Ninja Turtles 2377 1990 0 0 3 30 1990 1991 None 1990-03-30

Let's take a look at if we can get the run times for each movie!


In [47]:
run_time=[]
for index, row in bom_df.iterrows():
    if row.close_date != None:
        run_time.append(row['close_date']-row['open_date'])
    else: 
        run_time.append('N/A')

Looks like the data is ready for us to use! Let's save this data so we're ready to use it next time.


In [12]:
! pip install pymongo


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

In [48]:
# Save the movie Dictionaries corresponding to each row of the BoxOfficeMojo table.
import json 
import pymongo
from bson import json_util

# Make a dictionary out of the dataset for storage in JSON format.
movieSaved = {feature: bom_df[feature].values.tolist() for feature in bom_df.columns.values}
fp = open("allMovies.json","w")
json.dump(movieSaved, fp, default=json_util.default)
fp.close()

Loading and preparing IMDB review dataset

We have cleaned up our IMDB review dataset in the ipython notebook IMDB_reviews.ipynb. From that notebook we have been able to save dictionaries of our data, which we will now call.


In [49]:
with open("train_df_dict.json", "r") as fd:
    train_df_dict = json.load(fd)
with open("test_df_dict.json", "r") as fd:
    test_df_dict = json.load(fd)

In [50]:
train_df = pd.DataFrame(train_df_dict)
test_df = pd.DataFrame(test_df_dict)

The Stanford group distinguishes between train and test because this was relevant for their project. This may prove to be useful later, so we will keep two separate dataframes. However, for our purposes at the moment, we can combine them since the BOM data will serve as our true data set.


In [51]:
IMDB_df = train_df.append(test_df)

We want to figure out which movies from IMDB_df are also present in our BOM DF. So let's get all the movie titles in our BOM table.


In [52]:
BOM_movie_list = bom_df.title.values.tolist()

Now let's create a mask over IMDB_df, the boolean values of which indicate whether or not a movie is in the BOM list.


In [53]:
movie_mask = [(movie in BOM_movie_list) for movie in IMDB_df.movie_name]

In [54]:
sum(movie_mask)


Out[54]:
4111

We can now create our IMDB data frame with only those movies that also appear in the BOM tables from 1990 - 2015.


In [55]:
IMDB_dftouse=IMDB_df[movie_mask]

Finally we want to save our dictionary of IMDB_dftouse into a JSON file for storage.


In [56]:
IMDB_dftouse_dict = {feature: IMDB_dftouse[feature].values.tolist() for feature in IMDB_dftouse.columns.values}
fp = open("IMDB_dftouse_dict.json","w")
json.dump(IMDB_dftouse_dict, fp)
fp.close()

In [57]:
# Reopen
with open("IMDB_dftouse_dict.json", "r") as fd:
    IMDB_dftouse_dict = json.load(fd)
IMDB_dftouse = pd.DataFrame(IMDB_dftouse_dict)

Analyzing and Saving Review Attributes Using labMT Happiness Dictionary

Note: We originally intended to use the AFINN word list prepared by Finn Årup Nielsen to obtain word valences (http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=60100), but ultimately decided to use labMT.
labMTs word list contains around 10,000 scored words, whereas AFINN's only contains 2477.

Now let's download labMT. The file contains a "happiness" value, and ranks words by their happiness. It also includes mean and standard deviation, Twitter rank and Google rank.


In [58]:
url = 'http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0026752.s001'
labmt = pd.read_csv(url, skiprows=2, sep='\t', index_col=0)

In [59]:
labmt.head()


Out[59]:
happiness_rank happiness_average happiness_standard_deviation twitter_rank google_rank nyt_rank lyrics_rank
word
laughter 1 8.50 0.9313 3600 -- -- 1728
happiness 2 8.44 0.9723 1853 2458 -- 1230
love 3 8.42 1.1082 25 317 328 23
happy 4 8.30 0.9949 65 1372 1313 375
laughed 5 8.26 1.1572 3334 3542 -- 2332

Now let's create a happiness dictionary of (word, valence) pairs where each valence is that word's original valence minus the average valence.


In [60]:
average = labmt.happiness_average.mean()
happiness = (labmt.happiness_average - average).to_dict()

In [61]:
print "Score(happy): ", happiness['happy']
print "Score(miserable): ", happiness['miserable']
print "Best score: ", max(happiness.values())
print "Worst score: ", min(happiness.values())


Score(happy):  2.92476032088
Score(miserable):  -2.83523967912
Best score:  3.12476032088
Worst score:  -4.07523967912

In [62]:
# Save to disc
# fp = open("happiness.json","w")
# json.dump(happiness, fp)
# fp.close()

In [63]:
# Reopen
with open("happiness.json", "r") as fp:
    happiness = json.load(fp)

Now let's collect several attributes from a given review's text body, and save all valuable information into a new data frame. First we define a function that removes stop words (all non important words from a valence perspective) from a text body.


In [64]:
from sklearn.feature_extraction import text
stopwords = text.ENGLISH_STOP_WORDS
punctuation = list('.,;:!?()[]{}`''\"@#$%^&*+-|-=~_')

def removeStopWords(text, stopwords = stopwords):
    new_text = ""
    for word in text.split():
        if word not in stopwords:
            while len(word) != 0 and word[-1] in punctuation:
                word = word[:len(word)-1]
            new_text += word + ' '
    return new_text

Now we'll write a function that returns total happiness, average happiness, total scorable words, and percentage of scorable words in a given review text.


In [65]:
'''
Name: getValenceInfo()
Inputs: review text, dictionary of happiness
Returns: a 4-tuple of (happiness total, happiness average, total # of scorable words, % of scorable words)
'''
def getValenceInfo(text, valenceDict):
    total_words = len(text.split())
    happiness_total, count_relevant = 0, 0
    for word in text.split():
        if word in valenceDict.keys():
            count_relevant += 1
            happiness_total += valenceDict[word]
    if count_relevant != 0: 
        avg_valence = 1.*happiness_total/count_relevant
    else: 
        avg_valence = 0
    return happiness_total, avg_valence, total_words, 1.*count_relevant / total_words

Now we'll write a function that, given a data frame, returns a new data frame with the concatenation of valence (happiness) info in 4 new columns: valence sum, valence average, # of scorable words, % of scorable words.


In [66]:
'''
Name: getAllInfo
Input: data frame, happiness dictionary, list of stop words
Returns: a new data frame with 4 new columns: valence_sum, valence_avg, n_scorables, pct_scorables
'''
def getAllInfo(df, valenceDict, stopwords): 
    valence_suml, valence_avgl, review_lenl, review_fractionl = [], [], [], []
    for i, row in df.iterrows():
        cleaned_review = removeStopWords(row['text'], stopwords)
        valence_sum, valence_avg, review_len, review_fraction = getValenceInfo(cleaned_review, valenceDict)
        valence_suml.append(valence_sum)
        valence_avgl.append(valence_avg)
        review_lenl.append(review_len)
        review_fractionl.append(review_fraction)
    conc = pd.DataFrame({'valence_sum': valence_suml, 'valence_avg':valence_avgl ,'n_scorables': review_lenl, 
                         'pct_scorables': review_fractionl})
    return pd.concat([df, conc], axis=1)

Now let's create a new dataframe valence_df with the valence statistics run on our IMDB_df. This code takes a few minutes to run.


