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Lyrics-Based Emotion Classification Using Feature Selection by Partial Syntactic Analysis

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2 Author(s)
Minho Kim ; Dept. of Comput. Sci., Pusan Nat. Univ., Busan, South Korea ; Hyuk-Chul Kwon

Songs feel emotionally different to listeners depending on their lyrical contents, even when melodies are similar. Accordingly, when using features related to melody, like tempo, rhythm, tune, and musical note, it is difficult to classify emotions accurately through the existing music emotion classification methods. This paper therefore proposes a method for lyrics-based emotion classification using feature selection by partial syntactic analysis. Based on the existing emotion ontology, four kinds of syntactic analysis rules were applied to extract emotion features from lyrics. The precision and recall rates of the emotion feature extraction were 73% and 70%, respectively. The extracted emotion features along with the NB, HMM, and SVM machine learning methods were used, showing a maximum accuracy rate of 58.8%.

Published in:

Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on

Date of Conference:

7-9 Nov. 2011