Abstract
Massive online reviews contain a lot of useful information that can not only provide purchasing decision support for consumers, but also allow producers and suppliers to understand the competitive market. This paper proposes a new aspect-based online reviews mining method, which combines both textual data and numerical data. Firstly, the probability distribution of topics and words is constructed by LDA topic model. With word cloud images, the keywords are visualized and corresponding relationship between LDA topics and product reviews is analyzed. The weight of each aspect is calculated based on the probability distribution of documents and topics. Then, the dictionary-based approach is used to calculate the objective sentiment values of the product. The subjective sentiment tendency from different consumers because of their different individual needs are also taken into consideration. Finally, the directed graph model is constructed and the importance of each node is calculated by improved PageRank algorithm. The experimental results illustrate the feasibility of proposed mining method, which not only makes full use of massive online reviews, but also considers individual needs of consumers. It provides a new research idea for online customer review mining and personalized recommendation.
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