Fuzzy and Crisp Recursive Profiling of Online Reviewers and Businesses

Users of online review sites can benefit from knowing the profiles of the businesses, as well as the profiles of reviewers who reviewed the businesses. This paper describes crisp and fuzzy metaclustering techniques to evolve two recursively defined clustering schemes of both businesses and reviewers in parallel using a real-world dataset supplied by yelp.com. The objective is to profile the businesses and reviewers by grouping them based on similar characteristics. The novelty of the proposed approach is in the fact that the representations of both businesses and reviewers change dynamically throughout the metaclustering process. A business is represented by static information obtained from the database and dynamic information obtained from the clustering of reviewers who reviewed the business. Similarly, the reviewer representation augments the static representation from the database with profiles of businesses who are reviewed by these reviewers. The resulting web-based service provides a facility for users to find similar businesses/reviewers based on the category of the business, rating, number of reviews, and number of check-ins. It also provides a succinct profile of a business or reviewer based on these factors so that the users can put the reviews in context. Since an object can belong to multiple clusters in fuzzy metaclustering, it is possible to absorb some of the extreme groups consisting of outliers in one of the mainstream clusters. As a result, the fuzzy metaclustering leads to more uniformly distributed and moderate profiles.