Abstract
We investigate the effect of using sentiment expression boundaries in predicting sentiment polarity in aspect-level sentiment analysis. We manually annotate a freely available English sentiment polarity dataset with these boundaries and carry out a series of experiments which demonstrate that high quality sentiment expressions can boost the performance of polarity classification. Our experiments with neural architectures also show that CNN networks outperform LSTMs on this task and dataset.- Anthology ID:
- W18-6222
- Volume:
- Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Alexandra Balahur, Saif M. Mohammad, Veronique Hoste, Roman Klinger
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 156–166
- Language:
- URL:
- https://aclanthology.org/W18-6222
- DOI:
- 10.18653/v1/W18-6222
- Cite (ACL):
- Rasoul Kaljahi and Jennifer Foster. 2018. Sentiment Expression Boundaries in Sentiment Polarity Classification. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 156–166, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Sentiment Expression Boundaries in Sentiment Polarity Classification (Kaljahi & Foster, WASSA 2018)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/W18-6222.pdf
- Data
- MPQA Opinion Corpus