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A multi-label movie genre classification scheme based on the movie’s subtitles

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Abstract

Prediction of movie genres is an intriguing problem that has several applications in designing recommendation systems for the audiences, analyzing movie box office performance and understanding the theme of the movie to list some. This is a classic multi-label classification problem. An algorithm for movie genre detection has been proposed built on the yet unused movie’s subtitles which are a documented account of the movie’s visual content and dialogues. The basic idea is to identify words that have high frequency in a particular genre and use them as features for training the classification machine learning models. The performance of the algorithm was tested on English subtitles of 964 movies of six genres: Action, Fantasy, Horror, Romance, Sports and War. Experiments were conducted with varied number of features and six machine learning models. The best result was obtained using K-Nearest Neighbour (kNN) with the average precision for all genres being 77.7% with 200 features. Another noteworthy result was an average precision of 75.2% using kNN with merely 50 features. The algorithm performed very well for the genres: Sports and War with above 90% precision in some cases.

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Authors and Affiliations

  1. Department of Computer Science, Ramanujan College (University of Delhi), New Delhi, India

    Nikhil Kumar Rajput & Bhavya Ahuja Grover

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  1. Nikhil Kumar Rajput
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  2. Bhavya Ahuja Grover
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Correspondence to Bhavya Ahuja Grover.

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Appendix

Appendix

Table 1 Results for ThreshLow = 100
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Table 2 Results for ThreshLow = 500
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Table 3 Results for ThreshLow = 500
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Table 4 Results for ThreshLow = 500
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Table 5 Results for ThreshLow = 1000
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Rajput, N.K., Grover, B.A. A multi-label movie genre classification scheme based on the movie’s subtitles. Multimed Tools Appl 81, 32469–32490 (2022). https://doi.org/10.1007/s11042-022-12961-6

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  • DOI: https://doi.org/10.1007/s11042-022-12961-6

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