Presentation on Seminar Topic

PREDICTION OF BOX OFFICE SUCCESS OF MOVIES USING HYPE

ANALYSIS OF TWITTER DATA

By

SAMEER THIGALE

TUSHAR PRASAD

USTAT KAUR

VIBHA RAVICHANDRAN

Guided ByPROF. REENA PAGARE

Sponsored ByPERSISTENT SYSTEMS LIMITED

124 November 2014

A BRIEF OUTLINE

• PRESENCE OF “RICH INSIGHTS” IN SOCIAL NETWORKS

• IDEA OF PREDICTING BOX OFFICE SUCCESS OF MOVIES

• PRE-RELEASE HYPE- A SUCCESS FACTOR

AGENDA

• LITERATURE SURVEY• PROBLEM STATEMENT• MODEL EMPLOYED• BLOCK DIAGRAM• ACTIVITY DIAGRAM• PLATFORM AND TECHNOLOGY• LIMITATIONS• FUTURE SCOPE• FEASIBILITY ASSESSMENT• MATHEMATICAL MODEL• FUNCTIONAL POINT ANALYSIS• PROJECT PLAN AND INDIVIDUAL CONTRIBUTION• CONCLUSION• REFERENCES

LITERATURE SURVEY

• CURRENT SCENARIO IN MOVIE INDUSTRY

• FORECASTING METHODS EMPLOYED

– QUANTITATIVE

• TIME SERIES / EXPLANATORY

– QUALITATIVE

– UNPREDICTABLE

• SOCIAL MEDIA: A KNOWLEDGE REPOSITORY

– HYPE ANALYSIS

• SENTIMENT ANALYSIS

REFERENCE DESCRIPTION

FORECASTING-Methods and Applications by-Spyros M., Steven W., Rob H

We studied the Model used for forecasting and used for some cases from this reference. We are here using the regression model for better accuracy and efficiency.They are many other models such as weighted average model which might be a less efficient as in case of results

Predicting the future with social media- S Asur, B Huberman, HP Labs, Hp Journal,January 2012

From this paper we analyzed the variousfactors that could be considered for calculating the success rate. The factors may be hype,Distribution,Cast,Budget, Type of film etc.. We also Analyzed the concept of sentiment analyses from the same

Box-Office opening prediction of Movies based on Hype Analysis through Data Mining-A.Reddy,St.Francis Institute of TechnologyInternational Journal of Computer Application,October 2012

From this paper we studied the calculation of the hype factor and gathering tweets from twitter.

PROBLEM STATEMENT

• To demonstrate how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies.

• We further demonstrate how sentiments extracted from Twitter can be further utilized to improve the forecasting power of social media.

TERMS AFFINIATED WITH #MARYKOM

MODEL EMPLOYED

• MULTIPLE LINEAR REGRESSION

– WITH TIME SERIES REGRESSION

• The regression coefficients are calculated using partial differentiation and by using the particular data set available

