关键词: 商家排序, 消费决策, 在线评论, 直觉模糊集, 方面级情感分析

Abstract:

How to make full use of the sentiment information in online word-of-mouth and consumers’ personalized preferences to achieve online merchant ranking with high accuracy is of great practical significance and theoretical value to improve the efficiency of consumers’ decision making in massive information. In this paper, a ranking method is proposed based on deep learning that considers fuzzy sentiment, personalized preferences, incorporates online reviews, ratings, and popularity. First, an aspect-level sentiment classification model based on dual-attention BILSTM (Bi-directional long short-term memory) is constructed to identify the sentiment orientation of online reviews. Then, the sentiment value is converted to IFV (intuitionistic fuzzy values), and the intuitionistic fuzzy TOPSIS (technique for order preference by similarity to an ideal solution) method is used to calculate the closeness degree. After that, the attribute weighted score and popularity value are calculated based on consumer ratings and the number of reviews. Finally, the ranking of alternative restaurants is determined by integrating the three factors. Comparative experiments with standard datasets show that the dual-attention BILSTM model outperforms traditional models such as LR, SVM, BILSTM, and single-attention BILSTM models. The ranking results of eight restaurants in the example analysis have higher average overlap scores with the platform ranking, which indicates the effectiveness of the merchant ranking method proposed in this paper.

Key words: merchant ranking, consumption decision-making, online reviews, intuitionistic fuzzy sets (IFS), aspect-based sentiment analysis