SentimentLens: Reconciling Sentiment and Ratings via Dual-Modality in the Hospitality Sector
arXiv:2606.00084v1 Announce Type: cross Abstract: Online travel platforms generate vast volumes of user-generated hotel reviews, offering rich opportunities to understand traveler experiences at scale. However, transforming unstructured textual feedback into structured, actionable insights remains a challenging task. This paper presents SentimentLens, a scalable analysis system based on Aspect-Based Sentiment Analysis that performs knowledge extraction from unstructured hotel reviews and organizes them into interpretable service categories. SentimentLens integrates aspect term extraction, aspe
The proliferation of user-generated content and advancements in AI-driven natural language processing make automated sentiment analysis increasingly viable and necessary for businesses.
This development showcases AI's increasing capability to extract granular, actionable insights from unstructured data, moving beyond simple sentiment to aspect-based analysis for improved operational decisions.
Businesses in sectors reliant on customer feedback can now leverage advanced AI tools to automatically categorize and prioritize feedback, allowing for more targeted service improvements.
- · Hospitality sector
- · Online travel platforms
- · AI/NLP software providers
- · Customer experience management firms
- · Traditional manual survey companies
- · Generic sentiment analysis tools
Hotels and services can use SentimentLens to identify specific pain points and improve guest satisfaction more efficiently.
Improved customer experience could lead to higher loyalty and increased revenue for early adopters of such advanced analytics systems.
The widespread adoption of granular sentiment analysis could create new industry standards for service quality measurement and competitive benchmarking.
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Read at arXiv cs.CL