Applications of Topic Models by Jordan Boyd-Graber, Yuening Hu,David Mimno. (Jordan Boyd-Graber, Yuening Hu and David Mimno (2017), “Applications of Topic Models”, Foundations and Trends® in Information Retrieval: Vol. 11: No. 2-3, pp 143-296. http://dx.doi.org/10.1561/1500000030)
Abstract:
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.
The authors discuss the use of topic models for, 4. Historical Documents, 5. Understanding Scientific Publications, 6. Fiction and Literature, 7. Computational Social Science, 8. Multilingual Data and Machine Translation, and provide further guidance in: 9. Building a Topic Model.
If you have haystacks of documents to mine, Applications of Topic Models is a must have on your short reading list.