Cross-domain sentiment classification via topical correspondence transfer

Neurocomputing

Volumn 159, Issue 1, 2015, Pages 298-305

  Zhou, Guangyou ^a   Zhou, Yin ^a   Guo, Xiyue ^a   Tu, Xinhui ^a   He, Tingting ^a  

^a CENTRAL CHINA NORMAL UNIVERSITY   (China)

Author keywords

Cross domain; Sentiment classification; Topical correspondence transfer

Indexed keywords

NEURAL NETWORKS;

CROSS-DOMAIN; DIFFERENT DOMAINS; DOMAIN-SPECIFIC INFORMATION; GENERAL SOLUTIONS; REAL APPLICATIONS; SENTIMENT CLASSIFICATION; STATE-OF-THE-ART METHODS; TOPICAL CORRESPONDENCE TRANSFER;

ALGORITHMS;

ACCURACY; ALGORITHM; ANALYTIC METHOD; ARTICLE; CLASSIFICATION; CONTROLLED STUDY; CROSS DOMAIN SENTIMENT CLASSIFICATION; DATA ANALYSIS; DATA PROCESSING; EXPERIMENTAL STUDY; INTERMETHOD COMPARISON; LEARNING; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; TOPICAL CORRESPONDENCE TRANSFER;

EID: 84933280243     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.12.006     Document Type: Article

References (19)

1. G. Zhou, J. Zhao, D. Zeng, Sentiment classification with graph co-regularization, in: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin City University and Association for Computational Linguistics, Dublin, Ireland, 2014, pp. 1331-1340.
2. B. Pang, L. Lee, S. Vaithyanathan, Thumbs up?: sentiment classification using machine learning techniques, in: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - vol. 10, EMNLP '02, Association for Computational Linguistics, Stroudsburg, PA, USA, 2002, pp. 79-86.
3. Pang B., Lee L. Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2008, 2(1-2):1-135.
4. B. Liu, Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers.
5. Daumé H., Marcu D. Domain adaptation for statistical classifiers. J. Artif. Int. Res. 2006, 26(1):101-126.
6. J. Blitzer, R. McDonald, F. Pereira, Domain adaptation with structural correspondence learning, in: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP '06, Association for Computational Linguistics, Stroudsburg, PA, USA, 2006, pp. 120-128.
7. S.J. Pan, X. Ni, J.-T. Sun, Q. Yang, Z. Chen, Cross-domain sentiment classification via spectral feature alignment, in: Proceedings of the 19th International Conference on World Wide Web, WWW '10, ACM, New York, NY, USA, 2010, pp. 751-760.
8. Ando R.K., Zhang T. A framework for learning predictive structures from multiple tasks and unlabeled data. J. Mach. Learn. Res. 2005, 6:1817-1853.
9. T. Li, V. Sindhwani, C.H.Q. Ding, Y. Zhang, Knowledge transformation for cross-domain sentiment classification, in: SIGIR, ACM, 2009, pp. 716-717.
10. Y. He, C. Lin, H. Alani, Automatically extracting polarity-bearing topics for cross-domain sentiment classification, in: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - vol. 1, HLT '11, Association for Computational Linguistics, Stroudsburg, PA, USA, 2011, pp. 123-131.
11. D. Bollegala, D. Weir, J. Carroll, Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification, in: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - vol. 1, HLT '11, Association for Computational Linguistics, Stroudsburg, PA, USA, 2011, pp. 132-141.
12. X. Glorot, A. Bordes, Y. Bengio, Domain adaptation for large-scale sentiment classification: a deep learning approach, in: Proceedings of the Twenty-eight International Conference on Machine Learning, ICML, 2011.
13. S. Li, Y. Xue, Z. Wang, G. Zhou, Active learning for cross-domain sentiment classification, in: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI'13, AAAI Press, 2013, pp. 2127-2133.
14. D.D. Lee, H.S. Seung, Algorithms for non-negative matrix factorization, in: NIPS, MIT Press, 2001, pp. 556-562.
15. Kim H., Park H. Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J. Matrix Anal. Appl. 2008, 30(2):713-730.
16. Lin C.-J. Projected gradient methods for nonnegative matrix factorization. Neural Comput. 2007, 19(10):2756-2779.
17. A. Langville, C. Meyer, R. Albright, J. Cox, D. Duling, Initializations for the nonnegative matrix factorization, in: KDD, 2006.
18. Boutsidis C., Gallopoulos E. Svd based initialization: a head start for nonnegative matrix factorization. Pattern Recognit. 2008, 41(4):1350-1362.
19. Boyd S., Vandenberghe L. Convex Optimization 2004, Cambridge University Press, New York, NY, USA.