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An increasing number of people are choosing to shop online; hence, online reviews are an increasingly influential factor in consumer purchasing decisions. However, extracting useful information from online reviews is a challenge in the analysis of consumer sentiment. In this paper, we focus on the automatic discovery of the features evaluated in online reviews and the expression of sentiment. We propose a novel fine-grained topic model called the &#x201C;review unit topic model&#x201D; (RUTM) to extract semantic meanings and polarities. In this model, a review unit rather than a review sentence is treated as the representational model, and prior knowledge of sentiment is further exploited to identify aspect-aware sentiment polarities. We evaluate RUTM extensively using real-world review data. Experimental results demonstrate that the proposed model outperforms well-established baseline models in sentiment analysis tasks.

Mining Review Unit Model for Online Review Analysis