PREDICTION OF BOX OFFICE SUCCESS OF MOVIES USING HYPE ANALYSIS OF TWITTER

DATA(PREDICTING THE FUTURE)

By

SAMEER THIGALE, TUSHAR PRASAD

MIT COLLEGE OF ENGINEERING, PUNE

Internal Guide:

PROF. REENA PAGARE

Sponsored Organization:

PERSISTENT SYSTEMS LIMITED

A BRIEF OUTLINE

• Presence of “rich insights” in

social networks

• The Hypothesis:

“A Movie Well Talked About is Well Watched”

• Pre-release buzz- a success factor

2

LITERATURE SURVEY

3

REFERENCE DESCRIPTION

[1] FORECASTING- Methods andApplications by- Spyros M., Steven W., RobH., 3rd Edition, Wiley Publication (book)

Basic concepts of statistics like correlationStudy of forecasting models.Linear regressionTime series regression

[2] Predicting the Future with Social Media-S Asur, B Huberman, HP Labs, HP Journal, Jan2012

The various factors that could be consideredfor calculating the success rate might beattention seeking, Distribution, Polarity, Typeof film etc.Prediction can be made using linearregression.

EXISTING MODELS

• HOLLYWOOD STOCKEXCHANGE (HSX.COM)

– Uses Virtual Stocks to predict revenue

– Accuracy 90%, confidence: medium

• INTERNET MOVIE DB (IMDB.COM)

– Uses clicks, reviews, blogs, star casts to predict

• BoxOfficeMojo.com

– Uses clicks, reviews, blogs, star casts to predict

4

But None of the leading movie database sites use Social Media to make predictions. Why?

PROBLEM DEFINITION

• To demonstrate that the amount of attentiona subject has, has strong correlation to itsranking in future.

• To show that a simple regression model builtfrom the Twitter chatter can outperformmarket based predictions.

• To demonstrate how the model built can alsobe extended to products of consumer interest

5

Technical Keywords:Statistical prediction, Social network analysis, Regression

THE DATASET

• 100,000+ unique users

• Dataset of 6 weeks4 million tweets

6

MOVIE NAME

Jupiter Ascending

Shamitabh

SpongeBob: Sponge out of water

LoveSick

Fifty Shades of Grey

Birdman

American Sniper

Foxcatcher

Hot Tub Time Machine 2

Chappie Movie

Badlapur

MODEL EMPLOYED

• MULTIPLE LINEAR REGRESSION

– BASED ON FINDING “A STRAIGHT LINE PREDICTING Y(INCOME)”

7

MODEL EMPLOYED

A AVG COUNT OF TWEETS PER HOUR

P CALCULATED USING SENTIMENT ANALYSISRANGE: 0 TO 4 (0: VERY NEGATIVE, 4: VERY POSITIVE)

D NUMBER OF THEATRES MOVIE IS RELEASED IN

C CATEGORY OF MOVIE:ACTION, THRILLER, COMEDY, ANIMATION, ROMANCE

E STAR CAST- DIVIDED INTO 3 CATEGORIES; DEPEND ON TWITTER FOLLOWER

S SEQUELRANGE: 0 IF NOT SEQUEL, 1 IF SEQUEL

8

CONTRIBUTION

• In our model we are using multiple linearregression for forecasting which guarantees abetter and accurate outcome rather thanusing complicated Neural Networks, patternrecognition and other AI concepts.

• Model is robust and can be extended to otherconsumer products by just changing theregression parameters.

9

DEMO

10

SYSTEM ARCHITECTURE

11

PLATFORM AND TECHNOLOGY

• OPERATING SYSTEM AND ARCHITECTURE INDEPENDENT

– TESTED ON WINDOWS XP+, UBUNTU 12.04 LTS+

– BOTH 32-BIT AND 64-BIT ARCHITECTURE

• SOFTWARE REQUIREMENTS (MINIMUM):

– JDK 8

– MYSQL 5+

12

SALIENT FEATURES• Client-server architecture

• Accurate prediction

• Displays

– Sentiment of tweets

– tag cloud of tweets

– Location of tweet

– Rate of tweets per hour

PROUDLY BUILT ON THE OPEN SOURCE MODEL. ALL OPEN-SOURCE TOOLS USED. SOFTWARE LICENSED UNDER GNU GPL. 13

RESULTS

Features R2

Avg tweet rate 0.02

Avg tweet rate + theatre count 0.91

14

Movie Name Release Date What we predicted (in USD)

What actually happened!

Fifty Shades of Grey 13-Feb-2015 80,214,910 85,043,000

Shamitabh 06-Feb-2015 243,661 241,720

Kingsman: Secret Service

13-Feb-2015 34,345,613 36,225,000

HotTubTimeMachine2

20-Feb-2015 30,255,168 ????(IMDB SAYS 25M)

APPLICATIONS

• Forecasting products of consumer interestgiven the chatter

– Movies

– Elections

– ICC World Cup

– Epidemiology (Google Flu trends)

• For theatre owners to predict the number ofshows to be scheduled

– Similarly to retailers of respective products

15

LIMITATIONS

• Data cleaning limitations– Presence of reference to two or more movies

– Presence of sarcastic tweets

– Emoticons

• CONSTRAINTS:– Due to Twitter API limitations only 1% of tweets

can be caught (Can be improved by Firehoseaccess)

– Only tweets in English language accepted

16

Such a wonderful movie #Humshakal is!

I <3 d mve #Shamitabh

FUTURE SCOPE

• Estimating from “negative hype”

– For e.g. Revenue of #PK increased due to the#PKDebate

• Correlating success of songs to success ofmovie

– Famous example of the song “Tum Hi Ho”

• Correlating “structure” of retweets and“favorited” tweets

17

THANK YOU!

18