Multi-Label Classification of Film Genres Based on Synopsis Using Support Vector Machine, Logistic Regression and Naïve Bayes Algorithms

Movies were still a very popular means of entertainment. The current distribution of internet users causes a large amount of movie data to be created and distributed online. The emergence of movie streaming services makes consumers very interested in using automatic film genre classification. In this study, a multi-label film genre classification will be carried out based on an English synopsis. Data were collected from the Internet Movie Database (IMDb) website. The amount of data used in this study was 10,432 lines of data obtained using scraping techniques on June 7, 2022. Researchers divided the dataset labels into 18 labels representing each genre. Feature extraction using TF-IDF and Stemming. The multi-label classification algorithm used is the Support Vector Machine, Logistic Regression, and Naive Bayes Algorithms. Optimal parameter search using GridSearch of each algorithm. The optimum result in this study was obtained f1-score value of 0.58 using the SVM algorithm with TF-IDF feature extraction with stemming dataset, followed by NB with the f1-score value of 0.48 and LR with an f1-score value of 0.43.