Abstract
Sentiment analysis is important to develop marketing strategies, enhance sales and optimize supply chain for electronic commerce. Many supervised and unsupervised algorithms have been applied to build the sentiment analysis model, which assume that the distributions of the labeled and unlabeled data are identical. In this paper, we aim to deal with the issue of a classifier trained for use in one domain might not perform as well in a different one, especially when the distribution of the labeled data is different with that of the unlabeled data. To tackle this problem, we incorporate feature extraction methods into the neural network model for cross-domain sentiment classification. These methods are applied to simplify the structure of the neural network and improve the accuracy. Experiments on two real-world datasets validate the effectiveness of our methods for cross-domain sentiment classification.
The authors contributed equally to this work.
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Acknowledgements
This research has been substantially supported by a grant from the Soft Science Research Project of Guangdong Province (Grant No. 2014A030304013).
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Zhu, E., Huang, G., Mo, B., Wu, Q. (2016). Features Extraction Based on Neural Network for Cross-Domain Sentiment Classification. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_7
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DOI: https://doi.org/10.1007/978-3-319-32055-7_7
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