Abstract
This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that our system ranked above average.- Anthology ID:
- S17-2137
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 812–816
- Language:
- URL:
- https://aclanthology.org/S17-2137
- DOI:
- 10.18653/v1/S17-2137
- Cite (ACL):
- Yunxiao Zhou, Man Lan, and Yuanbin Wu. 2017. ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 812–816, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification (Zhou et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/S17-2137.pdf