Paper Info
Title
Exploring Time-Sensitive Variational Bayesian Inference LDA for Social Media Data.
Abstract
There is considerable interest among both researchers and the mass public in understanding the topics of discussion on social media as they occur over time. Scholars have thoroughly analysed sampling-based topic modelling approaches for various text corpora including social media; however, another LDA topic modelling implementation-Variational Bayesian (VB)-has not been well studied, despite its known efficiency and its adaptability to the volume and dynamics of social media data. In this paper, we examine the performance of the VB-based topic modelling approach for producing coherent topics, and further, we extend the VB approach by proposing a novel time-sensitive Variational Bayesian implementation, denoted as TVB. Our newly proposed TVB approach incorporates time so as to increase the quality of the generated topics. Using a Twitter dataset covering 8 events, our empirical results show that the coherence of the topics in our TVB model is improved by the integration of time. In particular, through a user study, we find that our TVB approach generates less mixed topics than state-of-the-art topic modelling approaches. Moreover, our proposed TVB approach can more accurately estimate topical trends, making it particularly suitable to assist end-users in tracking emerging topics on social media.
Year
DOI
Venue
2017
10.1007/978-3-319-56608-5_20
ADVANCES IN INFORMATION RETRIEVAL, ECIR 2017
Field
DocType
Volume
Data mining,Latent Dirichlet allocation,Bayesian inference,Computer science,Artificial intelligence,Adaptability,Social media,Information retrieval,Text corpus,Sampling (statistics),Topic model,Machine learning,Bayesian probability
Conference
10193
ISSN
Citations 
PageRank 
0302-9743
4
0.47
References 
Authors
18
5
Name
Order
Citations
PageRank
Anjie Fang1355.93
Craig Macdonald22588178.50
Iadh Ounis33438234.59
Philip Habel4342.88
Xiao Yang5141.69