Analyzing Topics and Trends in the PRIMA Literature

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9862)

Abstract

This study investigates the content of the literature published in the proceedings of the International Conference on Principles and Practices of Multi-Agent Systems (PRIMA). Our study is based on a corpus of the 611 papers published in eighteen PRIMA proceedings from 1998 (when the conference started) to 2015. We have developed an unsupervised topic model, using Latent Dirichlet Allocation (LDA), over the PRIMA corpus of papers to analyze popular topics in the literature published at PRIMA in the past eighteen years. We have also analyzed historical trends and examine the strength of each topic over time.

Keywords

Topic Modeling Latent Dirichlet Allocation Popular Topic Prima Literature Gibbs Sampling Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computing and Information TechnologyUniversity of WollongongWollongongAustralia

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