| ## Edmonds Dance Dataset | |
| This is from the paper [Multi-Emotion Classification for Song Lyrics](https://aclanthology.org/2021.wassa-1.24) | |
| by Darren Edmonds, João Sedoc | |
| Please cite our paper if you use this dataset. | |
| ``` | |
| @inproceedings{edmonds-sedoc-2021-multi, | |
| title = "Multi-Emotion Classification for Song Lyrics", | |
| author = "Edmonds, Darren and | |
| Sedoc, Jo{\~a}o", | |
| booktitle = "Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis", | |
| month = apr, | |
| year = "2021", | |
| address = "Online", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/2021.wassa-1.24", | |
| pages = "221--235", | |
| 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.", | |
| } | |
| ``` | |
| --- | |
| license: mit | |
| task_categories: | |
| - text-classification | |
| language: | |
| - en | |
| tags: | |
| - music | |
| size_categories: | |
| - n<1K | |
| --- |