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Evaluation of the dirichlet process multinomial mixture model for short-text topic modeling
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2973-3112
Campus Chapecó, Federal University of Fronteira sul, Chapecó, Brazil.
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-7106-0025
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))
2018 (English)In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018, USA: Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 79-83, article id 8638311Conference paper, Published paper (Refereed)
Abstract [en]

Fast-moving trends, both in society and in highly competitive business areas, call for effective methods for automatic analysis. The availability of fast-moving sources in the form of short texts, such as social media and blogs, allows aggregation from a vast number of text sources, for an up to date view of trends and business insights. Topic modeling is established as an approach for analysis of large amounts of texts, but the scarcity of statistical information in short texts is considered to be a major problem for obtaining reliable topics from traditional models such as LDA. A range of different specialized topic models have been proposed, but a majority of these approaches rely on rather strong parametric assumptions, such as setting a fixed number of topics. In contrast, recent advances in the field of Bayesian non-parametrics suggest the Dirichlet process as a method that, given certain hyper-parameters, can self-adapt to the number of topics of the data at hand. We perform an empirical evaluation of the Dirichlet process multinomial (unigram) mixture model against several parametric topic models, initialized with different number of topics. The resulting models are evaluated, using both direct and indirect measures that have been found to correlate well with human topic rankings. We show that the Dirichlet Process Multinomial Mixture model is a viable option for short text topic modeling since it on average performs better, or nearly as good, compared to the parametric alternatives, while reducing parameter setting requirements and thereby eliminates the need of expensive preprocessing. 

Place, publisher, year, edition, pages
USA: Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 79-83, article id 8638311
Keywords [en]
Bayesian-nonparametrics, Dirichlet-process, short-text, text-analysis, topic-modeling, Information analysis, Bayesian nonparametrics, Dirichlet process, Short texts, Text analysis, Topic Modeling, Mixtures
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-16747DOI: 10.1109/ISCBI.2018.00025ISI: 000462379700015Scopus ID: 2-s2.0-85063024705ISBN: 978-1-5386-9450-3 (electronic)ISBN: 978-1-5386-9451-0 (print)OAI: oai:DiVA.org:his-16747DiVA, id: diva2:1302774
Conference
6th International Symposium on Computational and Business Intelligence (ISCBI), 27-29 August 2018, Basel, Switzerland
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-09-30Bibliographically approved

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Karlsson, AlexanderMathiason, GunnarBae, Juhee

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