Detecting news topics from microblogs using sequential pattern mining
Lau, Cher Han (2014) Detecting news topics from microblogs using sequential pattern mining. PhD thesis, Queensland University of Technology.
This thesis presents a sequential pattern based model (PMM) to detect news topics from a popular microblogging platform, Twitter. PMM captures key topics and measures their importance using pattern properties and Twitter characteristics. This study shows that PMM outperforms traditional term-based models, and can potentially be implemented as a decision support system. The research contributes to news detection and addresses the challenging issue of extracting information from short and noisy text.
Impact and interest:
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|Item Type:||QUT Thesis (PhD)|
|Supervisor:||Tjondronegoro, Dian, Xu, Yue, & Li, Yuefeng|
|Keywords:||microblog, sequential pattern mining, news topic detection, topic detection, news detection, Twitter, text mining, information retrieval|
|Divisions:||Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Institution:||Queensland University of Technology|
|Deposited On:||21 Mar 2014 04:22|
|Last Modified:||10 Sep 2015 03:11|
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