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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.