A combined analysis system for network products based on aspect extraction and sentiment classification

Abstract

With the continuous development of online shopping, more and more online shopping review data has been accumulated. These reviews contain consumers' attitudes and opinions on the products, which are of great value for mining. This research introduces a set of methods to mine review information by using aspect extraction and sentiment analysis. By analyzing the aspects involved in the reviews and the sentiment polarity of the reviewers, the potential opinions on the products can be summarized and presented to the merchants so as to understand consumers' preferences and make reasonable improvements to the products. This research combines and adopts several techniques, such as aspect extraction, paragraph vector and sentiment classification, to build a complete process which can handle review text and get the results of review aspects and sentiment polarity. By taking an Amazon review dataset as an example and applying this process, the relevant aspects and sentiment polarities contained in the reviews are obtained, which reveals that the idea of this research is feasible.