In [67]:
%%time
valence_df = getAllInfo(IMDB_dftouse, happiness, stopwords)


---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-67-65c3726ef4dc> in <module>()
----> 1 get_ipython().run_cell_magic(u'time', u'', u'valence_df = getAllInfo(IMDB_dftouse, happiness, stopwords)')

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_cell_magic(self, magic_name, line, cell)
   2262             magic_arg_s = self.var_expand(line, stack_depth)
   2263             with self.builtin_trap:
-> 2264                 result = fn(magic_arg_s, cell)
   2265             return result
   2266 

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k)
    191     # but it's overkill for just that one bit of state.
    192     def magic_deco(arg):
--> 193         call = lambda f, *a, **k: f(*a, **k)
    194 
    195         if callable(arg):

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)
   1164         else:
   1165             st = clock2()
-> 1166             exec(code, glob, local_ns)
   1167             end = clock2()
   1168             out = None

<timed exec> in <module>()

<ipython-input-66-b22b5564e667> in getAllInfo(df, valenceDict, stopwords)
      8     for i, row in df.iterrows():
      9         cleaned_review = removeStopWords(row['text'], stopwords)
---> 10         valence_sum, valence_avg, review_len, review_fraction = getValenceInfo(cleaned_review, valenceDict)
     11         valence_suml.append(valence_sum)
     12         valence_avgl.append(valence_avg)

<ipython-input-65-882715705a0c> in getValenceInfo(text, valenceDict)
      9     for word in text.split():
     10         if word in valenceDict.keys():
---> 11             count_relevant += 1
     12             happiness_total += valenceDict[word]
     13     if count_relevant != 0:

KeyboardInterrupt: 

In [ ]:
valence_df.head()

Let's convert True/False to 1/0. It is needed to make valence_df JSON serializable, also better practice.


In [143]:
valence_df.positive = 1.0*valence_df.positive

In [144]:
# Save to disc
fp = open("valence_df_dict.json","w")
json.dump(valence_df.to_dict(), fp)
fp.close()

In [68]:
# Reopen
with open("valence_df_dict.json", "r") as fp:
    valence_df_dict = json.load(fp)
valence_df = pd.DataFrame(valence_df_dict)

Collecting Valence Statistics and Adjusting for Inflation

Let's see how well our valence summaries correlate with the reviews stars. This will serve as a sanity check to see if our valence estimates are working.


In [69]:
stars_val_avg = ols('stars ~ valence_avg',valence_df).fit()
stars_val_avg.summary()
sns.regplot(y="valence_avg", x="stars", data=valence_df)


Out[69]:
<matplotlib.axes._subplots.AxesSubplot at 0x10ea4ff50>

Yes it does, to some extent. The explanatory power is not high though.

Now, let's have a look at the valences of our positive and negative reviews and see how each is distributed to get a better sense of our data.


In [70]:
graph_val_avg = plt.hist(valence_df.valence_sum[valence_df.stars >=7],bins=40,alpha=0.7,label="Positive reviews")
graph_val_avg = plt.hist(valence_df.valence_sum[valence_df.stars <=4],bins=40,alpha=0.7,label="Negative reviews")
graph_val_avg = plt.title("Valence sum for positive and negative reviews")
graph_val_avg = plt.xlabel("Sum of valence")
graph_val_avg = plt.ylabel("Frequency")
graph_val_avg = plt.legend(loc='upper right')



In [71]:
graph_val_avg = plt.hist(valence_df.valence_avg[valence_df.stars >=7],bins=40,alpha=0.7,label="Positive reviews")
graph_val_avg = plt.hist(valence_df.valence_avg[valence_df.stars <=4],bins=40,alpha=0.7,label="Negative reviews")
graph_val_avg = plt.title("Valence average for positive and negative reviews")
graph_val_avg = plt.xlabel("Average valence")
graph_val_avg = plt.ylabel("Frequency")
graph23 = plt.legend(loc='upper left')


Now we'll import allMoives_new.json. Remember that we still need to convert some of the columns into the datetime type so we'll do that afterwards.


In [72]:
with open("allMovies.json", "r") as fd:
    bom_df = json.load(fd)
    bom_df = pd.DataFrame(bom_df)

In [73]:
bom_df[2572:2577]


Out[73]:
close_date close_day close_month close_year gross open_date open_day open_month open_year opening_gross opening_theaters ranking title total_theaters year
2572 {u'$date': 1446076800000} 29 10 2015 27740955 1440115200000000000 21 8 2015 10542116 2766 73 Sinister 2 2799 2015
2573 {u'$date': 1448496000000} 26 11 2015 27288872 1440547200000000000 26 8 2015 8111264 3355 74 No Escape 3415 2015
2574 None 0 0 2016 27121602 1445558400000000000 23 10 2015 10812861 3082 75 The Last Witch Hunter 3082 2015
2575 {u'$date': 1444521600000} 11 10 2015 26822144 1438905600000000000 7 8 2015 6610961 1603 76 Ricki and the Flash 2064 2015
2576 {u'$date': 1426723200000} 19 3 2015 26501323 1420156800000000000 2 1 2015 15027415 2602 77 The Woman in Black 2: Angel of Death 2602 2015

Let's make the dataframe a little bit cleaner.


In [74]:
# making close_date and open_date by concatenating the year, month and day
import datetime
close_date = []
for index, row in bom_df.iterrows():
    if row.close_day != '0':
        close_date.append(datetime.datetime(int(row.close_year), int(row.close_month), int(row.close_day)))
    else: 
        close_date.append(None)
bom_df['close_date'] = close_date

bom_df['open_date']=bom_df.open_year + '-' + bom_df.open_month + '-' + bom_df.open_day
bom_df['open_date']=bom_df['open_date'].apply(pd.datetools.parse)

# dropping unnecessary columns
bom_df = bom_df.drop('close_day', 1)
bom_df = bom_df.drop('close_month', 1)
bom_df = bom_df.drop('open_day', 1)
bom_df = bom_df.drop('open_month', 1)

Great! Let's take a look at all the dataframes we've created


In [75]:
bom_df.head()


