Abstract
This research work presents a movie success prediction mechanisms using Twitter mining. The proposed methods predict movie success in terms of rating and temporal popularity. We develop two models to achieve these goals. The first model, the rating prediction model (RPM), aims to predict the users’ satisfaction with the product using an ensemble regression model. The second model is a temporal product popularity model (T-PPM) that aims to predict the product’s temporal popularity using a random forest classifier. We collect a new dataset called TweetAMovie from IMDb and Twitter to evaluate the developed models. The comparative results against baseline methods demonstrate the superiority of the proposed models. On average, the RPM and T-PPM achieved 32.4% and 30.2% improvement in terms of accuracy. Also, the average precision and F-score results improved by T-PPM are 6.7% and 2.6%, respectively.
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Alhijawi, B., Awajan, A. (2022). Prediction of Movie Success Using Twitter Temporal Mining. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_12
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DOI: https://doi.org/10.1007/978-981-16-2377-6_12
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