Title :
Automatic Twitter Topic Summarization
Author :
Dunwei Wen ; Marshall, Geoffrey
Author_Institution :
Sch. of Comput. & Inf. Syst., Athabasca Univ., Athabasca, AB, Canada
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;
Conference_Titel :
Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4799-7980-6
DOI :
10.1109/CSE.2014.69
