Multi-Emotion Classification for Song Lyrics Darren Edmonds João Sedoc Donald Bren School of ICS Stern School of Business University of California, Irvine New York University
[email protected] [email protected] Abstract and Shamma, 2010; Bobicev and Sokolova, 2017; Takala et al., 2014). Building such a model is espe- Song lyrics convey a multitude of emotions to the listener and powerfully portray the emo- cially challenging in practice as there often exists tional state of the writer or singer. This considerable disagreement regarding the percep- paper examines a variety of modeling ap- tion and interpretation of the emotions of a song or proaches to the multi-emotion classification ambiguity within the song itself (Kim et al., 2010). problem for songs. We introduce the Edmonds There exist a variety of high-quality text datasets Dance dataset, a novel emotion-annotated for emotion classification, from social media lyrics dataset from the reader’s perspective, datasets such as CBET (Shahraki, 2015) and TEC and annotate the dataset of Mihalcea and Strapparava(2012) at the song level. We (Mohammad, 2012) to large dialog corpora such find that models trained on relatively small as the DailyDialog dataset (Li et al., 2017). How- song datasets achieve marginally better perfor- ever, there remains a lack of comparable emotion- mance than BERT (Devlin et al., 2019) fine- annotated song lyric datasets, and existing lyrical tuned on large social media or dialog datasets. datasets are often annotated for valence-arousal af- fect rather than distinct emotions (Çano and Mori- 1 Introduction sio, 2017). Consequently, we introduce the Ed- Text-based sentiment analysis has become increas- monds Dance Dataset1, a novel lyrical dataset that ingly popular in recent years, in part due to its was crowdsourced through Amazon Mechanical numerous applications in fields such as marketing, Turk.