Abstract
Online reviews can assist customers in making purchasing decisions, however, when multiple alternative products need to be compared based on online reviews, a large number of reviews information cannot be processed manually. Therefore, this study develops a novel method for product ranking based on mining online reviews and an interval-valued intuitionistic fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Concretely, it contains three major steps: (1) preprocessing, (2) calculating sentiment scores, and (3) ranking alternative products. First, the results of preprocessing are vectorization representation of online reviews, which are obtained with the Bidirectional Encoder Representations from Transformers (BERT) model. Then, sentiment orientations of online reviews of alternative products regarding different attributes are determined by a multiple classifiers system, which consists of multi-class base classifiers constructed using Support Vector Machine (SVM) with One-Vs-One (OVO) strategy. Lastly, based on the sentiment scores, the ranking result is obtained by the interval-valued intuitionistic fuzzy TOPSIS method. A case study on real-world datasets is provided to illustrate the application of our proposed method, which indicates the validity of our proposal. Thus, the method proposed in this paper can effectively assist consumers in selecting products based on online reviews that meet their preferences.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
