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      Computational Sociolinguistics: A Survey

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      Computational Linguistics

      MIT Press - Journals

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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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            I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience

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              Identity Work Among the Homeless: The Verbal Construction and Avowal of Personal Identities

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                Author and article information

                Journal
                Computational Linguistics
                Computational Linguistics
                MIT Press - Journals
                0891-2017
                1530-9312
                September 2016
                September 2016
                : 42
                : 3
                : 537-593
                Article
                10.1162/COLI_a_00258
                © 2016

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