Out[75]:
close_date close_year gross open_date open_year opening_gross opening_theaters ranking title total_theaters year
0 None 1991 285761243 1990-11-16 1990 17081997 1202 1 Home Alone 2173 1990
1 None 1991 217631306 1990-07-13 1990 12191540 1101 2 Ghost 1766 1990
2 None 1991 184208848 1990-11-09 1990 598257 14 3 Dances with Wolves 1636 1990
3 None 1991 178406268 1990-03-23 1990 11280591 1325 4 Pretty Woman 1811 1990
4 None 1991 135265915 1990-03-30 1990 25398367 2006 5 Teenage Mutant Ninja Turtles 2377 1990
5 None 1991 122012643 1990-03-02 1990 17161835 1225 6 The Hunt for Red October 1817 1990
6 None 1991 119394840 1990-06-01 1990 25533700 2060 7 Total Recall 2131 1990
7 None 1991 117540947 1990-07-06 1990 21744661 2507 8 Die Hard 2: Die Harder 2507 1990
8 None 1991 103738726 1990-06-15 1990 22543911 2332 9 Dick Tracy 2332 1990
9 None 1991 91457688 1990-12-22 1990 7918560 1833 10 Kindergarten Cop 1937 1990
10 None 1991 87727583 1990-05-25 1990 19089645 2019 11 Back to the Future Part III 2070 1990
11 None 1991 86303188 1990-07-27 1990 11718981 1349 12 Presumed Innocent 1451 1990
12 None 1991 82670733 1990-06-29 1990 15490445 2307 13 Days of Thunder 2307 1990
13 None 1991 80818974 1990-06-08 1990 19475559 2721 14 Another 48 HRS. 2721 1990
14 None 1991 71609321 1990-11-21 1990 13774642 1281 15 Three Men and a Little Lady 1614 1990
15 None 1991 70978012 1990-05-18 1990 15338160 1944 16 Bird on a Wire 2008 1990
16 None 1991 66666062 1990-12-25 1990 19558558 1901 17 The Godfather Part III 1922 1990
17 None 1991 61489265 1990-08-10 1990 10034685 1319 18 Flatliners 1483 1990
18 None 1991 61276872 1990-11-30 1990 10076834 1244 19 Misery 1370 1990
19 None 1991 56362352 1990-12-07 1990 159622 2 20 Edward Scissorhands 1372 1990
20 None 1991 53470891 1990-07-27 1990 10026900 1714 21 Problem Child 1769 1990
21 None 1991 53208180 1990-07-20 1990 8045760 1479 22 Arachnophobia 2005 1990
22 None 1991 52096475 1990-12-22 1990 417076 12 23 Awakenings 1330 1990
23 None 1991 47789074 1990-12-14 1990 8100640 1576 24 Look Who's Talking Too 1647 1990
24 None 1991 47410827 1990-02-09 1990 9213631 1301 25 Hard to Kill 1508 1990
25 None 1991 46836214 1990-09-19 1990 6368901 1070 26 Goodfellas 1328 1990
26 None 1991 46044396 1990-10-05 1990 11790047 1968 27 Marked for Death 1974 1990
27 None 1991 45681173 1990-06-22 1990 14145411 1768 28 Robocop 2 1806 1990
28 None 1991 44645619 1990-07-13 1990 7708029 1901 29 The Jungle Book (re-issue) (1990) 1923 1990
29 None 1991 44143410 1990-08-03 1990 8017438 1770 30 Young Guns II 1770 1990
... ... ... ... ... ... ... ... ... ... ... ...
2570 None 2016 31090320 2015-10-16 2015 13143310 2984 71 Crimson Peak 2991 2015
2571 None 2016 29476394 2015-09-02 2015 8246267 1960 72 A Walk in the Woods 2158 2015
2572 2015-10-29 00:00:00 2015 27740955 2015-08-21 2015 10542116 2766 73 Sinister 2 2799 2015
2573 2015-11-26 00:00:00 2015 27288872 2015-08-26 2015 8111264 3355 74 No Escape 3415 2015
2574 None 2016 27121602 2015-10-23 2015 10812861 3082 75 The Last Witch Hunter 3082 2015
2575 2015-10-11 00:00:00 2015 26822144 2015-08-07 2015 6610961 1603 76 Ricki and the Flash 2064 2015
2576 2015-03-19 00:00:00 2015 26501323 2015-01-02 2015 15027415 2602 77 The Woman in Black 2: Angel of Death 2602 2015
2577 2015-05-07 00:00:00 2015 26461644 2015-03-13 2015 11012305 3171 78 Run All Night 3171 2015
2578 2015-06-11 00:00:00 2015 25801047 2015-02-27 2015 10203437 2666 79 The Lazarus Effect 2666 2015
2579 2015-09-03 00:00:00 2015 25442958 2015-04-10 2015 237264 4 80 Ex Machina 2004 2015
2580 None 2016 22822987 2015-11-13 2015 8317545 2603 81 Love the Coopers 2603 2015
2581 2015-09-17 00:00:00 2015 22764410 2015-07-10 2015 9808463 2720 82 The Gallows 2720 2015
2582 2015-10-15 00:00:00 2015 22467450 2015-08-21 2015 8326530 3261 83 Hitman: Agent 47 3273 2015
2583 2015-03-26 00:00:00 2015 22348241 2015-01-30 2015 8310252 2893 84 Project Almanac 2900 2015
2584 2015-05-14 00:00:00 2015 21571189 2015-01-30 2015 6213362 1823 85 Black or White 1823 2015
2585 2015-07-30 00:00:00 2015 21067116 2015-05-29 2015 9670235 2815 86 Aloha 2815 2015
2586 2015-10-22 00:00:00 2015 19375982 2015-08-05 2015 4038962 2320 87 Shaun the Sheep Movie 2360 2015
2587 2015-05-21 00:00:00 2015 18754371 2015-01-16 2015 197000 12 88 Still Alice 1318 2015
2588 None 2016 18300124 2015-10-23 2015 8070493 1656 89 Paranormal Activity: The Ghost Dimension 1656 2015
2589 None 2016 17753943 2015-10-09 2015 521522 4 90 Steve Jobs 2493 2015
2590 2015-11-05 00:00:00 2015 17737646 2015-07-17 2015 2434908 361 91 Mr. Holmes 898 2015
2591 2015-09-17 00:00:00 2015 17506470 2015-06-19 2015 6100010 2002 92 Dope 2002 2015
2592 None 2016 17402274 2015-11-20 2015 6652996 2392 93 The Secret in their Eyes (2015) 2392 2015
2593 None 2016 17265460 2015-12-04 2015 16293325 2902 94 Krampus 2902 2015
2594 2015-03-19 00:00:00 2015 17223265 2015-02-06 2015 7217640 2875 95 Seventh Son 2875 2015
2595 None 2016 16792305 2015-11-06 2015 295009 5 96 Spotlight 1089 2015
2596 2015-07-23 00:00:00 2015 16432322 2015-04-17 2015 4577861 2012 97 Monkey Kingdom 2012 2015
2597 2015-11-19 00:00:00 2015 16029670 2015-09-04 2015 7355622 3434 98 The Transporter Refueled 3434 2015
2598 2015-06-25 00:00:00 2015 14674076 2015-03-13 2015 160089 4 99 It Follows 1655 2015
2599 2015-10-08 00:00:00 2015 14440985 2015-08-21 2015 5454284 2778 100 American Ultra 2778 2015

2600 rows × 11 columns

Now, we want to make a new dataframe flattened_df that we can use to run our regressions on. This dataframe will include all the columns in movie_df and the extra columns

