CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text
Published 1 January 2016Open access
Heba Elfardy, Mona Diab
Citations39
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Abstract
We present a supervised system that uses lexical, sentiment, semantic dictionaries and latent and frame semantic features to identify the stance of a tweeter towards an ideological target. We evaluate the performance of the proposed system on subtask A in SemEval-2016 Task 6: "Detecting Stance in Tweets". The system yields an average F =1 score of 63.6% on the task's test set and has been ranked the 6 th by the task organizers out of 19 judged systems.
Keywords
PsychologyComputer Science
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