Movie Revenue Prediction Based on Purchase Intention Mining Using YouTube Trailer Reviews |
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| Institution: | 1. Department of Information Technology, Faculty of Computer Science and Information Technology, Bayero University Kano, 700241 Kano, Nigeria;2. Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malyasia, 43600 UKM Bangi, Selangor, Malaysia;1. Institute of Informatics and Telecommunications, NCSR Demokritos, Athens, Greece;2. School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece;1. Universidad Autónoma de Madrid, Escuela Politécnica Superior, C/Francisco Tomás y Valiente,11,Madrid, 28049, Spain;2. School of Computing Science, University of Glasgow, Lilybank Gardens, G12 8QQ, Glasgow, Scotland, United Kingdom;1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;2. Baidu Inc., Beijing, China;3. Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Science, Tianjin Normal University, Tianjin 300387, China |
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| Abstract: | The increase in acceptability and popularity of social media has made extracting information from the data generated on social media an emerging field of research. An important branch of this field is predicting future events using social media data. This paper is focused on predicting box-office revenue of a movie by mining people's intention to purchase a movie ticket, termed purchase intention, from trailer reviews. Movie revenue prediction is important due to risks involved in movie production despite the high cost involved in the production. Previous studies in this domain focus on the use of twitter data and IMDB reviews for the prediction of movies that have already been released. In this paper, we build a model for movie revenue prediction prior to the movie's release using YouTube trailer reviews. Our model consists of novel methods of calculating purchase intention, positive-to-negative sentiment ratio, and like-to-dislike ratio for movie revenue prediction. Our experimental results prove the superiority of our approach compared to three baseline approaches and achieved a relative absolute error of 29.65%. |
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