Abstract
Customer reviews on a product regard multi-aspect with emotional tendencies. Aspects in a review show what properties customers concern about and sentiment towards an aspect reveals how a customer evaluates it. The aspect mining and sentiment analysis provides a lot of valuable references and market feedback information to online commercial platforms. Due to the unpredictability of aspects appearing in a review, the method proposed in this paper is supposed to be dynamic and intelligent and to define the sentiment related to an aspect negative or positive polarity in semantic analysis. Based on the improved aspect dictionary and sentiment dictionary, this paper presents a framework for aspect mining and sentiment analysis for online customer reviews-PowerMonitor. The experimental results show that the framework performs well in aspect extraction and aspect emotion judgment. We evaluate the model using small, widely used sentiment and subjectivity corpora from JD.com and find it out-performs several previously introduced methods for sentiment classification. We also introduce future works to serve as a reference for efforts in this area.
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