The Journal of Machine Learning Research
Statistical entity-topic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Exploring social annotations for information retrieval
Proceedings of the 17th international conference on World Wide Web
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Rated aspect summarization of short comments
Proceedings of the 18th international conference on World wide web
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Learning author-topic models from text corpora
ACM Transactions on Information Systems (TOIS)
The topic-perspective model for social tagging systems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic evaluation of topic coherence
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Topic modeling for personalized recommendation of volatile items
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Post-based collaborative filtering for personalized tag recommendation
Proceedings of the 2011 iConference
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
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In this paper, we propose a new probabilistic generative model for topic analysis of online reviews, called Author-Experience-Object-Topic Model (AEOT). This model is to capture the relationship between the authors, objects and reviews in order to improve the performance of topic analysis. The model, as a general one, can be transformed to six simpler models, and can produce topic-word, author-topic and object-topic distributions. Experimental results show that the model is suitable for topic analysis of online reviews, and outperforms other existing methods.