Lyric-based Sentiment Polarity Classification of Thai Songs - Srinilta, Chutimet; Sunhem, Wisuwat; Tungjitnob, Suchat; Thasanthiah, Saruta; Vatathanavaro, Supawit (My University, 2017)
Song sentiment polarity provides outlook of a song. It can be used in automatic music recommendation system. Sentiment polarity classification based solely on lyrics is challenging. It involves understanding linguistic knowledge, song characteristics and emotional interpretation of words. Since lyric is in a form of text. Techniques used in text mining, text sentiment analysis and music mood classification are studied and used together in our proposed model. Two types of classifier are proposed—lexicon-based classifier and machine learning-based classifier. N-gram model is used in feature set generation. Features are filtered by Information Gain. Feature weighting scheme is employed. We create a sentiment lexicon from Thai song corpus. Full lyric and certain parts of lyric are chosen for datasets. We evaluate our models under various environments. The best average accuracy achieved is 68%.
Keywords: Lyric; Music mood classification; Neural network; Sentiment polarity analysis; Thai songs