How can a single person understand what’s going on in a collection of millions of documents? This is an increasingly common problem: sifting through an organization’s e-mails, understanding a decade worth of newspapers, or characterizing a scientific field’s research. Topic models are a statistical framework that help users understand large document collections: not just to find individual documents but to understand the general themes present in the collection. This survey describes the recent academic and industrial applications of topic models with the goal of launching a young researcher capable of building their own applications of topic models. In addition to topic models’ effective application to traditional problems like information retrieval, visualization, statistical inference, multilingual modeling, and linguistic understanding, this survey also reviews topic models’ ability to unlock large text collections for qualitative analysis. We review their successful use by researchers to help understand fiction, non-fiction, scientific publications, and political texts.

一个人怎么能理解成千上万份文档中的内容？这是一个日益普遍的问题：浏览组织的电子邮件，了解十​​年的报纸价值或描述科学领域的研究特征。主题模型是一个统计框架，可以帮助用户了解大型文档集合：不仅可以查找单个文档，还可以理解集合中存在的一般主题。这项调查描述了主题模型的最新学术和工业应用，其目的是要培养能够构建自己的主题模型应用程序的年轻研究人员。除了主题模型可以有效地应用于信息检索，可视化，统计推断，多语言建模和语言理解等传统问题之外，这项调查还回顾了主题模型解锁大文本集进行定性分析的能力。我们审查了研究人员对它们的成功使用，以帮助理解小说，非小说，科学出版物和政治著作。