DocumentCode
245410
Title
Automatic Twitter Topic Summarization
Author
Dunwei Wen ; Marshall, Geoffrey
Author_Institution
Sch. of Comput. & Inf. Syst., Athabasca Univ., Athabasca, AB, Canada
fYear
2014
fDate
19-21 Dec. 2014
Firstpage
207
Lastpage
212
Abstract
This paper aims to generate digests of tweets from live trending and ongoing topics. The primary purpose is to group the tweets by importance or usefulness so that an end user can be presented with a reasonable extract of the most important content from the Twitter stream. Summarization is accomplished using a non-parametric Bayesian model applied to Hidden Markov Models and a novel observation model designed to allow ranking based on selected predictive characteristics of individual tweets.
Keywords
Bayes methods; hidden Markov models; nonparametric statistics; social networking (online); text analysis; Twitter stream; automatic Twitter topic summarization; hidden Markov models; nonparametric Bayesian model; Bayes methods; Clustering algorithms; Hidden Markov models; Image color analysis; Predictive models; Twitter; Vectors; Dirichlet process; HDP-HMM; Twitter; microblog summarization;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4799-7980-6
Type
conf
DOI
10.1109/CSE.2014.69
Filename
7023580
Link To Document