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

Proceedings of SemEval-2016, pages 434–439, San Diego, California, June 16-17, 2016. c⃝2016 Association for Computational Linguistics CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text Heba Elfardy Department of Computer Science Columbia University New York, NY heba@cs.columbia.edu Mona Diab Department of Computer Science The George Washington University Washington, DC mtdiab@gwu.edu Abstract We present a supervised system that uses lex- ical, sentiment, semantic dictionaries and la- tent and frame semantic features to identify the stance of a tweeter towards an ideologi- cal 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 Fβ=1 score of 63.6% on the task’s test set and has been ranked the 6th by the task organizers out of 19 judged systems. 1 Introduction & Background Automatically identifying a person’s ideological stance (or perspective) from written text is quite a challenging research problem that finds applications spanning from enhancing advertisements targeting to planning political campaigns. From a computa- tional viewpoint, perspective detection is closely re- lated to subjectivity and sentiment analysis. A per- son’s perspective normally influences his/her senti- ment towards different topics or targets. Conversely identifying the sentiment of a person towards multi- ple targets can serve as a cue for identifying his/her perspective. While from a social-science viewpoint, the notion of “perspective” is related to the concept of “framing”. Framing involves making some top- ics (or some aspects of the discussed topics) more prominent in order to promote the views and inter- pretations of the writer (communicator). The com- municator can either make these framing decisions consciously or unconsciously (Entman, 1993). In this paper, we present a system that employs lexical and semantic features to identify the stance of a person – “Favor”, “Against” or “None”– on topics that are often backed up by one’s ideology or belief system such as abortion, climate change and femi- nism. We evaluate the performance of the proposed system through the participation in Sub-Task A “Su- pervised Framework” of SemEval Task 6 “Detecting Stance in Tweets”. (Mohammad et al., 2016) We build on our previous work on automatic perspective detection (Elfardy et al., 2015) by exploring the use of sentiment, Linguistic Inquiry and Word Count (LIWC) dictionaries, frame and latent semantics in addition to standard lexical features to automatically classify a given tweet according to its stance on the topic of interest. Most current computational linguistics research on supervised stance detection performs document (or post) level stance classification, whether binary or multiway and use a variety of lexical, syntac- tic and semantic features to identify the stance of a post towards a specific contentious topic/target such as the Israeli-Palestinian conflict, abortion, creation- ism, gun-rights, gay-rights, healthcare, legalization of marijuana and death penalty (Lin et al., 2006; Klebanov et al., 2010; Hasan and NG, 2012; Hasan and Ng, 2013; Somasundaran and Wiebe, 2010; El- fardy et al., 2015). For a detailed literature review, we direct the reader to our *Sem paper. …

Content truncated to 500 words