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A Hybrid Recommendation System of Upcoming Movies Using Sentiment Analysis of YouTube Trailer Reviews

Author

Listed:
  • Sandipan Sahu

    (Department of Computer Science and Engineering, GIET University, Rayagada 765022, India)

  • Raghvendra Kumar

    (Department of Computer Science and Engineering, GIET University, Rayagada 765022, India)

  • Pathan MohdShafi

    (Department of Computer Science and Engineering, MITADT University, Loni Kalbhor 412201, India)

  • Jana Shafi

    (Department of Computer Science, College of Arts and Science, Prince Sattam bin Abdul Aziz University, Wadi Ad Dawasir 11991, Saudi Arabia)

  • SeongKi Kim

    (National Centre of Excellence in Software, Sangmyung University, Seoul 03016, Korea)

  • Muhammad Fazal Ijaz

    (Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea)

Abstract

Movies are one of the integral components of our everyday entertainment. In today’s world, people prefer to watch movies on their personal devices. Many movies are available on all popular Over the Top (OTT) platforms. Multiple new movies are released onto these platforms every day. The recommendation system is beneficial for guiding the user to a choice from among the overloaded contents. Most of the research on these recommendation systems has been conducted based on existing movies. We need a recommendation system for forthcoming movies in order to help viewers make a personalized decision regarding which upcoming new movies to watch. In this article, we have proposed a framework combining sentiment analysis and a hybrid recommendation system for recommending movies that are not yet released, but the trailer has been released. In the first module, we extracted comments about the movie trailer from the official YouTube channel for Netflix, computed the overall sentiment, and predicted the rating of the upcoming movies. Next, in the second module, our proposed hybrid recommendation system produced a list of preferred upcoming movies for individual users. In the third module, we finally were able to offer recommendations regarding potentially popular forthcoming movies to the user, according to their personal preferences. This method fuses the predicted rating and preferred list of upcoming movies from modules one and two. This study used publicly available data from The Movie Database (TMDb). We also created a dataset of new movies by randomly selecting a list of one hundred movies released between 2020 and 2021 on Netflix. Our experimental results established that the predicted rating of unreleased movies had the lowest error. Additionally, we showed that the proposed hybrid recommendation system recommends movies according to the user’s preferences and potentially promising forthcoming movies.

Suggested Citation

  • Sandipan Sahu & Raghvendra Kumar & Pathan MohdShafi & Jana Shafi & SeongKi Kim & Muhammad Fazal Ijaz, 2022. "A Hybrid Recommendation System of Upcoming Movies Using Sentiment Analysis of YouTube Trailer Reviews," Mathematics, MDPI, vol. 10(9), pages 1-22, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1568-:d:809784
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    References listed on IDEAS

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    1. Heng-Ru Zhang & Fan Min & Xu He & Yuan-Yuan Xu, 2015. "A Hybrid Recommender System Based on User-Recommender Interaction," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, February.
    2. Mohammed Amin Belarbi & Saïd Mahmoudi & Ghalem Belalem, 2017. "PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 8(4), pages 45-58, October.
    3. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
    4. Rong Xiang & Emmanuele Chersoni & Qin Lu & Chu‐Ren Huang & Wenjie Li & Yunfei Long, 2021. "Lexical data augmentation for sentiment analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(11), pages 1432-1447, November.
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    1. Saisai Yu & Ming Guo & Xiangyong Chen & Jianlong Qiu & Jianqiang Sun, 2023. "Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks," Mathematics, MDPI, vol. 11(6), pages 1-22, March.

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