Using Twitter data to predict the performance of Bollywood movies. Issue 9 (19th October 2015)

• Record Type: Journal Article
• Authors: Gaikar, Dipak Damodar; Marakarkandy, Bijith; Dasgupta, Chandan
• Editors: Xiaojun Wang, Professor Leroy White and Professor Xu Chen, Dr
• Is Part Of: Industrial management & data systems. Volume 115:Issue 9(2015)
• Page Start: 1604
• Page End: 1621
• Publication Date: 2015-10-19
• Subjects: Prediction -- Sentiment analysis -- Twitter -- Social media -- Social network -- Fuzzy inference system; Industrial management -- Periodicals; Electronic data processing -- Periodicals; Business -- Periodicals; Industrial management -- Great Britain -- Periodicals; 658.05
• DOI: 10.1108/IMDS-04-2015-0145
• Languages: English
• ISSNs: 0263-5577

Abstract

Abstract : Purpose: – The purpose of this paper is to address the shortcomings of limited research in forecasting the power of social media in India. Design/methodology/approach: – This paper uses sentiment analysis and prediction algorithms to analyze the performance of Indian movies based on data obtained from social media sites. The authors used Twitter4j Java API for extracting the tweets through authenticating connection with Twitter web sites and stored the extracted data in MySQL database and used the data for sentiment analysis. To perform sentiment analysis of Twitter data, the Probabilistic Latent Semantic Analysis classification model is used to find the sentiment score in the form of positive, negative and neutral. The data mining algorithm Fuzzy Inference System is used to implement sentiment analysis and predict movie performance that is classified into three categories: hit, flop and average. Findings: – In this study the authors found results of movie performance at the box office, which had been based on fuzzy interface system algorithm for prediction. The fuzzy interface system contains two factors, namely, sentiment score and actor rating to get the accurate result. By calculation of opening weekend collection, the authors found that that the predicted values were approximately same as the actual values. For the movie Singham Returns over method of prediction gave a box office collection as 84 crores and the actual collection turned out to be 88 crores. Research limitations/implications: – The current study suffers from the limitation of not having enough computing resources to crawl the data. For predicting box office collection, there is no correct availability of ticket price information, total number of seats per screen and total number of shows per day on all screens. In the future work the authors can add several other inputs like budget of movie, Central Board of Film Certification rating, movie genre, target audience that will improve the accuracy and quality of the prediction. Originality/value: – The authors used different factors for predicting box office movie performance which had not been used in previous literature. This work is valuable for promoting of product and services of the firms.