Abstract
The diverse movie industry faces many challenges in the promotion of the product across different demographics. Movie trailer engagements provide valuable information about how the audience perceives the movie. This information can be used to predict the success of the upcoming movie before it gets released. The previous research works were mainly concentrating on Hindi language movies to predict success. The current research paper includes the success prediction of movies other than Hindi. This paper aims to analyze various Machine Learning models’ performance and select the best performing model to predict movie success. The developed model can efficiently classify successful and unsuccessful movies. For the current research, the data is collected from various sources through web scrapping and API calls in Sacnilk, The Movie Database (TMDB), YouTube, and Twitter. Different machine learning classification models such as Random Forest, Logistic Regression, KNN, and Gaussian Naïve Bayes are tested to develop the best-performing prediction model. This research can help moviemakers to understand the popularity of the movie among the viewers and decide on an efficient promotional strategy to make the movie more successful.
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Emmanuvel, A., Bhagat, V., Jacob, L. (2021). Movie Success Prediction from Movie Trailer Engagement and Sentiment Analysis. In: Shukla, S., Unal, A., Kureethara, J.V., Mishra, D.K., Han, D.S. (eds) Data Science and Security. Lecture Notes in Networks and Systems, vol 290. Springer, Singapore. https://doi.org/10.1007/978-981-16-4486-3_43
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DOI: https://doi.org/10.1007/978-981-16-4486-3_43
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