We present CritLens, a visual analytics system that helps users build personalized multi-criteria decision models from review text. In everyday decisions -- choosing equipment, hotels, or restaurants -- evaluation criteria are either preset by platforms or generated by LLMs, leaving users unable to discover, adjust, or verify them against the underlying evidence. This is problematic because many preferences are latent: they surface only upon encountering specific reviews, and any fixed framework risks overlooking low-frequency but decisive details. CritLens addresses this gap by using LLMs to transform reviews into an initial AHP decision model, then supporting iterative, human-in-the-loop refinement. Through coverage gap detection in the embedding space, users discover criteria missed by the initial model; through interactive weight adjustment under AHP consistency constraints, they express personal priorities; and through a multi-level scorecard and exportable decision report, they trace every ranking back to the original review text. Two case studies, an eight-participant user study, and a quantitative consistency-repair experiment demonstrate the system's effectiveness.

翻译：我们提出CritLens，一个帮助用户从评论文本构建个性化多标准决策模型的可视分析系统。在日常决策中——选择设备、酒店或餐厅——评估标准要么由平台预设，要么由大语言模型生成，导致用户无法发现、调整或验证这些标准背后的证据支持。这存在根本性缺陷，因为许多偏好具有潜在性：仅当用户遇到特定评论时才会显现，而任何固定框架都可能遗漏低频但关键细节。CritLens通过大语言模型将评论转化为初始层次分析法决策模型，并支持迭代式人在回路精炼来弥补这一空白。通过嵌入空间中的覆盖缺口检测，用户可发现初始模型遗漏的标准；通过层次分析法一致性约束下的交互式权重调整，用户可表达个人优先级；通过多层级计分卡和可导出决策报告，用户可将每个排名回溯至原始评论文本。两项案例研究、一项八参与者用户研究及一项量化一致性修复实验验证了该系统的有效性。