This paper presents an unsupervised opinion analysis method for debate-side classification, i.e., recognizing which stance a person is taking in an online debate. In order to handle the complexities of this genre, we mine the web to learn associations that are indicative of opinion stances in debates. We combine this knowledge with discourse information, and formulate the debate side classification task as an Integer Linear Programming problem. Our results show that our method is substantially better than challenging baseline methods.

In this work, we investigate whether the analysis of opinion expressions can help in scoring persuasive essays. For this, we develop systems that predict holistic essay scores based on features extracted from opinion expressions, topical elements, and their combinations. Experiments on test taker essays show that essay scores produced using opinion features are indeed correlated with human scores. Moreover, we find that combining opinions with their targets (what the opinions are about) produces the best result when compared to using only opinions or only topics.

This work proposes opinion frames as a representation of discourse-level associations which arise from related opinion topics. We illustrate how opinion frames help gather more information and also assist disambiguation. Finally we present the results of our experiments to detect these associations.

This work investigates design choices in modeling a discourse scheme for improving opinion polarity classification. For this, two diverse global inference paradigms are used: a supervised collective classification framework and an unsupervised optimization framework. Both approaches perform substantially better than baseline approaches, establishing the efficacy of the methods and the underlying discourse scheme. We also present quantitative and qualitative analyses showing how the improvements are achieved.

No abstract