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      Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

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          Finding scientific topics.

          A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.
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            The Pascal Visual Object Classes (VOC) Challenge

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              Dynamic topic models

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                Author and article information

                Journal
                Multimedia Tools and Applications
                Multimed Tools Appl
                Springer Science and Business Media LLC
                1380-7501
                1573-7721
                June 2019
                November 28 2018
                June 2019
                : 78
                : 11
                : 15169-15211
                Article
                10.1007/s11042-018-6894-4
                2bd82f8b-1d11-4a71-8be8-39acdb919101
                © 2019

                http://www.springer.com/tdm


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