[目的/意义] 旨在探索从非结构化用户生成内容中提取及可视化用户情绪的方法，从感知层面深入分析用户生成的内容，对其应用前景进行探讨与展望。[方法/过程] 以豆瓣网站书籍评论为分析对象，借助中文领域的情绪词典与LDA隐主题建模方法实现细粒度情感要素提炼，并采用可视化技术对评论内容中反映的情绪要素进行分析。[结果/结论] 研究发现，主题分析法和词典法均能有效提炼评论内容中的用户情感要素，但存有差异，情感主题建模能够提供更细腻的用户情绪以及感知信息。通过应用场景的微调，本研究所涉及方法可应用于体验型产品推荐等多种形式的评论感知效用挖掘任务。

[Purpose/significance] This study aims to explore the methods on extracting and visualizing users’ emotions from unstructured user-generated content, analyze user-generated content from a perceptual level, and discuss the related application prospects. [Method/process] The research took book reviews of Douban as analysis object. Emotional dictionary in Chinese domain and LDA latent topic model were used to refine the fine-grained emotional elements. And further, visualization techniques helped to analyze the emotional elements reflected in the review content. [Result/conclusion] The study found that both latent topic model and emotion dictionary can effectively extract the user emotion elements in the content of the review, even though some difference still exists, such as the emotional topic model can provide more exquisite results. By fine-tuning the application scenario, the methods used in this study can be applied to various forms of perceived utility mining tasks about reviews, like experience-based products recommendation.