  • number of reviews for the movie in IMDB_df
  • average stars from the reviews
  • overall ranking

In [76]:
# set index to title
indexed_df = bom_df.set_index("title")

In [77]:
# use groupby to get the review_count, star_avg, valence_avg, valence_sum, pct_scorables, n_scorables
gold = valence_df.groupby("movie_name")
n_scorables = gold.n_scorables.mean()
pct_scorables = gold.pct_scorables.mean()
review_count = gold.movie_name.count()
star_avg = gold.stars.mean()
positive = gold.positive.mean()
valence_avg = gold.valence_avg.mean()
valence_sum = gold.valence_sum.mean()

In [78]:
# concatenate the two dfs into our final dataframe flattened_df
flattened_df = pd.concat([indexed_df, review_count], axis=1, join_axes=[indexed_df.index])
flattened_df.rename(columns={'movie_name': 'review_count'}, inplace=True)
flattened_df = pd.concat([flattened_df, star_avg], axis=1, join_axes=[indexed_df.index])
flattened_df.rename(columns={'stars': 'star_avg'}, inplace=True)
flattened_df = pd.concat([flattened_df, positive], axis=1, join_axes=[indexed_df.index])

flattened_df = pd.concat([flattened_df, valence_avg], axis=1, join_axes=[indexed_df.index])
# flattened_df.rename(columns={'movie_name': 'review_count'}, inplace=True)
flattened_df = pd.concat([flattened_df, valence_sum], axis=1, join_axes=[indexed_df.index])
# flattened_df.rename(columns={'movie_name': 'review_count'}, inplace=True)

flattened_df = pd.concat([flattened_df, n_scorables], axis=1, join_axes=[indexed_df.index])

flattened_df = pd.concat([flattened_df, pct_scorables], axis=1, join_axes=[indexed_df.index])


# flattened_df.rename(columns={'absolute_stars': 'abs_star_avg'}, inplace=True)

In [79]:
flattened_df.head()


Out[79]:
close_date close_year gross open_date open_year opening_gross opening_theaters ranking total_theaters year review_count star_avg positive valence_avg valence_sum n_scorables pct_scorables
title
Home Alone None 1991 285761243 1990-11-16 1990 17081997 1202 1 2173 1990 NaN NaN NaN NaN NaN NaN NaN
Ghost None 1991 217631306 1990-07-13 1990 12191540 1101 2 1766 1990 NaN NaN NaN NaN NaN NaN NaN
Dances with Wolves None 1991 184208848 1990-11-09 1990 598257 14 3 1636 1990 NaN NaN NaN NaN NaN NaN NaN
Pretty Woman None 1991 178406268 1990-03-23 1990 11280591 1325 4 1811 1990 2 1 0 0.407409 84.055626 338 0.613324
Teenage Mutant Ninja Turtles None 1991 135265915 1990-03-30 1990 25398367 2006 5 2377 1990 NaN NaN NaN NaN NaN NaN NaN

Let's create a new column for the absolute value of the valence averages of the reviews for a particular movie. This will eventually test the hypothesis of whether or not polarizing movies make a lot of money.


In [46]:
valence_df['abs_valence_avg'] = np.abs(valence_df['valence_avg']-np.mean(flattened_df['valence_avg']))
abs_valence_avg = gold.abs_valence_avg.mean()
flattened_df = pd.concat([flattened_df, abs_valence_avg], axis=1, join_axes=[indexed_df.index])

In [47]:
flattened_df.head()


Out[47]:
close_date close_year gross open_date open_year opening_gross opening_theaters ranking total_theaters year review_count star_avg positive valence_avg valence_sum n_scorables pct_scorables abs_valence_avg
title
Home Alone None 1991 285761243 1990-11-16 1990 17081997 1202 1 2173 1990 NaN NaN NaN NaN NaN NaN NaN NaN
Ghost None 1991 217631306 1990-07-13 1990 12191540 1101 2 1766 1990 NaN NaN NaN NaN NaN NaN NaN NaN
Dances with Wolves None 1991 184208848 1990-11-09 1990 598257 14 3 1636 1990 NaN NaN NaN NaN NaN NaN NaN NaN
Pretty Woman None 1991 178406268 1990-03-23 1990 11280591 1325 4 1811 1990 2 1 0 0.407409 84.055626 338 0.613324 0.129283
Teenage Mutant Ninja Turtles None 1991 135265915 1990-03-30 1990 25398367 2006 5 2377 1990 NaN NaN NaN NaN NaN NaN NaN NaN

Now let's take only those movies that appear in the IMDB data set, i.e. those that have a review count.


In [48]:
flattened_df = flattened_df[~flattened_df['review_count'].map(np.isnan)]
print "Num. overlapping movies: ", flattened_df.shape[0]


Out[48]:
(334, 18)

Adjusting for Inflation

The purchasing power of individuals change over time due to the changes in the value of money. Thus, to compare the true value of movies that came out in different years, we need yearly U.S. inflation data.

For instance, Avatar looks like highest grossing movie of all time, but when adjusted for inflation, turns out it's actually Gone with the Wind.

Hence, we need to adjust the gross incomes of movies for inflation over the past 26 years to get better insight into how successful they truly were in the box office.


In [80]:
inflation = pd.read_csv("inf.csv")

In [81]:
years_90 = range(1990,2015)
infdict = {}
infindex, infvalue = 0, 1

testlist = []
for row in inflation.values:
    currentval = 1 + (row[1]/100)
    cuminf = infvalue*currentval
    infdict[years_90[infindex]] = cuminf
    infindex += 1
    infvalue = cuminf
    testlist.append(cuminf)

inframe = pd.DataFrame(data=testlist, index=range(1990,2015))

In [82]:
infdict


Out[82]:
{1990: 1.0539795644,
 1991: 1.098615219150804,
 1992: 1.1318902930939463,
 1993: 1.1652998117775315,
 1994: 1.1956843237413164,
 1995: 1.229228287177842,
 1996: 1.2652594783591868,
 1997: 1.2948373218617284,
 1998: 1.3149368109750392,
 1999: 1.3437079860225376,
 2000: 1.3890830868495474,
 2001: 1.4283409518690031,
 2002: 1.4509948911070383,
 2003: 1.4839338531885458,
 2004: 1.5236622748059583,
 2005: 1.5753562785628925,
 2006: 1.626176391500925,
 2007: 1.6725658779300527,
 2008: 1.7367773595171863,
 2009: 1.7306023124666896,
 2010: 1.7589849421993997,
 2011: 1.814513310047201,
 2012: 1.852061710150393,
 2013: 1.8791913148899477,
 2014: 1.909675988181881}

For movies that are released this year, we use the inflation data of 2014, since the 2015 numbers will be released in 2016


In [89]:
newgross, newopengross = [], []
for gross, opengross, openyr in zip(flattened_df.gross, flattened_df.opening_gross, flattened_df.open_year):
    if int(openyr) == 2015:
        openyr = 2014
    newgross.append(infdict[int(openyr)]*gross)
    newopengross.append(infdict[int(openyr)]*opengross)


