NDLI: Microblog bursty topic detection based on user relationship

Content Provider	IEEE Xplore Digital Library
Author	Yanyan Du Yanxiang He Ye Tian Qiang Chen Lu Lin
Copyright Year	2011
Description	Author affiliation: School of Computer, Wuhan University Wuhan, China (Yanyan Du; Yanxiang He; Ye Tian; Qiang Chen; Lu Lin)
Abstract	Microblog is becoming more and more popular in people's daily life, through which people can read and send short text messages. Considering millions of data posted every day, detecting bursty topic in real-time will be helpful for people to grasp central information. In this paper we propose a novel bursty topic detection technique based on an improved method by calculating term weight, the calculating method we used takes user weight and the number of listeners, replies and collections into account. We first use a novel aging theory to model a term life cycle, then calculate user weight through improved PageRank algorithm to express term weight, at last adopt a unsupervised learning algorithm to detect bursty topic, meanwhile provide the results and evaluation in contrast with TF-IDF (term frequency-inverse document frequency) and UF-ITUF (user frequency-inverse thread user frequency) model to prove the validity of our proposed approach.
Starting Page	260
Ending Page	263
File Size	162679
Page Count	4
File Format	PDF
ISBN	9781424486229
e-ISBN	9781424486250
DOI	10.1109/ITAIC.2011.6030199
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Topic detection Hidden Markov models Aging Aging theory Feature extraction Time frequency analysis Bursty keywords User relationship Data mining Unsupervised learning
Content Type	Text
Resource Type	Article

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