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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2410.05778 |
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| _version_ | 1866929533239689216 |
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| author | Sakunkoo, Jonathan Sakunkoo, Annabella |
| author_facet | Sakunkoo, Jonathan Sakunkoo, Annabella |
| contents | Songs have been found to profoundly impact human emotions, with lyrics having significant power to stimulate emotional changes in the audience. There is a scarcity of large, high quality in-domain datasets for lyrics-based song emotion classification (Edmonds and Sedoc, 2021; Zhou, 2022). It has been noted that in-domain training datasets are often difficult to acquire (Zhang and Miao, 2023) and that label acquisition is often limited by cost, time, and other factors (Azad et al., 2018). We examine the novel usage of a large out-of-domain dataset as a creative solution to the challenge of training data scarcity in the emotional classification of song lyrics. We find that CNN models trained on a large Reddit comments dataset achieve satisfactory performance and generalizability to lyrical emotion classification, thus giving insights into and a promising possibility in leveraging large, publicly available out-of-domain datasets for domains whose in-domain data are lacking or costly to acquire. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05778 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Song Emotion Classification of Lyrics with Out-of-Domain Data under Label Scarcity Sakunkoo, Jonathan Sakunkoo, Annabella Computation and Language Machine Learning Songs have been found to profoundly impact human emotions, with lyrics having significant power to stimulate emotional changes in the audience. There is a scarcity of large, high quality in-domain datasets for lyrics-based song emotion classification (Edmonds and Sedoc, 2021; Zhou, 2022). It has been noted that in-domain training datasets are often difficult to acquire (Zhang and Miao, 2023) and that label acquisition is often limited by cost, time, and other factors (Azad et al., 2018). We examine the novel usage of a large out-of-domain dataset as a creative solution to the challenge of training data scarcity in the emotional classification of song lyrics. We find that CNN models trained on a large Reddit comments dataset achieve satisfactory performance and generalizability to lyrical emotion classification, thus giving insights into and a promising possibility in leveraging large, publicly available out-of-domain datasets for domains whose in-domain data are lacking or costly to acquire. |
| title | Song Emotion Classification of Lyrics with Out-of-Domain Data under Label Scarcity |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.05778 |