Abstract
Sentiment classification plays a vital role in current online commercial transactions because it is critical to understand users’ opinions and feedbacks in businesses or products. Cross-domain sentiment classification can adopt a well-trained classifier from one source domain to other target domains, which reduces the time and efforts of training new classifiers in these domains. Existing cross-domain sentiment classification methods require data or other information in target domains in order to train their models. However, collecting and processing new corpora require very heavy workload. Besides, the data in target domains may be private and not always available for training. To address these issues, motivated by multi-task learning, we design a Bifurcated-LSTM which takes advantages of attention-based LSTM classifiers along with augmented dataset and orthogonal constraints. This Bifurcated-LSTM can extract domain-invariant sentiment features from the source domain to perform sentiment analysis in different target domains. We conduct extensive experiments on seven classic types of product reviews, and results show that our system leads to significant performance improvement.
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Acknowledgments
This work was partially supported by the U.S. National Science Foundation under grants CNS-1602172 and CNS-1566479.
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Ji, J., Luo, C., Chen, X., Yu, L., Li, P. (2018). Cross-Domain Sentiment Classification via a Bifurcated-LSTM. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_54
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