Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Mining newsgroups using networks arising from social behavior
WWW '03 Proceedings of the 12th international conference on World Wide Web
The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Probabilistic models for discovering e-communities
Proceedings of the 15th international conference on World Wide Web
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
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
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Latent interest-topic model: finding the causal relationships behind dyadic data
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Micro-blogging Sentiment Detection by Collaborative Online Learning
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Aspect and sentiment unification model for online review analysis
Proceedings of the fourth ACM international conference on Web search and data mining
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
User-level sentiment analysis incorporating social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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In large social networks, users feel free to share their feelings about anything they are interested in and many research works have focused on modeling users' interests on social network for product recommendations or personal services. Unfortunately, there are fewer works about finding why users like or dislike something. More specifically, there are many researches about sentiment analysis of users' opinion toward products or topics, but fewer are focused on why they hold this feeling and which aspects or factors of the product (topic) lead to users' different opinions about it. In this paper, we present a hierarchical generative model, called user-sentiment topic model (USTM), which captures users' topics with sentiment information. Our aim is to use USTM to refine users' topics with different sentiment trends including positive, negative and neutral, which can be further used in social network analysis to find influential users on topic level with sentiment information. The experiment results on three datasets show that our proposed USTM can capture user's interests with their sentiment well, making it useful for social network analysis.