OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields

Chukwuyem Onyibe, Nizar Habash


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
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:
670–674
Language:
URL:
https://aclanthology.org/S17-2111
DOI:
10.18653/v1/S17-2111
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/S17-2111.pdf