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.
|Title of host publication||Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)|
|Place of Publication||Vancouver, Canada|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||5|
|State||Published - Aug 1 2017|
Onyibe, C., & Habash, N. (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) (pp. 670-674). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/S17-2111