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Litman BR, Kohl LS (1989) Predicting financial success of motion pictures: The’80s experience.
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Jungherr A, Jürgens P, Schoen H (2012) Why the pirate party won the German election of 2009 or the trouble with predictions: a response to tumasjan, a., sprenger, to, sander, pg, & welpe, im “predicting elections with twitter: what 140 characters reveal about political sentiment”. Association for Computational Linguistics, 293–296
MOVIES ABOUT ARTIFICIAL INTELLIGENCE AND SOCIAL MEDIA MOVIE
(2010) Movie reviews and revenues: An experiment in text regression //Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Joachims T (1999) Making large scale SVM learning practical Jansen BJ, Zhang M, Sobel K et al (2009) Twitter power: tweets as electronic word of mouth. Canadian Journal of Communication, 30 (4) (2006) Pundits, ideologues, and the ranters: The British Columbia election online. (2005) The predictive power of online chatter //Proceedings of the eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. Limits of electoral predictions using twitter // ICWSM. Gayo-Avello D, Metaxas P T, Mustafaraj E. J Adv neural inf Process Syst 9:155–161įriedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. ACM, 231–240.ĭrucker H, Burges CJC, Kaufman L et al (1997) Support vector regression machines. (2008) A holistic lexicon-based approach to opinion mining //Proceedings of the 2008 International Conference on Web Search and Data Mining. Educ Psychol Meas 20(1):37–46ĭing X, Liu B, Yu P S. University of Washington, SeattleĬohen J (1960) A coefficient of agreement for nominal scales. IEEE 112c-112cĬhen A (2002) Forecasting gross revenues at the movie box office. Proceedings of the 38th Annual Hawaii International Conference on. (2005) Movie review mining: A comparison between supervised and unsupervised classification approaches //System Sciences, 2005. (2010) Using Social Media to Predict Future Events with Agent-Based Markets. ACM, 144–152īothos E., Apostolou D., Mentzas G. (1992) A training algorithm for optimal margin classifiers // Proceedings of the fifth annual workshop on Computational learning theory. J Comput Sci 2(1):1–8īoser B E, Guyon I M, Vapnik V N. IEEE/WIC/ACM international conference on IEEE 1:492–499īollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. Both the linear and non-linear prediction models have the advantage of predicting movie grosses in our experiments.Īsur S, Huberman BA (2010) Predicting the future with social media //Web intelligence and intelligent agent technology (WI-IAT), 2010. The experimental results show that large-scale social media content is correlated with movie box-office revenues and that the purchase intention of users can lead to more accurate movie box-office revenue predictions.
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To evaluate the effectiveness of the proposed approach, a cross-validation experiment is conducted. In our model, the use of Linear Regression and Support Vector Regression in predicting the box-office revenue of a movie before its theatrical release is explored. More specifically, the attention and popularity of the movie, purchase intention of users, and comments of users are automatically mined from social media data. In this study, we employ both linear and non-linear regression models, which are based on the crowd wisdom of social media, especially the posts of users, to predict movie box-office revenues. Nowadays, social media has shown its predictive power in various domains, which motivates us to exploit social media content to predict box-office revenues. Predicting the box-office revenue of a movie before its theatrical release is an important but challenging problem that requires a high level of Artificial Intelligence.