---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-89-60ecfe487ae8> in <module>()
      1 newgross, newopengross = [], []
      2 for gross, opengross, openyr in zip(flattened_df.gross, flattened_df.opening_gross, flattened_df.open_year):
----> 3     newgross.append(infdict[int(openyr)]*gross)
      4     newopengross.append(infdict[int(openyr)]*opengross)

KeyError: 2015

In [54]:
dftouse = flattened_df
dftouse['adj_gross'] = newgross
dftouse['adj_opening_gross'] = newopengross

In [55]:
# creating binary variables of adjusted grossing for classifications
dftouse['adj_gross_bin'] = 1*(dftouse.adj_gross>np.mean(dftouse.adj_gross))
dftouse['adj_opening_gross_bin'] = 1*(dftouse.adj_opening_gross>np.mean(dftouse.adj_opening_gross))

In [56]:
dftouse.head()


Out[56]:
close_date close_year gross open_date open_year opening_gross opening_theaters ranking total_theaters year review_count star_avg positive valence_avg valence_sum n_scorables pct_scorables abs_valence_avg adj_gross adj_opening_gross adj_gross_bin adj_opening_gross_bin
title
Pretty Woman None 1991 178406268 1990-03-23 1990 11280591 1325 4 1811 1990 2 1.000000 0.0 0.407409 84.055626 338.000000 0.613324 0.129283 1.880366e+08 11889512.388355 1 0
Dick Tracy None 1991 103738726 1990-06-15 1990 22543911 2332 9 2332 1990 24 8.875000 1.0 0.619329 46.075866 142.750000 0.580769 0.294604 1.093385e+08 23760821.495652 1 1
Flatliners None 1991 61489265 1990-08-10 1990 10034685 1319 18 1483 1990 30 6.966667 0.8 0.341229 27.310654 145.866667 0.616311 0.164876 6.480843e+07 10576352.925191 0 0
Problem Child None 1991 53470891 1990-07-27 1990 10026900 1714 21 1769 1990 8 2.500000 0.0 0.181693 15.963458 120.000000 0.607353 0.192622 5.635723e+07 10568147.694282 0 0
Marked for Death None 1991 46044396 1990-10-05 1990 11790047 1968 27 1974 1990 3 4.000000 0.0 0.052945 10.623149 376.000000 0.564525 0.277000 4.852985e+07 12426468.601316 0 0

Now that we have adjusted for inflation, we can remove the previous gross and all columns pertaining to date (except perhaps year, since it's interesting).


In [57]:
keep = ['ranking','total_theaters', 'year', 'review_count', 'star_avg','positive','valence_avg','valence_sum',
        'n_scorables','pct_scorables','abs_valence_avg','adj_gross', 'adj_opening_gross', 'adj_gross_bin','adj_opening_gross_bin']
dftouse = dftouse[keep]

In [58]:
dftouse.head()


Out[58]:
ranking total_theaters year review_count star_avg positive valence_avg valence_sum n_scorables pct_scorables abs_valence_avg adj_gross adj_opening_gross adj_gross_bin adj_opening_gross_bin
title
Pretty Woman 4 1811 1990 2 1.000000 0.0 0.407409 84.055626 338.000000 0.613324 0.129283 1.880366e+08 11889512.388355 1 0
Dick Tracy 9 2332 1990 24 8.875000 1.0 0.619329 46.075866 142.750000 0.580769 0.294604 1.093385e+08 23760821.495652 1 1
Flatliners 18 1483 1990 30 6.966667 0.8 0.341229 27.310654 145.866667 0.616311 0.164876 6.480843e+07 10576352.925191 0 0
Problem Child 21 1769 1990 8 2.500000 0.0 0.181693 15.963458 120.000000 0.607353 0.192622 5.635723e+07 10568147.694282 0 0
Marked for Death 27 1974 1990 3 4.000000 0.0 0.052945 10.623149 376.000000 0.564525 0.277000 4.852985e+07 12426468.601316 0 0

In [60]:
# Let's save
fp = open("dftouse.json","w")
json.dump(dftouse.to_dict(), fp)
fp.close()

Preliminary Analysis

Plotting Gross Against Valence Stats


In [8]:
with open("complete_valence.json", "r") as fp:
    complete_df_dict = json.load(fp)
completedf = pd.DataFrame(complete_df_dict)

In [14]:
completedf = completedf.reset_index().drop('index',1)
dftouse = completedf

In [61]:
# Reopen dftouse
with open("dftouse.json", "r") as fp:
    dftouse_dict = json.load(fp)
dftouse = pd.DataFrame(dftouse_dict)

We have chosen to only use opening grossing of movies as our y-variable. This is because the time for which the movie is open is standardized. We considered using (total grossing)/(total time in theaters) as the y-variable, but this was not feasible because we do not have a closing date for every movie and therefore could not calculate the total time in theaters fo every review.

Let's remove outliers from the dataset, so that we can more easily see trends. An outlier in column valence_avg is defined as a point that it more than 3 standard deviations away from the mean.


In [33]:
def is_not_outlier(num, avg, sd, m = 3.):
    return np.absolute(num-avg) < m*sd

In [34]:
v_avg_avg = np.mean(dftouse.valence_avg)
v_avg_sd = np.std(dftouse.valence_avg)
mask = [is_not_outlier(num, v_avg_avg, v_avg_sd) for num in dftouse.valence_avg]
print "Num outliers: ", (len(dftouse.valence_avg) - sum(mask))
dftouse = dftouse[mask]

Now let's make scatter plots for each review, of opening gross values against average valence, sum valence, and polarity (absolute valence_avg).


In [37]:
# First, let's make scatter plots of opening gross numbers against valence_avg
fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize=(25, 8),tight_layout = True)
titles = ["Opening Gross vs. Valence Avg", "Opening Gross vs. Valence Sum", "Opening Gross vs. Polarity"]
xlabels = ["Valence Avg", "Valence Sum", "| Valence Avg |"]
x = [dftouse.valence_avg, dftouse.valence_sum, dftouse.abs_valence_avg]
for (ax, title, xlabel, x) in zip(axes.ravel(), titles, xlabels, x):
    m, b = np.polyfit(x, dftouse.adj_opening_gross, 1)
#     ax.plot(x, m*x + b, '-')
    ax.plot(x, dftouse.adj_opening_gross, '.',x, m*x + b, '-')
    ax.set_title(title)
    ax.set_xlabel(xlabel)
    ax.set_ylim(-1000, 1.8*1e8)


---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-37-692bc31d1be8> in <module>()
      3 titles = ["Opening Gross vs. Valence Avg", "Opening Gross vs. Valence Sum", "Opening Gross vs. Polarity"]
      4 xlabels = ["Valence Avg", "Valence Sum", "| Valence Avg |"]
----> 5 x = [dftouse.valence_avg, dftouse.valence_sum, dftouse.abs_valence_avg]
      6 for (ax, title, xlabel, x) in zip(axes.ravel(), titles, xlabels, x):
      7     m, b = np.polyfit(x, dftouse.adj_opening_gross, 1)

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in __getattr__(self, name)
   2148                 return self[name]
   2149             raise AttributeError("'%s' object has no attribute '%s'" %
-> 2150                                  (type(self).__name__, name))
   2151 
   2152     def __setattr__(self, name, value):

AttributeError: 'DataFrame' object has no attribute 'abs_valence_avg'

The above scatterplots seem to show that there is little correlation between opening gross and all three predictors.

