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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.
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Patra et al. (2016) studied this question.