1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 2. University of Chinese Academic of Sciences, Beijing 100190, China
Abstract：This paper addresses the problem of learning aspect-dependent sentiment knowledge. Specifically, a novel topic model, called Joint Aspect/Opinion Model (JAO), is proposed to detect aspects and aspect-specific opinion words simultaneoasly in an unsupervised manner. Then, we propose to infer aspect-dependent sentiment polarity scores for these opinion words based on the hitting times from the words to a handful of positive/negative seed words, by applying Markov random walks over an aspect-specific word relation graph. Experimental results on restaurant review data show the effectiveness of the proposed approaches.
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2015, Vol. 29 
