Paper Info
Title
A Hybrid Modeling Approach for an Automated Lyrics-Rating System for Adolescents
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.
Year
DOI
Venue
2019
10.1007/978-3-030-15712-8_53
european conference on information retrieval
Field
DocType
Citations 
Inappropriate words,Information retrieval,Rating system,Computer science,Semantic context,Lexicon,Lyrics
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Jayong Kim100.34
Mun Y. Yi2115058.52