Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Increasing participation in online communities: A framework for human-computer interaction
Computers in Human Behavior
ATTention: understanding authors and topics in context of temporal evolution
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
ATT: analyzing temporal dynamics of topics and authors in social media
Proceedings of the 3rd International Web Science Conference
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In web forum analysis, both the discussion topics and author interests are greatly concerned. We introduce a linked topic and interest model based on Latent Dirichlet Allocation (LDA) to explore discussion topics and author interests. Rather than having two separate models or modeling combined topics and interests with just one hidden topic assignment variable, the proposed model has separate but linked hidden variables for topic and interest exploration. As exact model parameter inference is intractable, Gibbs sampling is employed to estimate topic, author, and interest distributions. The joint distribution of the linked hidden variables also provides an interpretation of an interest in terms of weighted topics or vice versa. We apply the model to a NIPS data set and a corpus containing text contents of a popular digital camera web forum. Topics and interests discovered by using the model is demonstrated. The model generalization capability is also assessed by means of perplexity and the results show that the linked topic and interest model has performance exceeding that of LDA document topic model and author topic model.