Α ranking model based on user generated content and fuzzy logic

User Generated Comments and ranking based on it, has been studied extensively due to its impact on consumers and businesses. Literature highlights that platforms’ rankings might be deceiving due to fake reviews or rates that do not correspond to reality, but existing ranking models are a ‘black box’ thus, they cannot be assessed. This study fills in this gap by proposing a step-by-step ranking model which is based on comments rather than rates or the theoretically established criteria (e.g., food, service, atmosphere), which are not capable to portray the complexity of the dining experience. The proposed ranking model extracts topics from qualitative data (documents) and converts them into quantitative values through the lens of fuzzy logic. The topics are used as the ranking space while, pairwise comparisons between items (restaurants) are performed to extract a ranking ladder. We applied our model to TripAdvisor’s documents from restaurants in Athens, Greece. •Proposes a ranking approach/model to extract topics from qualitative user data.•Converts qualitative data to quantitative to perform the ranking using a fuzzy logic.•Evaluated the method using TripAdvisor data from 290 restaurants located in Athens, Greece.•Identifies the criteria e.g. food, service, atmosphere that affect the ranking of the restaurants.