Title: Generative Sentiment Classification Model Affiliating Domain-Specific Sentiment Lexicons
Abstract: Sentiment classification focuses on learning a classification model from labeled opinion text, which can predict the sentiment polarity in other opinion text. In this field, one important research topic is to combine the prior knowledge with a generative classifier to construct a new model. By studying the domain character and weight of sentiment lexicons, this paper proposes an approach to automatically construct domain-specific sentiment lexicons which can be reformulated as a generative prior, and then combine it with a generative model. Experimental results show that the proposed generative model performs significantly and consistently better than some of the-state-of-the-art domain-independent methods.
Publication Year: 2011
Publication Date: 2011-01-01
Language: en
Type: article
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