Paper Info
Title
Dynamically Modeling Semantic Dependencies in Web Forum Threads
Abstract
The huge amount of knowledge in web forums has motivated great research interests in recent years. However, tracking semantic dependencies in each thread in web forums has posed a challenging problem for researchers. In this paper, we explore an unsupervised topic model to burst through this issue by simultaneously modeling the semantics and the reply relationship in a thread. The proposed model is a dynamic extension of Latent Dirichlet Allocation (LDA) for the structure of web forum threads, where each post is considered as a mixture of topics that vary along the asynchronous conversation. The experimental results on two different forum data sets show encouraging performance of our proposed PPM in ranking the influence of posts.
Year
DOI
Venue
2011
10.1109/WI-IAT.2011.36
Web Intelligence
Keywords
Field
DocType
asynchronous conversation,web forum threads,unsupervised topic model,dynamically modeling semantic dependencies,latent dirichlet allocation,different forum data set,proposed ppm,challenging problem,web forum,web forum thread,dynamic extension,statistical analysis,computer model,topic modeling,markov process,data model,internet
Data mining,Asynchronous communication,Latent Dirichlet allocation,World Wide Web,Information retrieval,Computer science,Thread (computing),Web modeling,Topic model,Social Semantic Web,Semantics,The Internet
Conference
Citations 
PageRank 
References 
2
0.39
15
Authors
5
Name
Order
Citations
PageRank
Zhaochun Ren151131.69
Jun Ma21280127.50
Gang Wang320.39
Chaoran Cui414520.19
Xiaohui Han5175.41