Abstract
Accurate detection of emotions in user- generated text was shown to have several applications for e-commerce, public well-being, and disaster management. Currently, the state-of-the-art performance for emotion detection in text is obtained using complex, deep learning models trained on domain-specific, labeled data. In this paper, we propose ESTeR , an unsupervised model for identifying emotions using a novel similarity function based on random walks on graphs. Our model combines large-scale word co-occurrence information with word-associations from lexicons avoiding not only the dependence on labeled datasets, but also an explicit mapping of words to latent spaces used in emotion-enriched word embeddings. Our similarity function can also be computed efficiently. We study a range of datasets including recent tweets related to COVID-19 to illustrate the superior performance of our model and report insights on public emotions during the on-going pandemic.- Anthology ID:
- 2020.findings-emnlp.93
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1043–1056
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.93
- DOI:
- 10.18653/v1/2020.findings-emnlp.93
- Cite (ACL):
- Sujatha Das Gollapalli, Polina Rozenshtein, and See-Kiong Ng. 2020. ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1043–1056, Online. Association for Computational Linguistics.
- Cite (Informal):
- ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection (Gollapalli et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.93.pdf
- Code
- nusids/ester
- Data
- CARER