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
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Product feature categorization with multilevel latent semantic association
Proceedings of the 18th ACM conference on Information and knowledge management
Generalizing dependency features for opinion mining
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
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
Product Feature Mining with Nominal Semantic Structure
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
On the design of LDA models for aspect-based opinion mining
Proceedings of the 21st ACM international conference on Information and knowledge management
The FLDA model for aspect-based opinion mining: addressing the cold start problem
Proceedings of the 22nd international conference on World Wide Web
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In the field of opinion mining, extraction of fine-grained product feature is a challenging problem. Noun is the most important features to represent product features. Generative model such as the latent Dirichlet allocation (LDA) has been used for detecting keyword clusters in document corpus. As adjectives often dominate review corpus, they are often excluded from the vocabulary in such generative model for opinion sentiment analysis. On the other hand, adjectives provide useful context for noun features as they are often semantically related to the nouns. To take advantage of such semantic relations, dependency tree is constructed to extract pairs of noun and adjective with semantic dependency relation. We propose a semantic dependent word pairs generative model for pairs of noun and adjective for each sentence. Product features and their corresponding adjectives are simultaneously clustered into distinct groups which enable improved accuracy of product features as well as providing clustered adjectives. Experimental results demonstrated the advantage of our models with lower perplexity, average cluster entropies, compared to baseline models based on LDA. Highly semantic cohesive, descriptive and discriminative fine-grained product features are obtained automatically.