Abstract
We describe the system submitted to the SemEval-2016 for detecting stance in tweets (Task 6, Subtask A). One of the main goals of stance detection is to automatically determine the stance of a tweet towards a specific target as 'FAVOR', 'AGAINST', or 'NONE'. We developed a supervised system using Support Vector Machines to identify the stance by analyzing various lexical and semantic features. The average F1 score achieved by our system is 60.60.
Cite
CITATION STYLE
Patra, B. G., Das, D., & Bandyopadhyay, S. (2016). JU-NLP at SemEval-2016 Task 6: Detecting stance in tweets using support vector machines. In SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings (pp. 440–444). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s16-1071
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