However, we foresee a limitation to our analysis. The reviews dataset we have was constructed by taking the most polarizing reviews that did not represent the realistic viewership response. For example, very successful movies that have bad reviews might be predicted as lowly grossing because they have a few poor reviews and it is in our dataset. To curb this problem, we are going to divide our dataset into movies that that received poor reviews and movies receivin good reviews. This is defined as the reviewer giving less than 4 stars and the reviewer giving more than 7 stars. We then conduct our regression analysis for each of these sub-datasets. Our hypothesis is that good, higher grossing movies that received poor reviews will receive less poorer reviews on average. In contrast, the good movies that received good reviews should receive higher reviews on average. We create these separate datasets below.


In [20]:
dftouse_small = dftouse[dftouse['valence_avg'] <= np.mean(dftouse['valence_avg'])]
dftouse_large = dftouse[dftouse['valence_avg'] >= np.mean(dftouse['valence_avg'])]

In [21]:
# And now, regressions
reg_list = []

og_val_avg = ols('adj_opening_gross ~ valence_avg',dftouse).fit()
og_val_avg_small = ols('adj_opening_gross ~ valence_avg',dftouse_small).fit()
og_val_avg_large = ols('adj_opening_gross ~ valence_avg',dftouse_large).fit()
og_abs_val_avg_large = ols('adj_opening_gross ~ abs_valence_avg',dftouse).fit()

og_val_sum = ols('adj_opening_gross ~ valence_sum',dftouse).fit()
og_val_sum_small = ols('adj_opening_gross ~ valence_sum',dftouse_small).fit()
og_val_sum_large = ols('adj_opening_gross ~ valence_sum',dftouse_large).fit()

reg_list.append(og_val_avg)
reg_list.append(og_val_avg)
reg_list.append(og_val_avg_small)
reg_list.append(og_val_avg_large)
reg_list.append(og_abs_val_avg_large)

reg_list.append(og_val_sum)
reg_list.append(og_val_sum_small)
reg_list.append(og_val_sum_large)

og_val_avg.summary()
og_val_avg_small.summary()
og_val_avg_large.summary()
og_abs_val_avg_large.summary()

og_val_sum.summary()
og_val_sum_small.summary()
og_val_sum_large.summary()


---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-21-e535dc2ef9cf> in <module>()
      2 reg_list = []
      3 
----> 4 og_val_avg = ols('adj_opening_gross ~ valence_avg',dftouse).fit()
      5 og_val_avg_small = ols('adj_opening_gross ~ valence_avg',dftouse_small).fit()
      6 og_val_avg_large = ols('adj_opening_gross ~ valence_avg',dftouse_large).fit()

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/statsmodels/base/model.pyc in from_formula(cls, formula, data, subset, *args, **kwargs)
    145         (endog, exog), missing_idx = handle_formula_data(data, None, formula,
    146                                                          depth=eval_env,
--> 147                                                          missing=missing)
    148         kwargs.update({'missing_idx': missing_idx,
    149                        'missing': missing})

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/statsmodels/formula/formulatools.pyc in handle_formula_data(Y, X, formula, depth, missing)
     63         if data_util._is_using_pandas(Y, None):
     64             result = dmatrices(formula, Y, depth, return_type='dataframe',
---> 65                                NA_action=na_action)
     66         else:
     67             result = dmatrices(formula, Y, depth, return_type='dataframe',

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/highlevel.pyc in dmatrices(formula_like, data, eval_env, NA_action, return_type)
    295     eval_env = EvalEnvironment.capture(eval_env, reference=1)
    296     (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env,
--> 297                                       NA_action, return_type)
    298     if lhs.shape[1] == 0:
    299         raise PatsyError("model is missing required outcome variables")

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/highlevel.pyc in _do_highlevel_design(formula_like, data, eval_env, NA_action, return_type)
    150         return iter([data])
    151     builders = _try_incr_builders(formula_like, data_iter_maker, eval_env,
--> 152                                   NA_action)
    153     if builders is not None:
    154         return build_design_matrices(builders, data,

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/highlevel.pyc in _try_incr_builders(formula_like, data_iter_maker, eval_env, NA_action)
     55                                        formula_like.rhs_termlist],
     56                                       data_iter_maker,
---> 57                                       NA_action)
     58     else:
     59         return None

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/build.pyc in design_matrix_builders(termlists, data_iter_maker, NA_action)
    658                                                    factor_states,
    659                                                    data_iter_maker,
--> 660                                                    NA_action)
    661     # Now we need the factor evaluators, which encapsulate the knowledge of
    662     # how to turn any given factor into a chunk of data:

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/build.pyc in _examine_factor_types(factors, factor_states, data_iter_maker, NA_action)
    422     for data in data_iter_maker():
    423         for factor in list(examine_needed):
--> 424             value = factor.eval(factor_states[factor], data)
    425             if factor in cat_sniffers or guess_categorical(value):
    426                 if factor not in cat_sniffers:

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/eval.pyc in eval(self, memorize_state, data)
    483     #    http://nedbatchelder.com/blog/200711/rethrowing_exceptions_in_python.html
    484     def eval(self, memorize_state, data):
--> 485         return self._eval(memorize_state["eval_code"], memorize_state, data)
    486 
    487 def test_EvalFactor_basics():

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/eval.pyc in _eval(self, code, memorize_state, data)
    466                                  self,
    467                                  self._eval_env.eval,
--> 468                                  code, inner_namespace=inner_namespace)
    469 
    470     def memorize_chunk(self, state, which_pass, data):

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/compat.pyc in call_and_wrap_exc(msg, origin, f, *args, **kwargs)
    115 def call_and_wrap_exc(msg, origin, f, *args, **kwargs):
    116     try:
--> 117         return f(*args, **kwargs)
    118     except Exception as e:
    119         if sys.version_info[0] >= 3:

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/eval.pyc in eval(self, expr, source_name, inner_namespace)
    125         code = compile(expr, source_name, "eval", self.flags, False)
    126         return eval(code, {}, VarLookupDict([inner_namespace]
--> 127                                             + self._namespaces))
    128 
    129     @classmethod

<string> in <module>()

NameError: name 'adj_opening_gross' is not defined

Unfortunately, our regressions above didn't give us significant results, meaning that there might not a strong relationship between our valance variables and gross box office success.

Now, we attempt to fit a logistic regression on our data to see if we'll get a better result.


