Online reviews in which users publish detailed commentary about their experiences and opinions with products, services, or events are extremely valuable to users who rely on them to make informed decisions. However, reviews vary greatly in quality and are con-stantly increasing in number, therefore, automatic assessment of review helpfulness is of growing importance. Previous work has addressed the problem by treating a review as a stand-alone docu-ment, extracting features from the review text, and learning a func-tion based on these features for predicting the review quality. In this work, we exploit contextual information about authors ’ iden-tities and social networks for improving review quality prediction. We propose a generic framework for incorporating social context information by adding regularization constraints to the text-based predictor. Our approach can effectively use the social context infor-mation available for large quantities of unlabeled reviews. It also has the advantage that the resulting predictor is usable even when social context is unavailable. We validate our framework within a real commerce portal and experimentally demonstrate that using social context information can help improve the accuracy of re-view quality prediction especially when the available training data is sparse.

A correct structural analysis of a discourse is a prerequisite for understanding it. This paper sketches the outline of a discourse grammar which acknowledges several different levels of structure. This grammar, the "Dynamic Discourse Model", uses an Augmented Transition Network parsing mechanism to build a representation of the semantics of a discourse in a stepwise fashion, from left to right, on the basis of the semantic representations of the individual clauses which constitute the discourse. The intermediate states of the parser model the intermediate states of the social situation which generates the discourse.The paper attempts to demonstrate that a discourse may indeed be viewed as constructed by means of sequencing and recursive nesting of discourse constituents. It gives rather detailed examples of discourse structures at various levels, and shows how these structures are described in the framework proposed here.