Abstract
Recently, cross-domain sentiment classification is becoming popular owing to its potential applications, such as marketing et al. It seeks to generalize a model, which is trained on a source domain and using it to label samples in the target domain. However, the source and target distributions differ substantially in many cases. To address this issue, we propose a comprehensive model, which takes sample filtering and labeling adaptation into account simultaneously, named joint Sample Filtering with Subject-based Ensemble Model (SF-SE). Firstly, a sentence level Latent Dirichlet Allocation (LDA) model, which incorporates topic and sentiment together (SS-LDA) is introduced. Under this model, a high-quality training dataset is constructed in an unsupervised way. Secondly, inspired by the distribution variance of domain-independent and domain-specific features related to the subject of a sentence, we introduce a Subject-based Ensemble model to efficiently improve the classification performance. Experimental results show that the proposed model is effective for cross-domain sentiment classification.
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Acknowledgements
This work is partially supported by grant from the Natural Science Foundation of China (No. 61277370, 61402075), Natural Science Foundation of Liaoning Province, China (No. 201202031, 2014020003), State Education Ministry and The Research Fund for the Doctoral Program of Higher Education (No. 20090041110002), the Fundamental Research Funds for the Central Universities.
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Yang, L., Zhang, S., Lin, H., Wei, X. (2015). Incorporating Sample Filtering into Subject-Based Ensemble Model for Cross-Domain Sentiment Classification. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_10
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