Using Twitter data to predict the performance of Bollywood movies

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 ...

[1]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[2]  Andreas Georgiou.  Are TV ratings possible with Twitter , 2013 .

[3]  Ramanathan V. Guha,et al.  The predictive power of online chatter , 2005, KDD '05.

[4]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .

[5]  Chien-Chung Chan,et al.  Prediction of movies box office performance using social media , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[6]  Patrick Paroubek,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.

[7]  Eric W. T. Ngai,et al.  Social media models, technologies, and applications: An academic review and case study , 2015, Ind. Manag. Data Syst..

[8]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[9]  Prashant Kumar Singh,et al.  Tracing Information Flow and Analyzing the Effects of Incomplete Data in Social Media , 2012, 2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks.

[10]  Xiaohui Yu,et al.  ARSA: a sentiment-aware model for predicting sales performance using blogs , 2007, SIGIR.

[11]  Ajay Siva Santosh Reddy,et al.  Box-Office Opening Prediction of Movies based on Hype Analysis through Data Mining , 2012 .

[12]  Angelika Dimoka,et al.  The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation , 2006, Inf. Syst. Res..

[13]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

[14]  Ok Ran Jeong,et al.  SNS Information Extraction for Social Search , 2013, 2013 International Conference on Information Science and Applications (ICISA).

[15]  Brendan T. O'Connor,et al.  From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.

[16]  Gilad Mishne,et al.  Leave a Reply: An Analysis of Weblog Comments , 2006 .

[17]  G. Anuradha,et al.  S-ANFIS: Sentiment aware adaptive network-based fuzzy inference system for Predicting Sales Performance using Blogs/Reviews , 2012 .

[18]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, Web Intelligence.

[19]  David A. Broniatowski.  Extracting social values and group identities from social media text data , 2012, 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP).

[20]  Jure Leskovec,et al.  Social media analytics: tracking, modeling and predicting the flow of information through networks , 2011, WWW.

[21]  Sungzoon Cho,et al.  Box-office forecasting based on sentiments of movie reviews and Independent subspace method , 2016, Inf. Sci..

[22]  Yongzheng Zhang,et al.  Predicting purchase behaviors from social media , 2013, WWW.

[23]  Xiangji Huang,et al.  Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain , 2012, IEEE Transactions on Knowledge and Data Engineering.

[24]  Zhenyu Yang,et al.  Sentiment analysis on tweets for social events , 2013, Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[25]  M. Tsagkias,et al.  Mining social media: tracking content and predicting behavior , 2012 .

[26]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[27]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[28]  K. Charalampidou.  Estimating popularity by sentiment and polarization classification on social media , 2012 .

[29]  Ee-Peng Lim,et al.  Tweets and Votes: A Study of the 2011 Singapore General Election , 2012, 2012 45th Hawaii International Conference on System Sciences.