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Examining the impacts of a movie trailer on box office performance with multi-modal deep learning approaches
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논문 기본 정보

Type
Academic journal
Author
EunSeo Yoo (Yonsei University) Junlin Lee (Yonsei University) YeonJoo Shin (Yonsei University) Ho Seung Kang (Yonsei University) Sang Yup Lee (Yonsei University)
Journal
Korea Intelligent Information Systems Society Journal of Intelligence and Information Systems Vol.30 No.3 KCI Accredited Journals
Published
2024.9
Pages
99 - 113 (15page)
DOI
10.13088/jiis.2024.30.3.099

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Examining the impacts of a movie trailer on box office performance with multi-modal deep learning approaches
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Movie trailers are known to be one of the most effective means of promoting a movie. They play a crucial role in helping audiences decide whether or not to watch a film, as they provide information about the plot, mood, scale, characters, and more. However, despite their importance, the impact of movie trailers on box office performance has not been systematically studied. To bridge the gap in the literature, this paper employs deep learning algorithms to extract visual, auditory, and dialogue features from movie trailers. Using these features, we analyze the impact of movie trailers on box office performance through a multi-modal approach utilizing deep learning-based models. Analyzing 296 movies released in South Korea from 2017 to 2023 revealed that the most accurate prediction of a movie’s box office success was achieved by considering the collective impact of different features of a trailer, rather than considering each feature separately. We also found that our model most accurately predicted the cumulative number of movie’ viewers up to the seven days after a movie’s release. Finally, the results showed that the genre of the movie did not significantly impact the effectiveness of movie trailers in predicting the box office performance. We believe that the present study makes a contribution to the field as it comprehensively analyzed the impact of movie trailers on the box office performance from a multifaceted perspective using a variety of deep learning algorithms.
#Movie Trailers #Box Office Performance Prediction #Deep Learning #Multi-modal Approach #영화 예고편 동영상 #영화 흥행 예측 #딥러닝 #멀티 모달 분석

Contents

1. Introduction
2. Research Methods
3. Results
4. Conclusion and Future Research Directions
References
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References (29)

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Abdali, A.-M. R., & Al-Tuma, R. F. (2019). Robust real-time violence detection in video using CNN and LSTM. In 2019 2nd Scientific Conference of Computer Sciences (SCCS). 104-108. https://doi.org/10.1109/SCCS.2019.8852616 google schola Asur, S., & Huberman, B. A. (2010). Predicting the future with social media. In 2010 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology.Vol 1. 492-499. IEEE. https://doi.org/10.1109/WI-IAT.2010.63 google schola Choudhery, D., & Leung, C. K. (2017). Social media mining: prediction of box office revenue. In Proceedings of the 21st international database engineering & applications symposium,20-29. https://doi.org/10.1145/3105831.3105854 google schola Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020). Electra: Pre-training text encoders as discriminators rather than generators. https://doi.org/10.48550/arXiv.2003.10555 google schola Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. https://doi.org/10.48550/arXiv.1810.04805 google schola

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영화 예고편이 영화 흥행에 미치는 영향 분석 - 멀티모달 딥러닝 방법을 기반으로 -
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