Ranking products through online reviews considering the mass assignment of features based on BERT and q-rung orthopair fuzzy set theory

A product ranking method is an effective tool that can analyze a significant number of online product reviews to recommend suitable products to consumers. However, existing product ranking methods have two main limitations: (1) the high manual annotation costs and (2) the inability to express consumers’ purchasing decisions because the information is limited to a single feature of each product.

To overcome the limitations, this paper proposes a novel product ranking method considering the mass assignment of features based on bidirectional encoder representations using transformers (BERT) and q-rung orthopair fuzzy set theory. First, BERT is adopted to identify sentiment orientations of online product reviews and product features from online product reviews.

Subsequently, the product features are clustered into groups and the relative frequencies of product features are obtained. Second, the relative frequencies of product features are transformed into q-rung orthopair fuzzy numbers based on mass assignment theory. Third, the q-rung orthopair fuzzy numbers are aggregated by the q-rung orthopair fuzzy generalized weighted Heronian mean operator to rank the products.

Finally, we implement the method using a case study of six different phones to verify its feasibility. Using the case study, we also perform comparisons and sensitivity analyses, which demonstrate the superiority of our method.