{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T04:22:12Z","timestamp":1781670132991,"version":"3.54.5"},"reference-count":250,"publisher":"Emerald","issue":"2-3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,7,20]]},"abstract":"<jats:p>How can a single person understand what\u2019s going on in a collection of millions of documents? This is an increasingly widespread problem: sifting through an organization\u2019s e-mails, understanding a decade worth of newspapers, or characterizing a scientific field\u2019s research. This monograph explores the ways that humans and computers make sense of document collections through tools called topic models. 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.<\/jats:p>\n                  <jats:p>How can a single person understand what\u2019s going on in a collection of millions of documents? This is an increasingly common problem: sifting through an organization\u2019s e-mails, understanding a decade worth of newspapers, or characterizing a scientific field\u2019s 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\u2019 effective application to traditional problems like information retrieval, visualization, statistical inference, multilingual modeling, and linguistic understanding, this survey also reviews topic models\u2019 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.<\/jats:p>","DOI":"10.1561\/1500000030","type":"journal-article","created":{"date-parts":[[2017,7,20]],"date-time":"2017-07-20T10:00:48Z","timestamp":1500544848000},"page":"143-296","source":"Crossref","is-referenced-by-count":188,"title":["Applications of Topic Models"],"prefix":"10.1108","volume":"11","author":[{"given":"Jordan","family":"Boyd-Graber","sequence":"first","affiliation":[{"name":"Department of Computer Science, umiacs, Language Science University of Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuening","family":"Hu","sequence":"additional","affiliation":[{"name":"Google, Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Mimno","sequence":"additional","affiliation":[{"name":"Information Science Cornell University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2017,7,20]]},"reference":[{"key":"2026040903214167900_ref001","first-page":"1981","article-title":"Mixed membership stochastic blockmodels","volume":"9","author":"Airoldi","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"2026040903214167900_ref002","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/JCDL.2014.6970174","article-title":"Representing topics labels for exploring digital libraries","volume-title":"Proceedings of the IEEE\/ACM Joint Conference on Digital Libraries","author":"Aletras","year":"2014"},{"issue":"10","key":"2026040903214167900_ref003","article-title":"On paragraphs. scale, themes, and narrative form","volume":"1","author":"Algee-Hewitt","year":"2015","journal-title":"Stanford Literary Lab Pamphlets"},{"key":"2026040903214167900_ref004","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4615-0933-2","volume-title":"Topic Detection and Tracking: Event-based Information Organization","author":"Allan","year":"2002"},{"key":"2026040903214167900_ref005","doi-asserted-by":"crossref","DOI":"10.1109\/ICDM.2008.140","article-title":"On-line LDA: Adaptive topic models for mining text streams with applications to topic detection and tracking","volume-title":"International Conference on Data Mining","author":"AlSumait","year":"2008"},{"key":"2026040903214167900_ref006","article-title":"A spectral algorithm for latent Dirichlet allocation","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"Anandkumar","year":"2012"},{"key":"2026040903214167900_ref007","article-title":"Latent topic feedback for information retrieval","volume-title":"Knowledge Discovery and Data Mining","author":"Andrzejewski","year":"2011"},{"key":"2026040903214167900_ref008","doi-asserted-by":"crossref","DOI":"10.1145\/1553374.1553378","article-title":"Incorporating domain knowledge into topic modeling via Dirichlet forest priors","volume-title":"Proceedings of the International Conference of Machine Learning","author":"Andrzejewski","year":"2009"},{"key":"2026040903214167900_ref009","article-title":"A practical algorithm for topic modeling with provable guarantees","volume-title":"Proceedings of the International Conference of Machine Learning","author":"Arora","year":"2013"},{"key":"2026040903214167900_ref010","doi-asserted-by":"crossref","DOI":"10.1145\/2232817.2232861","article-title":"Topic models for taxonomies","volume-title":"Joint Conference on Digital Libraries","author":"Bakalov","year":"2012"},{"key":"2026040903214167900_ref011","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/P16-2087","article-title":"Nonparametric spherical topic modeling with word embeddings","volume-title":"Proceedings of the Association for Computational Linguistics","author":"Batmanghelich","year":"2016"},{"key":"2026040903214167900_ref012","doi-asserted-by":"crossref","DOI":"10.1002\/asi.23786","article-title":"Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence?","author":"Baumer","year":"2017","journal-title":"Journal of the Association for Information Science and Technology"},{"key":"2026040903214167900_ref013","doi-asserted-by":"crossref","DOI":"10.21437\/Eurospeech.1997-421","article-title":"A latent semantic analysis framework for large-span language modeling","volume-title":"European Conference on Speech Communication and Technology","author":"Bellegarda","year":"1997"},{"key":"2026040903214167900_ref014","article-title":"Statistical language model adaptation: review and perspectives","volume":"42","author":"Bellegarda","year":"2004"},{"key":"2026040903214167900_ref015","first-page":"1137","article-title":"A neural probabilistic language model","volume":"3","author":"Bengio","year":"2003","journal-title":"J. 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