OPEN ACCESS
Various researchers and analysts highlighted the potential of Big Data, and social networks in particular, to optimize demand forecasts in managerial decision processes in different sectors. Other authors focused the attention on the potential of Twitter data in particular to predict TV ratings. In this paper, the interactions between television audience and social networks have been analysed, especially considering Twitter data. In this experiment, about 2.5 million tweets were collected, for 14 USA TV series in a nine-week period through the use of an ad hoc crawler created for this purpose. Subsequently, tweets were classified according to their sentiment (positive, negative, neutral) using an original method based on the use of decision trees. A linear regression model was then used to analyse the data. To apply linear regression, TV series have been grouped in clusters; clustering is based on the average audience for the individual series and their coefficient of variability. The conclusions show and explain the existence of a significant relationship between audience and tweets.
Audience, Forecasting, Regression, Sentiment, TV, Twitter.
[1] Hassani, H. & Silva, E.S., Forecasting with big data: a review. Annals of Data Science, 2(1), pp. 5–19, 2015. http://dx.doi.org/10.1007/s40745-015-0029-9
[2] Marr, B., Big Data: A Game Changer in the Retail Sector, Forbes, available at http://www. forbes.com/sites/bernardmarr/2015/11/10/big-data-a-game-changer-in-the-retail-sector/, 2015.
[3] Arias, M., Arratia, A. & Xuriguera, R., Forecasting with twitter data, special issue on social web mining. ACM Transactions on Intelligent Systems and Technology (TIST), 51, 2013.
[4] O’Connor, B., Balasubramanyan, R., Routledgex, B.R. & Smith, N.A., From tweets to polls: linking text sentiment to public opinion time series. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, Washington, pp. 122–129, 2010.
[5] Zhang, X., Fuehres, H. & Gloor, P.A., Predicting stock market indicators through twitter “I hope it is not as bad as I fear”. Procedia-Social and Behavioral Sciences, 26, pp. 55–62, 2011. http://dx.doi.org/10.1016/j.sbspro.2011.10.562
[6] Wolfram, M.S.A., Modelling the stock market using twitter. M.S. Thesis, School of Informatics, University of Edinburgh, 2010.
[7] Bollen, J., Mao, H. & Zeng, X., Twitter mood predicts the stock market. Journal of Computational Science, 2(1), pp. 1–8, 2011. http://dx.doi.org/10.1016/j.jocs.2010.12.007
[8] Mishne, G. & Glance, N., Predicting movie sales from blogger sentiment. Proceedings of AAAI 2006 Spring Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), pp. 155–158, 2006.
[9] Wakamiya, S., Lee, R., Kawai, Y. & Sumiya, K., Crowd- powered TV viewing rates: measuring relevancy between tweets and TV. Database Systems for Advanced Applications, pp. 390–401, 2011.
[10] Nielsen Media Research, Must see TV: how twitter activity ahead of fall season premieres could indicate success, available at http://www.nielsen.com/us/en/insights/news/2015/must-see-tvhow-twitter-activity-ahead-of-fall-season-premieres-could-indicate-success.html, 2015.
[11] Mihanović, A., Gabelica, H. & KrstiĆ, Z., Big data and sentiment analysis using KNIME: Online Reviews vs. Social Media. Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, 2014.
[12] Wakade, S., Shekar, C., Liszka, K.J. & Chan, C., Text mining for sentiment analysis of twitter data. International Conference on Information and Knowledge Engineering, (IKE’12), Las Vegas, pp. 109–114, 2012.