A Novel Interval Type-2 Fuzzy AHP-TOPSIS Approach For Ranking Reviewers in online communities


K.N. Toosi University of Tech


Online product review websites have become excellent platforms for customers to share their opinions about a variety of products and services in the form of online reviews. Despite being an invaluable source of information for both consumers and firms, the quality of online reviews varies greatly. To tackle the problem of low quality reviews, in this paper, we address reviewer credibility problem and propose a novel framework for ranking reviewers in terms of credibility based on interval type-2 fuzzy analytical hierarchy (IT2 FAHP) and technique for order performance by similarity to ideal solution (TOPSIS). The novel IT2 FAHP is used to determine weights of features representing reviewers where the interval type-2 trapezoidal fuzzy numbers are used predominantly and TOPSIS method is used to obtain final ranking of reviewers. To illustrate an application of the proposed framework, we conducted an experimental study using real data crawled from Epinions. This proposed framework provides a more effective and systematic approach especially for firms to find high-credible reviewers and to select their reviews and opinions for opinion mining.