SENTIMENT ANALYSIS MODEL FOR EVALUATING SERVICE QUALITY BASED ON USER REVIEWS ON TOURISM PLATFORMS
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Abstract
The rapid development of online tourism platforms has transformed the way travelers select destinations, accommodations, and tourism services. User-generated reviews on platforms such as TripAdvisor, Booking.com, Airbnb, and Google Reviews provide valuable insights into customer experiences and service quality. Traditional methods of evaluating service quality often rely on surveys and questionnaires, which may be time-consuming and limited in scope. Sentiment analysis, a branch of Natural Language Processing (NLP), offers an effective approach to automatically extract opinions and emotions from textual reviews. This study proposes a sentiment analysis model for evaluating tourism service quality based on user reviews. The model utilizes machine learning and NLP techniques to classify customer sentiments and identify key factors influencing customer satisfaction. The findings demonstrate the potential of sentiment analysis in improving tourism service management and enhancing customer experience.
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References
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