Word association norms, mutual information, and lexicography
Computational Linguistics
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Knowledge transformation for cross-domain sentiment classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Product feature categorization with multilevel latent semantic association
Proceedings of the 18th ACM conference on Information and knowledge management
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
OpinionIt: a text mining system for cross-lingual opinion analysis
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
On the design of LDA models for aspect-based opinion mining
Proceedings of the 21st ACM international conference on Information and knowledge management
Set-Similarity joins based semi-supervised sentiment analysis
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
| Hi-index | 0.00 |
Aspect-oriented opinion mining detects the reviewers' sentiment orientation (e.g. positive, negative or neutral) towards different product-features. Domain customization is a big challenge for opinion mining due to the accuracy loss across domains. In this paper, we show our experiences and lessons learned in the domain customization for the aspect-oriented opinion analysis system OpinionIt. We present a customization method for sentiment classification with multi-level latent sentiment clues. We first construct Latent Semantic Association model to capture latent association among product-features from the unlabeled corpus. Meanwhile, we present an unsupervised method to effectively extract various domain-specific sentiment clues from the unlabeled corpus. In the customization, we tune the sentiment classifier on the labeled source domain data by incorporating the multi-level latent sentiment clues (e.g. latent association among product-features, domain-specific and generic sentiment clues). Experimental results show that the proposed method significantly reduces the accuracy loss of sentiment classification without any labeled target domain data.