CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text

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.