Y = ßaA + ßpP + ßdD + ßbB + ßeE + ßsS + e

REGRESSION COEFFFICIENTS

ATTENTION SEEKING POLARITYHEATNESS

ERROR FACTORCATEGORY STAR CAST

SEQUEL

MODEL EMPLOYED

A

P CALCULATED USING SENTIMENT ANALYSIS

D FOLLOWER COUNT-T/FOLLOWER COUNTT=AVG NO OF FOLLOWERS PER ALL USERS WHO TWEETED

B CATEGORY OF MOVIE- ACTION, THRILLER, COMEDY, SCI-FI, ANIMATION, 3-D, ROMANCE

E STAR CAST- INPUT THROUGH USER

S SEQUEL FACTOR

e ERROR FACTOR

BLOCK DIAGRAM

SERVER SIDE ACTIVITY DIAGRAM

PLATFORM AND TECHNOLOGY

PLATFORM-UBUNTU

TECHNOLOGY-Java-SENTIMENT ANALYSER- SENTIWORD, LINGPIPE-TWITTER API-OAUTH 2.0

HARDWARE-COMMODITY HARDWARE-SERVER

APPLICATION SOFTWARES-ECLIPSE-MYSQL SERVERAPACHE TOMCAT-TWITTER4J

PLATFORM USED

• Linux-open source

• MySQL-optimized database for web based applications

• Twitter4j-for accessing tweets through twitter

API

• Sentimental Analyzer-Sentinet

• Oauth 2.0-for authorization

FEATURES & APPLICATIONS

• FORECAST MOVIE SUCCESS RATE

• ESTIMATE REVENUE FROM MOVIE

• COMPARE MOVIES– SCHEDULING

• HYPE ANALYSIS

• EFFECT OF PUBLIC HOLIDAYS ON SUCCESS

• Heatmap- pleasureness/hypeness of tweets

• Twitter affinity-which tweets are interrelated

LIMITATIONS

• FORECASTING ACCURACY IMPROVES OVER TIME

• TWITTER LIMITATIONS

– IT’S CONSIDERED A NEWS NETWORK

FUTURE SCOPE

• TWEETS IN OTHER LANGUAGES CAN BE TAKEN INTO ACCOUNT WITH TRANSLATORS

• SENTIMENTS FROM FACEBOOK AND OTHER SITES CAN BE ADDED

MATHEMATICAL MODEL

Let S be the system:S = {S, AP, A, P, D, B, Q, E, DB, En, C, Y, Er |

f1,f2,f3,f4,f5}

S-Server AP-ApplicationA-Attention Seeking Factor P-PolarityD-Distribution Factor B-BudgetQ-Star Cast E-SequelDB-Database for movie C- CATEGORYY-Regression Output Er-Error Factor

MATHEMATICAL MODEL

SET THEORY:A={A1,A2…An}A is the attention seeking factorA=(R,S,T)R=rate of tweetsS=seasonal VariablesT=time specified

P={P1,P2…Pn}P is the PolarityP=(Pos, Neg, Neu)Pos=Positive TweetNeg=Negative TweetNeu=Neutral Tweet

D={D1,D2..Dn]D is the Distribution AreaD=(f, t)f=follower countt=Average no of followers per all users who tweeted

MATHEMATICAL MODEL

Sr.no Function Description

1 f1(AP)->S Function invoked by AP to send request to server

2 f2(S)->DB Function invoked by S to fetch information from Database

3. f3(En)->Y Function invoked by Engine to calculate the output through regression at certain interval of time

4. f4(S)->AP Function invoked by S to display response output to the AP

5. f5(AP)->DB Function invoked to store the results in the database

Following Functions can be mapped onto the elements of the set

MATHEMATICAL MODELMATHEMATICAL MODEL

Sr.no Function Mapping of the Function

1 f1(AP)->S One-to-one

2 f2(S)->DB One- to-many

3. f3(En)->Y One-to-many

4. f4(S)->AP One-to-one

5. f5(AP)->DB One-to-many

MAPPINGS:

Failure Condition:The Application works on the inputs from the user. Thus allocation of resources is animportant factor. The integrity of data maintained by the system is of utmostimportance and the set of these parameters should be mutually exclusive. This willensure that no resource entity is left unaccounted for. If sufficient no of tweets arenot available then the accuracy may be hampered.

FUNCTION POINT ANALYSIS

EXTERNAL INPUTS •Movie Details(Movie name, Release Date, Budget, Star Cast)•User Details(Stakeholder)

EXTERNAL OUTPUTS •Revenue of movies Predicted•Success Rate of the movies

EXTERNAL INQUIRY •Information about Past Transaction •Comparison of Movies

INTERNAL LOGICAL FILES •User log File•User Command regarding Analysis

EXTERNAL INTERFACE FILES •Regression coefficients•Twitter Data•Polarity and other Parameters like Hype, Distribution Factor.

PROJECT PLAN

Group formation, Search and

Finalization , Guide allocation

Literature survey, Group discussion

basic study

Development of Mathematical moddel and

development of UML and project

plan

Refinement, Delivery,

Documentation,Feedback

Coding, Testing, Deployment and

presentation of topic at seminars

Deployment

Planning

Modeling

Construction

Communication

JanAug Oct DecNovSeptJuly

FebMar

May

INDIVIDUAL CONTRIBUTION

GROUP MEMBER NAME WORK DONE

SAMEER THIGALE TWITTER DATA ANALYSIS, OAUTH, UML

TUSHAR PRASAD SENTIMENT ANALYSIS, PLATFORM SURVEY

USTAT KAUR LITERATURE SURVEY, SRS

VIBHA RAVICHANDRAN FORECASTING MODEL, PROJECT PLAN, FPA

CONCLUSION

• In this project we have shown how social media can beutilized to forecast future outcomes.

• Specifically, using the rate of chatter from tweets fromthe popular site Twitter, we constructed a multiplelinear regression model for predicting box-officerevenues of movies in advance of their release.

• At a deeper level, this work shows how social media expresses a collective wisdom which, when properly tapped, can yield an extremely powerful and accurate indicator of future outcomes.

REFERENCES

[1] FORECASTING-Methods and Applications by-Spyros M., Steven W., Rob H.

[2] Predicting the future with social media- S Asur, B Huberman, HP Labs, Hp Journal,January 2012

[3]Box-Office opening prediction of Movies based on Hype Analysis through Data Mining-A.Reddy,St.Francis Institute of Technology

International Journal of Computer Application,October 2012

THANK YOU!

25