An integrated method for product ranking through online reviews based on evidential reasoning theory and stochastic dominance

Online reviews play an important role in consumers' purchasing decisions. However, many online reviews confuse consumers when they wish to make a purchase but lack experience. To solve the problem of product ranking based on online reviews, two important issues must be addressed: sentiment analysis and product ranking based on multi-criteria decision-making (MCDM) methods. Therefore, this paper proposes an integrated MCDM method for product ranking through online reviews based on evidential reasoning (ER) the-ory and stochastic dominance (SD) rules. First, online reviews are preprocessed to obtain product attributes and weight values. Then, we use naive Bayes (NB), logistic regression (LR), and support vector machines (SVM) for the sentiment analysis of online reviews, and the results of the three classifiers are aggregated using ER theory. In addition, accord-ing to the confidence distribution matrix of sentiment orientations, SD rules are used to determine the stochastic dominance relations between pairwise alternatives for each attri-bute. Furthermore, we use the stochastic multi-criteria acceptability analysis (SMAA)-PROMETHEE method to obtain the final product ranking results and conduct sensitivity analysis. Finally, a case study on ranking computer products from JD Mall through online reviews is provided to illustrate the validity of the proposed method.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

Online reviews play a significant role in consumers' purchasing decisions, but they can be confusing for inexperienced buyers. This paper addresses the issues of sentiment analysis and product ranking based on multi-criteria decision-making methods in order to solve the problem of product ranking using online reviews. The proposed integrated method utilizes evidential reasoning theory and stochastic dominance rules to rank products based on online reviews, and it has been validated through a case study on computer products from JD Mall.