Summary of Song Emotion Classification Of Lyrics with Out-of-domain Data Under Label Scarcity, by Jonathan Sakunkoo and Annabella Sakunkoo
Song Emotion Classification of Lyrics with Out-of-Domain Data under Label Scarcity
by Jonathan Sakunkoo, Annabella Sakunkoo
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the use of a large out-of-domain dataset as a solution to the challenge of training data scarcity in song lyrics emotion classification. By leveraging a Reddit comments dataset and training CNN models, researchers found that this approach achieves satisfactory performance and generalizability to lyrical emotion classification. This work highlights the potential for using publicly available datasets to overcome the limitations of acquiring in-domain training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how we can use big datasets from other places to help us understand how song lyrics make people feel. Usually, it’s hard to get enough good data just for songs, but we tried something new and found that it worked! We used a huge collection of comments from Reddit and trained special computer models on them. These models did a great job at understanding the emotions behind song lyrics. This is important because it might help us use big datasets in other places where getting the right data is hard. |
Keywords
» Artificial intelligence » Classification » Cnn
