Abstract
Emotion classification from text typically requires some degree of word-emotion association, either gathered from pre-existing emotion lexicons or calculated using some measure of semantic relatedness. Most emotion lexicons contain a fixed number of emotion categories and provide a rather limited coverage. Current measures of computing semantic relatedness, on the other hand, do not adapt well to the specific task of word-emotion association and therefore, yield average results. In this work, we propose an unsupervised method of learning word-emotion association from large text corpora, called Selective Co-occurrences (SECO), by leveraging the property of mutual exclusivity generally exhibited by emotions. Extensive evaluation, using just one seed word per emotion category, indicates the effectiveness of the proposed approach over three emotion lexicons and two state-of-the-art models of word embeddings on three datasets from different domains.- Anthology ID:
- C16-1149
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1579–1590
- Language:
- URL:
- https://aclanthology.org/C16-1149
- DOI:
- Cite (ACL):
- Ameeta Agrawal and Aijun An. 2016. Selective Co-occurrences for Word-Emotion Association. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1579–1590, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Selective Co-occurrences for Word-Emotion Association (Agrawal & An, COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/C16-1149.pdf