{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:11:08Z","timestamp":1762506668679,"version":"3.41.2"},"reference-count":44,"publisher":"Emerald","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,10,19]]},"abstract":"<jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>\u2013 The purpose of this paper is to address the shortcomings of limited research in forecasting the power of social media in India.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design\/methodology\/approach<\/jats:title>\n                  <jats:p>\u2013 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>\u2013 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Research limitations\/implications<\/jats:title>\n                  <jats:p>\u2013 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Originality\/value<\/jats:title>\n                  <jats:p>\u2013 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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1108\/imds-04-2015-0145","type":"journal-article","created":{"date-parts":[[2015,10,22]],"date-time":"2015-10-22T13:05:38Z","timestamp":1445519138000},"page":"1604-1621","source":"Crossref","is-referenced-by-count":45,"title":["Using Twitter data to predict the performance of Bollywood movies"],"prefix":"10.1108","volume":"115","author":[{"given":"Dipak Damodar","family":"Gaikar","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Thakur College of Engineering and Technology, Mumbai, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bijith","family":"Marakarkandy","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Thakur college of engineering and Technology, Mumbai, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chandan","family":"Dasgupta","sequence":"additional","affiliation":[{"name":"SBM, NMIMS University, Mumbai, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"2025072819433292400_b3","doi-asserted-by":"crossref","unstructured":"Asur, S.\n           and Huberman, B.A. (2010), \u201cPredicting the future with social media\u201d, IEEE\/WIC\/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Vol. 1, pp. 492-499.","DOI":"10.1109\/WI-IAT.2010.63"},{"key":"2025072819433292400_b4","doi-asserted-by":"crossref","unstructured":"Bindra, G.S.\n          , Kandwal, K.K., Singh, P.K. and Khanna, S. (2012), \u201cTracing information flow and analyzing the effects of incomplete data in social media\u201d, IEEE Fourth International Conference, pp. 235-240.","DOI":"10.1109\/CICSyN.2012.51"},{"key":"2025072819433292400_b5","doi-asserted-by":"crossref","unstructured":"Bollen, J.\n          , Mao, H. and Zeng, X.J. (2010), \u201cTwitter mood predicts the stock market\u201d, 1010.3003, Conference on Artificial Intelligence, October, pp. 1-8.","DOI":"10.1016\/j.jocs.2010.12.007"},{"key":"2025072819433292400_b7","unstructured":"Brook, D.\n           (2006), \u201cOnline database websites for movies, television, and video games\u201d, available at: www.imdb.com (accessed April 5, 2014)."},{"key":"2025072819433292400_b8","unstructured":"Charalampidou, K.\n           (2012), \u201cEstimating popularity by sentiment and polarization classification on social media\u201d, doctoral dissertation, TU Delft, Delft University of Technology, Delft."},{"key":"2025072819433292400_b13","doi-asserted-by":"crossref","unstructured":"Gruhl, D.\n          , Guha, R., Kumar, R., Novak, J. and Tomkins, A. (2005), \u201cThe predictive power of online chatter\u201d, Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 78-87.","DOI":"10.1145\/1081870.1081883"},{"key":"2025072819433292400_b14","doi-asserted-by":"crossref","unstructured":"Hodeghatta, U.R.\n           (2013), \u201cSentiment analysis of Hollywood movies on Twitter\u201d, IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1401-1404.","DOI":"10.1145\/2492517.2500290"},{"key":"2025072819433292400_b15","unstructured":"Hofmann, T.\n           (1999), \u201cProbabilistic latent semantic analysis\u201d, Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 289-296."},{"key":"2025072819433292400_b18","doi-asserted-by":"crossref","unstructured":"Leskovec, J.\n           (2011), \u201cSocial media analytics: tracking, modeling and predicting the flow of information through networks\u201d, ACM 22nd International Conference on World Wide Web, pp. 277-228.","DOI":"10.1145\/1963192.1963309"},{"key":"2025072819433292400_b21","unstructured":"MATHWORKS\n           (2014), \u201cFuzzy logic toolbox: building a fuzzy inference system\u201d, the Math Works Inc.\u201d, Natick, MA, available at: http:\/\/in.mathworks.com\/products\/fuzzy-logic (accessed April 20, 2013)."},{"key":"2025072819433292400_b24","unstructured":"Mishne, G.\n           and Glance, N. (2006), \u201cLeave a reply: an analysis of weblog comments\u201d, third annual workshop on the Weblogging Ecosystem, Edinburgh, May 22-26."},{"key":"2025072819433292400_b25","unstructured":"Nassirpour, S.\n          , Zargham, P. and Mahalati, R.N. (2012), \u201cElectronic devices sales prediction using social media sentiment analysis\u201d."},{"key":"2025072819433292400_b27","doi-asserted-by":"crossref","unstructured":"O\u2019Connor, B.\n          , Balasubramanyan, R., Routledge, B.R. and Smith, N.A. (2010), \u201cFrom tweets to polls: linking text sentiment to public opinion\u201d,                   Time Series. ICWSM                , Vol. 11, pp. 122-129.","DOI":"10.1609\/icwsm.v4i1.14031"},{"key":"2025072819433292400_b28","unstructured":"Pak, A.\n           and Paroubek, P. (2010), \u201cTwitter as a corpus for sentiment analysis and opinion mining\u201d, Proceedings of LREC, pp. 1320-1326."},{"issue":"4","key":"2025072819433292400_b151","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1287\/isre.1060.0106","article-title":"The nature and role of feedback text comments in online marketplaces: implications for trust building, price premiums, and seller differentiation","volume":"17","author":"","year":"2006","journal-title":"Information Systems Research"},{"issue":"1","key":"2025072819433292400_b30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5120\/8852-2794","article-title":"Box-office opening prediction of movies based on hype analysis through data mining","volume":"56","author":"","year":"2012","journal-title":"International Journal of Computer Applications"},{"key":"2025072819433292400_b32","doi-asserted-by":"crossref","unstructured":"Skoric, M.\n          , Poor, N., Achananuparp, P., Lim, E.P. and Jiang, J. 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