Extraction of user opinions by adjective-context co-clustering for game review texts

Abstract

We present our preliminary work on extracting fine-grained user opinions from game review texts. In sentiment analysis, user-generated texts such as blogs, comments and reviews are usually represented by the words which appeared in the texts. However, for complex multi-faceted objects such as games, single words are not sufficient to represent opinions on individual aspects of the object. We propose to represent such an object by pairs of aspect and each aspect's quality/value, for example great-graphics. We used a large adjective-context co-occurrence matrix extracted from user reviews posted at a game site, and applied co-clustering to reduce the dimensions of the matrix. The derived co-clusters are pairs of row clusters × column clusters. By examining the derived co-clusters, we were able to discover the aspects and their qualities which the users care about strongly in games. © 2012 Springer-Verlag Berlin Heidelberg.