Domain customization for aspect-oriented opinion analysis with multi-level latent sentiment clues

ABSTRACT

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.

References

1. D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3(5):993--1022, 2003.
2. J. Blitzer, M. Dredze, and F. Pereira. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of ACL'07, pages 440--447, 2007.
3. K. W. Church and P. Hanks. Word association norms, mutual information and lexicography. Computational Linguistics, 16(1):22--29, 1990.
4. H. Guo, H. Zhu, Z. Guo, X. Zhang, and Z. Su. Product feature categorization with multi-level latent semantic association. In Proceedings of CIKM09, 2009.
5. H. Guo, H. Zhu, Z. Guo, X. Zhang, and Z. Su. Opinionit: A text mining system for cross-lingual opinion analysis. In Proceedings of CIKM10, 2010.
6. T. Li, V. Sindhwani, C. Ding, and Y. Zhang. Knowledge transformation for cross-domain sentiment classification. In Proceedings of SIGIR'09, 2009.
7. S. J. Pan, X. Ni, J. Sun, Q. Yang, and Z. Chen. Cross-domain sentiment classification via spectral feature alignment. In Proceedings of WWW2010, 2010.
8. P. Pang and L. Lee. A sentiment education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of ACL04, 2004.
9. P. Turney. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL'02, 2002.
10. T. Zhang, F. Damerau, and D. E. Johnson. Text chunking based on a generalization of Winnow. Journal of Machine Learning Research, 2:615--637, 2002.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Natural language processing

      • Language resources