Ideological Perspective Detection Using Semantic Features

In this paper, we propose the use of word sense disambiguation and latent semantic features to automatically identify a person’s perspective from his/her written text. We run an Amazon Mechanical Turk experiment where we ask Turkers to answer a set of constrained and open-ended political questions drawn from the American National Election Studies (ANES). We then extract the proposed features from the answers to the open-ended questions and use them to predict the answer to one of the constrained questions, namely, their preferred Presidential Candidate. In addition to this newly created dataset, we also evaluate our proposed approach on a second standard dataset of “Ideological-Debates”. This latter dataset contains topics from four domains: Abortion, Creationism, Gun Rights and GayRights. Experimental results show that using word sense disambiguation and latentsemantics, whether separately or combined, beats the majority and random baselines on the cross-validation and held-out-test sets for both the ANES and the four domains of the “Ideological Debates” datasets. Moreover combining both feature sets outperforms a stronger unigram-only classification system.