Abstract
We address the task of targeted sentiment as a means of understanding the sentiment that students hold toward courses and instructors, as expressed by students in their comments. We introduce a new dataset consisting of student comments annotated for targeted sentiment and describe a system that can both identify the courses and instructors mentioned in student comments, as well as label the students’ sentiment toward those entities. Through several comparative evaluations, we show that our system outperforms previous work on a similar task.- Anthology ID:
- C16-1233
- 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:
- 2471–2481
- Language:
- URL:
- https://aclanthology.org/C16-1233
- DOI:
- Cite (ACL):
- Charles Welch and Rada Mihalcea. 2016. Targeted Sentiment to Understand Student Comments. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2471–2481, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Targeted Sentiment to Understand Student Comments (Welch & Mihalcea, COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/C16-1233.pdf