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Detecting Twitter topics using Latent Dirichlet Allocation
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2016 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Latent Dirichlet Allocations is evaluated for its suitability when detecting topics in a stream of short messages limited to 140 characters. This is done by assessing its ability to model the incoming messages and its ability to classify previously unseen messages with known topics. The evaluation shows that the model can be suitable for certain applications in topic detection when the stream size is small enough. Furthermoresuggestions on how to handle larger streams are outlined.

Place, publisher, year, edition, pages
2016. , 48 p.
Series
UPTEC IT, ISSN 1401-5749 ; 16001
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-277260OAI: oai:DiVA.org:uu-277260DiVA: diva2:904196
Educational program
Master of Science Programme in Information Technology Engineering
Supervisors
Examiners
Available from: 2016-02-18 Created: 2016-02-18 Last updated: 2016-02-18Bibliographically approved

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CiteExportLink to record
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