earticle

영어

This study demonstrates how online hotel reviews can classify customer loyalty by analyzing textual features such as sentiment, rating, and loyalty keywords. Using a review dataset from TripAdvisor, the research applies sentiment analysis tools (VADER, TextBlob, SenticNet) and topic modeling techniques to classify loyalty. Results show that loyalty keywords imply a significant difference in their presence between loyal and non-loyal customers. At the same time, a considerable difference in their presence between loyal and non-loyal customers, while sentiment scores present significantly moderate results. However, Rao-Striling diversity and review length do not exhibit a significant difference. The study contributes a framework for using review content to identify loyal customers, offering practical methods for businesses to enhance customer retention and improve CRM strategies. Limitations include reliance on sentiment scores for loyalty classification and the exclusion of behavioral data, suggesting future research could incorporate more comprehensive customer data for validation.