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Red Opal: product-feature scoring from reviews
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Topic identification for fine-grained opinion analysis
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Expanding domain sentiment lexicon through double propagation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A Hybrid Approach to Vietnamese Word Segmentation Using Part of Speech Tags
KSE '09 Proceedings of the 2009 International Conference on Knowledge and Systems Engineering
Grouping product features using semi-supervised learning with soft-constraints
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Sentiment Analysis for Vietnamese
KSE '10 Proceedings of the 2010 Second International Conference on Knowledge and Systems Engineering
Clustering product features for opinion mining
Proceedings of the fourth ACM international conference on Web search and data mining
Extracting and ranking product features in opinion documents
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Opinion word expansion and target extraction through double propagation
Computational Linguistics
Constrained LDA for grouping product features in opinion mining
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
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Feature-based opinion mining and summarizing (FOMS) of reviews is an interesting issue in the opinion mining field. SentiWordNet is an useful lexical resource for opinion mining, especially for FOMS. In this paper, an upgrading FOMS model on Vietnamese reviews on mobile phone products is described. Feature words and opinion words were extracted based on some Vietnamese syntactic rules. Extracted feature words were grouped by using HAC clustering and semi-supervised SVM-kNN classification. Customers' opinion orientation and summarization on features was determined by using a VietSentiWordNet, which had been extended from an initial VietSentiWordNet. Experiments on feature extraction and opinion summarization on features are showed.