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 . The importance of discourse in opinion analysis is being increasingly recognized ( Polanyi and Zaenen , 2006 ) . Motivated by the need to enable discourse-based opinion analysis , previous research ( Asher et al . , 2008 ; Somasundaran et al . , 2008 ) developed discourse schemes and created manually annotated corpora . However , it was not known whether and how well these linguistic ideas and schemes can be translated into effective computational implementations . In this paper , we first investigate ways in which an opinion discourse scheme can be computationally modeled , and then how it can be utilized to improve polarity classification . Specifically , the discourse scheme we use is from Somasundaran et al . ( 2008 ) , which was developed to support a global , interdependent polarity interpretation . To achieve discourse-based global inference , we explore two different frameworks . The first is a supervised framework that learns interdependent opinion interpretations from training data . The second is an unsupervised optimization framework which uses constraints to express the ideas of coherent opinion interpretation embodied in the scheme . For the supervised framework , we use Iterative Collective Classification ( ICA ) , which facilitates machine learning using relational information . The unsupervised optimization is implemented as an Integer Linear Programming ( ILP ) problem . Via our implementations , we aim to empirically test if discourse-based approaches to opinion analysis are useful . Our results show that both of our implementations achieve significantly better accuracies in polarity classification than classifiers using local information alone . This confirms the hypothesis that the discourse-based scheme is useful , and also shows that both of our design choices are effective . We also find that there is a difference in the way ICA and ILP achieve improvements , and a simple hybrid approach , which incorporates the strengths of both , is able to achieve significant overall improvements over both . Our analyses show that even when our discourse-based methods bootstrap from noisy classifications , they can achieve good improvements . The rest of this paper is organized as follows : we discuss related work in Section 2 and the discourse scheme in Section 3 . We present our discourse-based implementations in Section 4 , experiments in Section 5 , discussions in Section 6 and conclusions in Section 7 . This work focuses on the first step to ascertain whether discourse relations are useful for improving opinion polarity classification , whether they can be modeled and what modeling choices can be used . To this end , we explored two distinct paradigms : the supervised ICA and the unsupervised ILP . We showed that both of our approaches are effective in exploiting discourse relations to significantly improve polarity classification . We found that there is a difference in how ICA and ILP achieve improvements , and that combining the two in a hybrid approach can lead to further overall improvement . Quantitatively , we showed that our approach is able to achieve a large increase in recall of the polar categories without harming the precision , which results in the performance improvements . Qualitatively , we illustrated how , even if the bootstrapping process is noisy , the optimization and discourse constraints effectively rectify the misclassifications . The improvements of our diverse global inference approaches indicate that discourse information can be adapted in different ways to augment and improve existing opinion analysis techniques . The automation of the discourse-relation recognition is the next step in this research . The behavior of ICA and ILP can change , depending on the automation of discourse level recognition . The implementation and comparison of the two methods under full automation is the focus of our future work .

Challenge: The utility of discourse schemas and annotated corpora motivated by discourse-based opinion analysis for effective computational analysis remains unknown.
Approach: They investigate the influences of different discourse schemes on opinion polarity classification performance when coupled with supervised and unsupervised inference to achieve discourse-based global inference.
Outcome: Both approaches investigated outperform baseline approaches showing the efficacy of exploiting discourse relations, and a combination of supervised and unsupervised performs the best.