Classification of Korean Drama Popularity Based on Ratings Using Naïve Bayes

Abstrak

This study aims to classify the popularity of Korean dramas based on ratings obtained from the MyDramaList website. With the rapid growth of digital entertainment platforms, evaluating drama popularity has become increasingly important for understanding audience preferences and supporting decision-making in the content industry. The Naive Bayes algorithm is employed as the classification method due to its computational efficiency and suitability for handling categorical and numerical features. The dataset comprises 351 Korean dramas with attributes including title, year of release, genre, tags, number of episodes, cast information, synopsis, and user ratings. Ratings serve as the primary label for categorizing dramas into three classes: Top Dramas (rating ≥ 8.5), Popular (7.5–8.4), and Less Popular (< 7.5). The classification pipeline involves data preprocessing, feature encoding, and model training using Naive Bayes. Evaluation results yield an overall accuracy of 79%, with per-class performance assessed through precision, recall, and F1-score metrics. Supplementary visualizations, including pie charts, bar charts, and word clouds, are employed to analyze the distribution of dominant genres and tags across popularity categories. The findings indicate that the proposed approach provides a viable baseline for drama popularity classification while revealing content patterns, such as the prevalence of specific genres and thematic tags among top-rated dramas, that may inform content curation strategies on digital platforms.