Abstract
We demonstrate a method of improving a seed sentiment lexicon developed on essay data by using a pivot-based paraphrasing system for lexical expansion coupled with sentiment profile enrichment using crowdsourcing. Profile enrichment alone yields up to 15% improvement in the accuracy of the seed lexicon on 3-way sentence-level sentiment polarity classification of essay data. Using lexical expansion in addition to sentiment profiles provides a further 7% improvement in performance. Additional experiments show that the proposed method is also effective with other subjectivity lexicons and in a different domain of application (product reviews).- Anthology ID:
- Q13-1009
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 1
- Month:
- Year:
- 2013
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 99–110
- Language:
- URL:
- https://aclanthology.org/Q13-1009
- DOI:
- 10.1162/tacl_a_00213
- Cite (ACL):
- Beata Beigman Klebanov, Nitin Madnani, and Jill Burstein. 2013. Using Pivot-Based Paraphrasing and Sentiment Profiles to Improve a Subjectivity Lexicon for Essay Data. Transactions of the Association for Computational Linguistics, 1:99–110.
- Cite (Informal):
- Using Pivot-Based Paraphrasing and Sentiment Profiles to Improve a Subjectivity Lexicon for Essay Data (Beigman Klebanov et al., TACL 2013)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/Q13-1009.pdf