Abstract
Song lyrics convey a multitude of emotions to the listener and powerfully portray the emotional state of the writer or singer. This paper examines a variety of modeling approaches to the multi-emotion classification problem for songs. We introduce the Edmonds Dance dataset, a novel emotion-annotated lyrics dataset from the reader’s perspective, and annotate the dataset of Mihalcea and Strapparava (2012) at the song level. We find that models trained on relatively small song datasets achieve marginally better performance than BERT (Devlin et al., 2018) fine-tuned on large social media or dialog datasets.- Anthology ID:
- 2021.wassa-1.24
- Volume:
- Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Orphee De Clercq, Alexandra Balahur, Joao Sedoc, Valentin Barriere, Shabnam Tafreshi, Sven Buechel, Veronique Hoste
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 221–235
- Language:
- URL:
- https://aclanthology.org/2021.wassa-1.24
- DOI:
- Cite (ACL):
- Darren Edmonds and João Sedoc. 2021. Multi-Emotion Classification for Song Lyrics. In Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 221–235, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Emotion Classification for Song Lyrics (Edmonds & Sedoc, WASSA 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.wassa-1.24.pdf
- Data
- DailyDialog