Abstract
The South Korean government operates human-based lyrics-rating systems to reduce adolescents’ exposure to inappropriate songs. In this study, we developed lyrics classification models for an automated lyrics-rating system for adolescents. There are two kinds of inappropriate lyrics for adolescents: (1) lyrics with inappropriate words and (2) lyrics with inappropriate content based on the semantic context. To tackle the first issue, we propose \( {\text{logCD}}_{\alpha } \) as a method for generating a lexicon of inappropriate words. It attained the highest performance among the lexicon-based filtering methods examined. Further, to deal with the second issue, we propose a hybrid classification model that combines \( {\text{logCD}}_{\alpha } \) with an RNN based model. The hybrid model composed of a ‘lexicon-checking model’ and a ‘context-checking model’ achieved the highest performance among all of the models examined, highlighting the effectiveness of combining the models to specifically target each of the two types of inappropriate lyrics.
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Kim, J., Yi, M.Y. (2019). A Hybrid Modeling Approach for an Automated Lyrics-Rating System for Adolescents. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_53
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