PolariCQ: Polarity classification of political quotations

Abstract

We consider the problem of automatically classifying quotations about political debates into both topic and polarity. These quotations typically appear in news media and online forums. Our approach maps quotations onto one or more topics in a category system of political debates, containing more than a thousand fine-grained topics. To overcome the difficulty that pro/con classification faces due to the brevity of quotations and sparseness of features, we have devised a model of quotation expansion that harnesses antonyms from thesauri like WordNet. We developed a suite of statistical language models, judiciously customized to our settings, and use these to define similarity measures for unsupervised or supervised classifications. Experiments show the effectiveness of our method.