From reviews to perceived pricing: A sentiment analysis of hotel experiences

Purpose – User-generated content provides valuable insights into customer sentiment, influencing service delivery and pricing decisions. However, limited empirical research has examined how customer sentiment relates to perceived hotel pricing. This study explores the relationship between sentiment expressed in online reviews and customers’ perceptions of hotel prices, identifying key service attributes associated with these perceptions. Methodology/Design/Approach – This study employs an innovative approach using Synthesio, an AI-powered social intelligence platform, to conduct large-scale sentiment analysis. A dataset of 30,500 TripAdvisor reviews was analyzed to examine how sentiment toward specific service attributes relates to perceived pricing. Findings – Five attributes—room, staff service, food experience, location, and hotel facilities—were identified as key factors associated with price perceptions. Room quality, particularly cleanliness, showed the strongest association with pricing sentiment. Although staff service was generally positive, it did not consistently offset dissatisfaction caused by poor room quality. Location, as a relatively fixed attribute, influenced sentiment primarily when expectations were unmet. Originality of the research – This study contributes to data-driven pricing research by applying attribute-level sentiment analysis to examine perceived pricing, offering both methodological innovation and practical implications for the hospitality industry.