The Journal of Machine Learning Research
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Multi-facet Rating of Product Reviews
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
An unsupervised aspect-sentiment model for online reviews
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Aspect and sentiment unification model for online review analysis
Proceedings of the fourth ACM international conference on Web search and data mining
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
Domain-dependent/independent topic switching model for online reviews with numerical ratings
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Sentiment topic models for social emotion mining
Information Sciences: an International Journal
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In this paper, we aim to jointly extract aspects and aspect-specific sentiment knowledge from online reviews, where the sentiment knowledge refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities. To this end, we propose a Joint Aspect/Sentiment model (JAS). JAS detects aspect-specific opinion words by integrating opinion word lexicon knowledge to explicitly separate opinion words from factual words. More importantly, JAS exploits sentiment prior and aspect-contextual sentence-level co-occurrences of opinion words in reviews to further identify aspect-aware sentiment polarities for the opinion words. We apply the learned aspect-specific sentiment knowledge to practical aspect-level sentiment analysis tasks. Experimental results show the effectiveness of JAS in learning aspect-specific sentiment knowledge and the practical value of this knowledge when applied to aspect-level sentiment classification.