Detecting Twitter topics using Latent Dirichlet Allocation

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 s...

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Main Author:	Risch, Johan
Format:	Others
Language:	English
Published:	Uppsala universitet, Institutionen för informationsteknologi 2016
Online Access:	http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-277260

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spelling	ndltd-UPSALLA1-oai-DiVA.org-uu-2772602016-02-19T05:01:44ZDetecting Twitter topics using Latent Dirichlet AllocationengRisch, JohanUppsala universitet, Institutionen för informationsteknologi2016Latent 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. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-277260UPTEC IT, 1401-5749 ; 16001application/pdfinfo:eu-repo/semantics/openAccess
collection	NDLTD
language	English
format	Others
sources	NDLTD
description	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.
author	Risch, Johan
spellingShingle	Risch, Johan Detecting Twitter topics using Latent Dirichlet Allocation
author_facet	Risch, Johan
author_sort	Risch, Johan
title	Detecting Twitter topics using Latent Dirichlet Allocation
title_short	Detecting Twitter topics using Latent Dirichlet Allocation
title_full	Detecting Twitter topics using Latent Dirichlet Allocation
title_fullStr	Detecting Twitter topics using Latent Dirichlet Allocation
title_full_unstemmed	Detecting Twitter topics using Latent Dirichlet Allocation
title_sort	detecting twitter topics using latent dirichlet allocation
publisher	Uppsala universitet, Institutionen för informationsteknologi
publishDate	2016
url	http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-277260
work_keys_str_mv	AT rischjohan detectingtwittertopicsusinglatentdirichletallocation
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Detecting Twitter topics using Latent Dirichlet Allocation

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