Abstract
We describe a supervised system that uses optimized Condition Random Fields and lexical features to predict the sentiment of a tweet. The system was submitted to the English version of all subtasks in SemEval-2017 Task 4.- Anthology ID:
- S17-2111
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 670–674
- Language:
- URL:
- https://aclanthology.org/S17-2111
- DOI:
- 10.18653/v1/S17-2111
- Cite (ACL):
- Chukwuyem Onyibe and Nizar Habash. 2017. OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 670–674, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields (Onyibe & Habash, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/S17-2111.pdf