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Issue No. 07 - July (2017 vol. 29)
ISSN: 1041-4347
pp: 1370-1383
Fangzhao Wu , Department of Electronic Engineering, Tsinghua University, Beijing, China
Zhigang Yuan , Department of Electronic Engineering, Tsinghua University, Beijing, China
Yongfeng Huang , Department of Electronic Engineering, Tsinghua University, Beijing, China
ABSTRACT
We propose a collaborative multi-domain sentiment classification approach to train sentiment classifiers for multiple domains simultaneously. In our approach, the sentiment information in different domains is shared to train more accurate and robust sentiment classifiers for each domain when labeled data is scarce. Specifically, we decompose the sentiment classifier of each domain into two components, a global one and a domain-specific one. The global model can capture the general sentiment knowledge and is shared by various domains. The domain-specific model can capture the specific sentiment expressions in each domain. In addition, we extract domain-specific sentiment knowledge from both labeled and unlabeled samples in each domain and use it to enhance the learning of domain-specific sentiment classifiers. Besides, we incorporate the similarities between domains into our approach as regularization over the domain-specific sentiment classifiers to encourage the sharing of sentiment information between similar domains. Two kinds of domain similarity measures are explored, one based on textual content and the other one based on sentiment expressions. Moreover, we introduce two efficient algorithms to solve the model of our approach. Experimental results on benchmark datasets show that our approach can effectively improve the performance of multi-domain sentiment classification and significantly outperform baseline methods.
INDEX TERMS
Collaboration, User-generated content, Motion pictures, Training, Robustness, Benchmark testing, Parallel algorithms
CITATION

F. Wu, Z. Yuan and Y. Huang, "Collaboratively Training Sentiment Classifiers for Multiple Domains," in IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. 7, pp. 1370-1383, 2017.
doi:10.1109/TKDE.2017.2669975
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