A graphical framework to detect and categorize diverse opinions from online news

This paper proposes a framework to extract diverse opinionated sentences within a given news article, by introducing the concept of diversity in a graphical model for opinion detection. We conduct extensive evaluation and find that the proposed modification leads to impressive improvements in performance and makes the final results of the model more usable. The proposed method (OP-D) not only performs much better than the other techniques used for opinion detection and introducing diversity, but is also able to select opinions from different categories (Asher et al., 2009). By developing a classification model which categorizes the identified sentences into various opinion categories, we find that OP-D is able to push opinions from different categories uniformly among the top opinions.