Online-Review-Driven Products Ranking: A Hybrid Approach

Abstract

Online customer reviews (OCRs) are the real feelings of customers in the process of using products, which have great reference value for potential customers’ purchase decisions. However, it is difficult for consumers to extract helpful information from very large numbers of OCRs. To support consumers’ purchase decisions, this paper proposes a hybrid method to rank alternative products through OCRs. In this method, we use the fine-grained Bidirectional Encoder Representation from Transformers (BERT) model for aspect-level sentiment analysis (SA) and convert SA results of sub-criteria into a corresponding interval intuitionistic fuzzy number, accurately extracting customer satisfaction in OCRs and reducing the errors caused by different amounts of OCRs. Furthermore, in order to obtain the ranking results of products, the subjective and objective weights are combined to determine weight of feature. Subsequently, an improved interval intuitionistic fuzzy VIKOR method is proposed to rank mobile games. Finally, we conduct a case study and make some comparisons, which show that our method can reduce the complexity of accurately obtaining consumers’ personal preferences and help consumers make more accurate decisions.