Abstract
What do linguistic models reveal about the emotions associated with words? In this study, we consider the task of estimating word-level emotion intensity scores for specific emotions, exploring unsupervised, supervised, and finally a self-supervised method of extracting emotional associations from pretrained vectors and models. Overall, we find that linguistic models carry substantial potential for inducing fine-grained emotion intensity scores, showing a far higher correlation with human ground truth ratings than state-of-the-art emotion lexicons based on labeled data.- Anthology ID:
- 2021.emnlp-main.781
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9911–9917
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.781
- DOI:
- 10.18653/v1/2021.emnlp-main.781
- Cite (ACL):
- Shahab Raji and Gerard de Melo. 2021. Guilt by Association: Emotion Intensities in Lexical Representations. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9911–9917, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Guilt by Association: Emotion Intensities in Lexical Representations (Raji & de Melo, EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/landing_page/2021.emnlp-main.781.pdf