In [69]:
logit_model = logit('adj_gross_bin ~ abs_valence_avg',dftouse).fit()
print logit_model.summary()


Optimization terminated successfully.
         Current function value: 0.620079
         Iterations 5
                           Logit Regression Results                           
==============================================================================
Dep. Variable:          adj_gross_bin   No. Observations:                  331
Model:                          Logit   Df Residuals:                      329
Method:                           MLE   Df Model:                            1
Date:                Sun, 06 Dec 2015   Pseudo R-squ.:                 0.01112
Time:                        21:28:47   Log-Likelihood:                -205.25
converged:                       True   LL-Null:                       -207.55
                                        LLR p-value:                   0.03168
===================================================================================
                      coef    std err          z      P>|z|      [95.0% Conf. Int.]
-----------------------------------------------------------------------------------
Intercept          -0.1770      0.292     -0.605      0.545        -0.750     0.396
abs_valence_avg    -2.8592      1.357     -2.107      0.035        -5.518    -0.200
===================================================================================

Clearly not the way to go.

Checking for Hidden Clusters in the Data

Looking at the regression results above, we can see our valence data is not strongly correlated with gross box office numbers. However, this does not imply that there is not a relationship between movie reviews and box office success. It is certainly possible that there are different underlying structures for various groups of movies in the world.

A neat way to discover these structures is to implement a Clustering Algorithm. Using the sklearn.mixture.GMM, we can determine if there are underlying Gaussian distributions in the population that is creating the patches of data we see in our sample. Thus, we can determine which subsets of our data we should be running our regressions on.


In [23]:
import gensim

In [24]:
from sklearn.feature_extraction.text import CountVectorizer
#credit: Lab10
vectorizer = CountVectorizer(min_df=1, stop_words='english')
X=vectorizer.fit_transform(valence_df.text)

corpus=vectorizer.get_feature_names()
id2words = dict((v, k) for k, v in vectorizer.vocabulary_.iteritems())
corpus_gensim = gensim.matutils.Sparse2Corpus(X, documents_columns=False)

lda = gensim.models.ldamodel.LdaModel(corpus_gensim, id2word=id2words, num_topics=2, update_every=1, chunksize=2000, passes=1)

lda.print_topics()


---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-24-4738c0f1329c> in <module>()
      2 #credit: Lab10
      3 vectorizer = CountVectorizer(min_df=1, stop_words='english')
----> 4 X=vectorizer.fit_transform(valence_df.text)
      5 
      6 corpus=vectorizer.get_feature_names()

NameError: name 'valence_df' is not defined

In [25]:
Xall=dftouse[['valence_sum', 'adj_gross']].values
from sklearn.mixture import GMM
n_clusters=2
clfgmm = GMM(n_components=n_clusters, covariance_type="tied")
clfgmm.fit(Xall)
print clfgmm
gmm_means=clfgmm.means_
gmm_covar=clfgmm.covars_
print gmm_means, gmm_covar


---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-25-91e7a3e626ac> in <module>()
----> 1 Xall=dftouse[['valence_sum', 'adj_gross']].values
      2 from sklearn.mixture import GMM
      3 n_clusters=2
      4 clfgmm = GMM(n_components=n_clusters, covariance_type="tied")
      5 clfgmm.fit(Xall)

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
   1789         if isinstance(key, (Series, np.ndarray, Index, list)):
   1790             # either boolean or fancy integer index
-> 1791             return self._getitem_array(key)
   1792         elif isinstance(key, DataFrame):
   1793             return self._getitem_frame(key)

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_array(self, key)
   1833             return self.take(indexer, axis=0, convert=False)
   1834         else:
-> 1835             indexer = self.ix._convert_to_indexer(key, axis=1)
   1836             return self.take(indexer, axis=1, convert=True)
   1837 

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis, is_setter)
   1110                 mask = check == -1
   1111                 if mask.any():
-> 1112                     raise KeyError('%s not in index' % objarr[mask])
   1113 
   1114                 return _values_from_object(indexer)

KeyError: "['adj_gross'] not in index"

In [26]:
from scipy import linalg

def plot_ellipse(splot, mean, cov, color):
    v, w = linalg.eigh(cov)
    u = w[0] / linalg.norm(w[0])
    angle = np.arctan(u[1] / u[0])
    angle = 180 * angle / np.pi  # convert to degrees
    # filled Gaussian at 2 standard deviation
    ell = mpl.patches.Ellipse(mean, 2 * v[0] ** 0.5, 2 * v[1] ** 0.5,
                              180 + angle, color=color, lw=3, fill=False)
    ell.set_clip_box(splot.bbox)
    ell1 = mpl.patches.Ellipse(mean, 1 * v[0] ** 0.5, 1 * v[1] ** 0.5,
                              180 + angle, color=color, lw=3, fill=False)
    ell1.set_clip_box(splot.bbox)
    ell3 = mpl.patches.Ellipse(mean, 3 * v[0] ** 0.5, 3 * v[1] ** 0.5,
                              180 + angle, color=color, lw=3, fill=False)
    ell3.set_clip_box(splot.bbox)
    #ell.set_alpha(0.2)
    splot.add_artist(ell)
    splot.add_artist(ell1)
    splot.add_artist(ell3)

In [75]:
plt.figure()
ax=plt.gca()
plot_ellipse(ax, gmm_means[0], gmm_covar, 'k')
plot_ellipse(ax, gmm_means[1], gmm_covar, 'k')
gmm_labels=clfgmm.predict(Xall)
for k, col in zip(range(n_clusters), ['blue','red']):
    my_members = gmm_labels == k
    ax.plot(Xall[my_members, 0], Xall[my_members, 1], 'w',
            markerfacecolor=col, marker='.', alpha=0.5)


This gives us a good approximation of two distinct clusters in our data. Basically, the movies that make more than 500 million dollars in total adjusted gross belongs to the red cluster, and the rest (which are the signifcant majority of the movies) belong to the blue cluster, so we should take this into account when doing further analysis.

Let's try to see if we would get a better result with 3 clusters:


In [76]:
n_clusters=3
clfgmm3 = GMM(n_components=n_clusters, covariance_type="tied")
clfgmm3.fit(Xall)
print clfgmm
gmm_means=clfgmm3.means_
gmm_covar=clfgmm3.covars_
print gmm_means, gmm_covar
plt.figure()
ax=plt.gca()
plot_ellipse(ax, gmm_means[0], gmm_covar, 'k')
plot_ellipse(ax, gmm_means[1], gmm_covar, 'k')
plot_ellipse(ax, gmm_means[2], gmm_covar, 'k')
gmm_labels=clfgmm3.predict(Xall)
for k, col in zip(range(n_clusters), ['blue','red', 'green']):
    my_members = gmm_labels == k
    ax.plot(Xall[my_members, 0], Xall[my_members, 1], 'w',
            markerfacecolor=col, marker='.', alpha=0.5)


GMM(covariance_type='tied', init_params='wmc', min_covar=0.001,
  n_components=2, n_init=1, n_iter=100, params='wmc', random_state=None,
  thresh=None, tol=0.001)
[[  2.18593152e+01   7.36447081e+07]
 [  3.08921900e+01   8.14926414e+08]
 [  2.45745724e+01   1.36629515e+08]] [[  1.77916251e+02   7.15394460e+07]
 [  7.15394460e+07   6.26401705e+15]]

