Affective Lyrics Analysis for Mood Estimation of Chinese Pop Music
Date Issued
2009
Date
2009
Author(s)
Wu, Ying-Shian
Abstract
Music mood estimation (MME) is a key technology in mood-based music recommendation.While mainstream MME research nowadays relies on audio music analysis, exploringhe significance of lyrics text in predicting song emotion is gaining attention in recent years.ne major impediment to MME research is the lack of a clearly labeled and publicly available dataset annotating the emotion ratings of lyrics text and audio separately. Inight of this, we compiled a dataset of 600 pop songs (iPop) from the mood ratings of 246 participants who experienced three different song sessions, lyrics text (L), audio music track (M), and lyrics text plus audio music track (LM). We then applied statistical analysis to estimate how lyrics text and audio contribute to a song''s overall valence-arousal (V-A) mood ratings. Our results show that lyrics text are not only a valid measure for estimating a song''s mood ratings but also provide supplementary information that can improve audioonly MME systems. Furthermore, the lyrics text is more dominant at deciding the valence value of music than audio music track and audio music track is more dominant at deciding the arousal value of music than lyrics text.o improve the performance of MME system, we proposed sentiment score approach which extracts affective words to be lyrics text feature. The approach first calculates sentiment score base on the words'' average term frequencies in positive music and negative music, then extracts affective words according to their sentiment scores. We also construct an MME system system which uses affective words as lyrics text feature. In estimating mood of music, the model with affective words as lyrics text feature performs better than the model with lyrics text feature including both affective words and non affective words or the model with only audio track features.
Subjects
affective computing
music information retrieval
affective word extraction
lyrics
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-98-R96922001-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):c25bdb03a7c638fd6b43de4f59f23c19