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LDA-TM: A two-step approach to twitter topic data clustering
Högskolan Dalarna, Akademin Industri och samhälle, Informatik.ORCID-id: 0000-0003-3681-8173
2016 (engelsk)Inngår i: Proceedings of the 2016 IEEE International Conference on Cloud Computing and Big Data Analysis, IEEE conference proceedings, 2016, s. 342-347Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The Twitter System is the biggest social network in the world, and everyday millions of tweets are posted and talked about, expressing various views and opinions. A large variety of research activities have been conducted to study how the opinions can be clustered and analyzed, so that some tendencies can be uncovered. Due to the inherent weaknesses of the tweets - very short texts and very informal styles of writing - it is rather hard to make an investigation of tweet data analysis giving results with good performance and accuracy. In this paper, we intend to attack the problem from another aspect - using a two-layer structure to analyze the twitter data: LDA with topic map modelling. The experimental results demonstrate that this approach shows a progress in twitter data analysis. However, more experiments with this method are expected in order to ensure that the accurate analytic results can be maintained.

sted, utgiver, år, opplag, sider
IEEE conference proceedings, 2016. s. 342-347
Emneord [en]
big data; twitter data; data analyties; LDA; topic model
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-22827DOI: 10.1109/ICCCBDA.2016.7529581ISI: 000391255500057ISBN: 978-1-5090-2594-7 (tryckt)OAI: oai:DiVA.org:du-22827DiVA, id: diva2:954497
Konferanse
2016 IEEE International Conference on Cloud Computing and Big Data Analysis, Chengdu, China 5-7 July 2016
Tilgjengelig fra: 2016-08-22 Laget: 2016-08-22 Sist oppdatert: 2018-01-10bibliografisk kontrollert

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