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, aspect sentiment classification, semantic category assignment, and multi-level analytical modules to support region-level, hotel-level, and category-level evaluation. The system is designed to operate across different geographic contexts and hospitality settings. To demonstrate its practical utility, we apply SentimentLens to a large real-world dataset of over 10,000 publicly available hotel reviews. Through extensive analysis, the framework reveals how traveler sentiment varies across regions, service categories, and hotel archetypes. We further implement a cross-modal reconciliation of textual sentiment and numerical ratings to identify latent operational conflicts, structural inconsistencies in service quality, and high-impact improvement opportunities using importance--performance and entropy-based analyses. The results show that SentimentLens effectively transforms large-scale unstructured reviews into actionable intelligence, supporting data-driven decision-making for hospitality management and tourism policy. While demonstrated using a national case study, the proposed system is generalizable to other destinations and review-driven service domains.

翻译：摘要：在线旅行平台产生了海量的用户生成酒店评论，为规模化理解旅行者体验提供了丰富机会。然而，将非结构化文本反馈转化为结构化、可操作的知识仍是一项具有挑战性的任务。本文提出SentimentLens——一个基于方面级别情感分析的可扩展分析系统，该系统从非结构化酒店评论中执行知识提取，并将其组织成可解释的服务类别。SentimentLens整合了方面术语提取、方面情感分类、语义类别分配及多层次分析模块，以支持区域级、酒店级和类别级评估。该系统设计为可跨越不同地理背景和酒店环境运行。为展示其实用价值，我们将SentimentLens应用于一个包含超过10,000条公开酒店评论的大型真实数据集。通过广泛分析，该框架揭示了旅行者情感如何随区域、服务类别及酒店类型变化。我们进一步实施文本情感与数值评分的跨模态调和，以识别潜在运营冲突、服务质量的结构性不一致性，并利用重要性-绩效及基于熵的分析发现高影响力的改进机会。结果表明，SentimentLens能有效将大规模非结构化评论转化为可操作情报，支持酒店管理与旅游政策的数据驱动决策。虽然基于国家案例研究进行演示，但所提出的系统可推广至其他目的地及评论驱动的服务领域。