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This paper explores the problem of content-based rating inference from online opinion-based texts, which often expresses differing opinions on multiple aspects. To sufficiently capture information from various aspects, we propose an aspect-based segmentation algorithm to first segment a user review into multiple single-aspect textual parts, and an aspect-augmentation approach to generate the aspect-specific feature vector of each aspect for aspect-based rating inference. To tackle the problem of inconsistent rating annotation, we present a tolerance-based criterion to optimize training sample selection for parameter updating during the model training process. Finally, we present a collaborative rating inference model which explores meaningful correlations between ratings across a set of aspects of user opinions for multi-aspect rating inference. We compared our proposed methods with several other approaches, and experiments on real Chinese restaurant reviews demonstrated that our approaches achieve significant improvements over others.
Date of Publication: Fourth Quarter 2012