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LDA-TM: A Two-Step Approach to Twitter Topic Data Clustering
Dalarna University, School of Technology and Business Studies, Information Systems.ORCID iD: 0000-0003-3681-8173
2016 (English)In: Proceedings of the 2016 IEEE International Conference on Cloud Computing and Big Data Analysis, IEEE conference proceedings, 2016, 342-347 p.Conference paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. 342-347 p.
Keyword [en]
big data; twitter data; data analyties; LDA; topic model
National Category
Computer and Information Science
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-22827DOI: 10.1109/ICCCBDA.2016.7529581ISBN: 978-1-5090-2594-7OAI: oai:DiVA.org:du-22827DiVA: diva2:954497
Conference
2016 IEEE International Conference on Cloud Computing and Big Data Analysis, Chengdu, China 5-7 July 2016
Available from: 2016-08-22 Created: 2016-08-22 Last updated: 2016-08-23Bibliographically approved

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Song, William Wei
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