ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection

Sujatha Das Gollapalli, Polina Rozenshtein, See-Kiong Ng


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.93.pdf
Code
 nusids/ester
Data
CARER