Sentiment vector space model for lyric-based song sentiment classification

Authors:
Yunqing Xia;Linlin Wang;Kam-Fai Wong;Mingxing Xu
Affiliations:
Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;The Chinese University of Hong Kong, Shatin, Hong Kong;Tsinghua University, Beijing, China
Venue:
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Year:
2008

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Abstract

Lyric-based song sentiment classification seeks to assign songs appropriate sentiment labels such as light-hearted and heavy-hearted. Four problems render vector space model (VSM)-based text classification approach ineffective: 1) Many words within song lyrics actually contribute little to sentiment; 2) Nouns and verbs used to express sentiment are ambiguous; 3) Negations and modifiers around the sentiment keywords make particular contributions to sentiment; 4) Song lyric is usually very short. To address these problems, the sentiment vector space model (s-VSM) is proposed to represent song lyric document. The preliminary experiments prove that the s-VSM model outperforms the VSM model in the lyric-based song sentiment classification task.