SentimentLens: Reconciling Sentiment and Ratings via Dual-Modality in the Hospitality Sector

SentimentLens is an AI system that uses aspect-based sentiment analysis to extract insights from hotel reviews, converting unstructured text into actionable intelligence for hospitality management. The framework reconciles textual sentiment with numerical ratings across 10,000+ reviews to identify service inconsistencies and operational improvement opportunities.

SentimentLens addresses a fundamental challenge in the hospitality sector: converting massive volumes of unstructured customer feedback into structured, actionable business intelligence. The system employs advanced natural language processing techniques to perform aspect-term extraction, sentiment classification, and semantic categorization—transforming raw reviews into multi-level analytical frameworks that operate at regional, hotel, and service-category levels. This approach goes beyond simple sentiment scoring by identifying discrepancies between what customers write and how they numerically rate experiences, revealing hidden operational conflicts and quality gaps that traditional metrics miss.

The broader context reflects a growing industry trend toward data-driven hospitality management. As online travel platforms accumulate unprecedented volumes of user-generated content, hospitality operators face immense difficulty extracting competitive advantage from this unstructured data. SentimentLens fills this gap by automating knowledge extraction at scale, enabling region-specific and property-specific insights that drive targeted improvements.

For the hospitality and tourism sectors, this framework carries significant practical implications. Hotels can identify which service categories generate the largest sentiment-rating discrepancies, pinpointing where customer expectations diverge from actual experiences. Tourism boards and destination management organizations gain region-level competitive intelligence. The importance-performance and entropy-based analyses highlight high-impact improvement opportunities, helping operators allocate limited resources toward changes with maximum customer satisfaction ROI.

The system's demonstrated generalizability to different geographic contexts and review platforms suggests potential expansion beyond hospitality. Similar dual-modality reconciliation approaches could enhance decision-making in restaurants, airlines, and other service industries dependent on customer reviews.

• →SentimentLens converts unstructured hotel reviews into multi-level insights across regional, hotel, and service-category dimensions using aspect-based sentiment analysis.
• →The system identifies latent conflicts between textual sentiment and numerical ratings, revealing hidden service quality gaps that traditional metrics overlook.
• →Cross-modal reconciliation and entropy-based analysis pinpoint high-impact operational improvements for hospitality managers and tourism policy makers.
• →The framework demonstrates scalability across geographic contexts and review-driven service domains beyond hospitality.
• →Data-driven analysis of 10,000+ reviews reveals how traveler sentiment varies systematically across regions, hotel types, and service categories.