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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2012: Machine Learning and Knowledge Discovery in Databases pp 7–8Cite as

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Analyzing Text and Social Network Data with Probabilistic Models

Analyzing Text and Social Network Data with Probabilistic Models

  • Padhraic Smyth20 
  • Conference paper
  • 4449 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7523)

Abstract

Exploring and understanding large text and social network data sets is of increasing interest across multiple fields, in computer science, social science, history, medicine, and more. This talk will present an overview of recent work using probabilistic latent variable models to analyze such data. Latent variable models have a long tradition in data analysis and typically hypothesize the existence of simple unobserved phenomena to explain relatively complex observed data. In the past decade there has been substantial work on extending the scope of these approaches from relatively small simple data sets to much more complex text and network data. We will discuss the basic concepts behind these developments, reviewing key ideas, recent advances, and open issues. In addition we will highlight common ideas that lie beneath the surface of different approaches including links (for example) to work in matrix factorization. The concluding part of the talk will focus more specifically on recent work with temporal social networks, specifically data in the form of time-stamped events between nodes (such as emails exchanged among individuals over time).

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Authors and Affiliations

  1. University of California, Irvine, USA

    Padhraic Smyth

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  1. Padhraic Smyth
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Editors and Affiliations

  1. Intelligent Systems Laboratory, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Peter A. Flach, Tijl De Bie & Nello Cristianini,  & 

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© 2012 Springer-Verlag Berlin Heidelberg

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Smyth, P. (2012). Analyzing Text and Social Network Data with Probabilistic Models. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33460-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-33460-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33459-7

  • Online ISBN: 978-3-642-33460-3

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