A Hybrid Modeling Approach for an Automated Lyrics-Rating System for Adolescents

  • Jayong Kim
  • Mun Y. YiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)


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.


Lyrics classification Offensive language detection RNN 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Graduate School of Knowledge Service EngineeringKorea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea

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