Paper Type
ERF
Abstract
This study develops an AI-driven framework to assess hotel service quality from online reviews. By using a large corpus of TripAdvisor hotel reviews, we apply BERTopic to uncover latent service topics at the sentence level and a transformer-based sentiment model to classify sentiment polarity (positive, neutral, and negative) and generate continuous sentiment scores. Topics are then mapped to the service quality model SERVQUAL dimensions (Tangibles, Reliability, Responsiveness, Assurance, Empathy). For each review, we compute dimension salience (coverage) and performance (mean sentiment) and derive weighted indicators to capture both what guests discuss and how they feel. We use a regression model to estimate each dimension’s contribution to overall ratings, with review length included as a control variable. This approach enables scalable, interpretable, near real-time service quality monitoring for hotel managers.
Paper Number
1350
Recommended Citation
LEI, NI; Tian, Xin; and Li, Zhigang, "Leveraging AI Sentiment Analysis to Improve Hotel Service Quality Based on Online Customer Reviews" (2026). AMCIS 2026 Proceedings. 1.
https://aisel.aisnet.org/amcis2026/sig_odis/sig_odis/1
Leveraging AI Sentiment Analysis to Improve Hotel Service Quality Based on Online Customer Reviews
This study develops an AI-driven framework to assess hotel service quality from online reviews. By using a large corpus of TripAdvisor hotel reviews, we apply BERTopic to uncover latent service topics at the sentence level and a transformer-based sentiment model to classify sentiment polarity (positive, neutral, and negative) and generate continuous sentiment scores. Topics are then mapped to the service quality model SERVQUAL dimensions (Tangibles, Reliability, Responsiveness, Assurance, Empathy). For each review, we compute dimension salience (coverage) and performance (mean sentiment) and derive weighted indicators to capture both what guests discuss and how they feel. We use a regression model to estimate each dimension’s contribution to overall ratings, with review length included as a control variable. This approach enables scalable, interpretable, near real-time service quality monitoring for hotel managers.
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