论文标题
帮助用户发现观点：通过联合主题模型增强意见采矿
Helping users discover perspectives: Enhancing opinion mining with joint topic models
论文作者
论文摘要
关于堕胎之类的辩论主张的支持或反对应为合法的辩论，可能会有不同的根本原因，我们称之为观点。本文探讨了如何通过联合主题建模来增强意见挖掘，以确定主题中的不同观点，从而提供了来自非结构化文本的信息概述。我们在一项评估人类对提取观点的可理解性的用户研究中评估了四个联合主题模型（TAM，JST，Vodum和Lam）。根据结果，我们得出结论，诸如TAM之类的联合主题模型可以发现与人类判断保持一致的观点。此外，我们的结果表明，在解释主题模型的输出时，用户不受对堕胎主题的现有立场的影响。
Support or opposition concerning a debated claim such as abortion should be legal can have different underlying reasons, which we call perspectives. This paper explores how opinion mining can be enhanced with joint topic modeling, to identify distinct perspectives within the topic, providing an informative overview from unstructured text. We evaluate four joint topic models (TAM, JST, VODUM, and LAM) in a user study assessing human understandability of the extracted perspectives. Based on the results, we conclude that joint topic models such as TAM can discover perspectives that align with human judgments. Moreover, our results suggest that users are not influenced by their pre-existing stance on the topic of abortion when interpreting the output of topic models.
