This study investigates the semantic alignment between Airbnb property descriptions and guest reviews. Word2Vec embeddings and affinity propagation clustering are used to identify granular semantic concepts, enabling a detailed comparison of the two text types. A new metric, concept coverage ratio, is introduced to measure the extent to which the guest review content is reflected in property descriptions. Results show that a higher concept coverage ratio is generally associated with more positive sentiment in reviews, suggesting that better alignment between host and guest perspectives contributes to guest satisfaction. However, longer and detailed descriptions may limit the potential for pleasantly surprising guests, as it reduces the chance for positive disconfirmation. These findings offer practical insights for improving communication in peer-to-peer accommodation.

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Airbnb,Semantic similarity,Clustering,Word embeddings,Sentiment analysis

• Economics and Business - Social Sciences
• Sociology - Social Sciences
• Social and Economic Geography - Social Sciences
• Other Social Sciences - Social Sciences