Since there is so much overlap between the lower two clusters this time, we see that clustering into 2 groups is the better choice for our data. (We have also tried more clusters, but they similarly give worse results than the 2-clusters version)

Bag of Words

First import our dataset & dictionary


In [147]:
# with open("IMDB_dftouse_dict.json", "r") as fd:
#     IMDB_dftouse_dict = json.load(fd)
# maas_IMDB = pd.DataFrame(IMDB_dftouse_dict)

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

In [148]:
maas_IMDB.head()


Out[148]:
movie_id movie_name n_scorables pct_scorables positive stars text url valence_avg valence_sum
0 10027 Titanic 120 0.666667 1 7 Sure, Titanic was a good movie, the first time... http://www.imdb.com/title/tt0120338/usercommen... 0.479760 38.380826
1 10028 Titanic 65 0.538462 1 10 When I saw this movie I was stunned by what a ... http://www.imdb.com/title/tt0120338/usercommen... 0.508760 17.806611
10 10037 Titanic 95 0.589474 1 9 The ship may have sunk but the movie didn't!!!... http://www.imdb.com/title/tt0120338/usercommen... 0.700832 39.246578
100 11015 Cliffhanger 84 0.500000 1 9 This movie is directed by Renny Harlin the fin... http://www.imdb.com/title/tt0106582/usercommen... 0.794284 33.359933
1000 10544 The First Power 194 0.618557 0 4 When childhood memory tells you this was a sca... http://www.imdb.com/title/tt0099578/usercommen... 0.132427 15.891239

In [149]:
maas_IMDB.shape


Out[149]:
(4111, 10)

In [150]:
with open("happiness.json", "r") as fp:
    happiness = json.load(fp)

In [151]:
len(happiness)


Out[151]:
10222

Now we begin the Natural Language processing

This will take the form of a few steps.

  1. Punctuation: we will remove punctuation from reviews, so that all words can be recognized
  2. Lemmatization: we will reduce words to lemmas
  3. 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

  1. For each review create a list of nouns, and a list of adjectives
  2. 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
  3. Run the sentiment analysis on the adjectives (N.B. At this point it is still not on an external dataset)
  4. Putting the topics and sentiment analysis together (marrying the nouns & the adjectives)
  5. Estimate the overall mean for each movie on each topic

In [152]:
from pattern.en import parse
from pattern.en import pprint
from pattern.vector import stem, PORTER, LEMMA
punctuation = list('.,;:!?()[]{}`''\"@#$^&*+-|=~_')

In [153]:
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 [154]:
import re
regex1=re.compile(r"\.{2,}")
regex2=re.compile(r"\-{2,}")

In [155]:
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 [42]:
%%time
# review_parts = maas_IMDB.map(lambda x: get_parts(x.text))
review_parts = []
for index, row in maas_IMDB.iterrows():
    review_parts.append(get_parts(row.text))


---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-42-0e46d096a063> in <module>()
----> 1 get_ipython().run_cell_magic(u'time', u'', u'# review_parts = maas_IMDB.map(lambda x: get_parts(x.text))\nreview_parts = []\nfor index, row in maas_IMDB.iterrows():\n    review_parts.append(get_parts(row.text))')

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_cell_magic(self, magic_name, line, cell)
   2262             magic_arg_s = self.var_expand(line, stack_depth)
   2263             with self.builtin_trap:
-> 2264                 result = fn(magic_arg_s, cell)
   2265             return result
   2266 

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k)
    191     # but it's overkill for just that one bit of state.
    192     def magic_deco(arg):
--> 193         call = lambda f, *a, **k: f(*a, **k)
    194 
    195         if callable(arg):

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)
   1164         else:
   1165             st = clock2()
-> 1166             exec(code, glob, local_ns)
   1167             end = clock2()
   1168             out = None

<timed exec> in <module>()

NameError: name 'maas_IMDB' is not defined

In [157]:
review_parts[1]


Out[157]:
([[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 [158]:
ldadata = [ele[0] for ele in review_parts]

In [159]:
ldadata


Out[159]:
[[[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'],
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   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 [160]:
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 [161]:
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 [162]:
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 [163]:
id2word[0], vocab.keys()[5], vocab[vocab.keys()[5]]


Out[163]:
(u'sure', u'yellow', 5224)

In [164]:
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 [165]:
all_reviews = []
for i in ldadata: # into a review
    this_review = []
    for j in i:
        for k in j: 
            if k not in this_review: 
                this_review.append(k)
    all_reviews.append(this_review)

corpus = []
for i in all_reviews: 
    corpus.append(auxillary_function(i, vocab))

In [166]:
corpus


Out[166]:
[[(0, 1),
  (1, 1),
  (2, 1),
  (3, 1),
  (4, 1),
  (5, 1),
  (6, 1),
  (7, 1),
  (8, 1),
  (9, 1),
  (10, 1),
  (11, 1),
  (12, 1),
  (13, 1),
  (14, 1),
  (15, 1),
  (16, 1),
  (17, 1),
  (18, 1),
  (19, 1),
  (20, 1),
  (21, 1)],
 [(1, 1),
  (10, 1),
  (22, 1),
  (23, 1),
  (24, 1),
  (25, 1),
  (26, 1),
  (27, 1),
  (28, 1)],
 [(1, 1),
  (29, 1),
  (30, 1),
  (31, 1),
  (32, 1),
  (33, 1),
  (34, 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),
  (47, 1),
  (48, 1),
  (49, 1),
  (50, 1)],
 [(64, 1),
  (1, 1),
  (66, 1),
  (67, 1),
  (4, 1),
  (69, 1),
  (68, 1),
  (65, 1),
  (51, 1),
  (52, 1),
  (53, 1),
  (54, 1),
  (55, 1),
  (56, 1),
  (57, 1),
  (58, 1),
  (59, 1),
  (60, 1),
  (61, 1),
  (62, 1),
  (63, 1)],
 [(1, 1),
  (2, 1),
  (25, 1),
  (30, 1),
  (48, 1),
  (55, 1),
  (61, 1),
  (70, 1),
  (71, 1),
  (72, 1),
  (73, 1),
  (74, 1),
  (75, 1),
  (76, 1),
  (77, 1),
  (78, 1),
  (79, 1),
  (80, 1),
  (81, 1),
  (82, 1),
  (83, 1),
  (84, 1),
  (85, 1),
  (86, 1),
  (87, 1),
  (88, 1),
  (89, 1),
  (90, 1),
  (91, 1),
  (92, 1),
  (93, 1),
  (94, 1),
  (95, 1),
  (96, 1),
  (97, 1),
  (98, 1),
  (99, 1),
  (100, 1),
  (101, 1),
  (102, 1),
  (103, 1),
  (104, 1),
  (105, 1),
  (106, 1),
  (107, 1),
  (108, 1),
  (109, 1),
  (110, 1),
  (111, 1),
  (112, 1),
  (113, 1),
  (114, 1)],
 [(1,