Abstract
Sentiment regression is a task of summarizing the overall sentiment of a review with a real-valued score. However, the regression model trained in one domain probably performs poorly in a different domain due to the distribution variety. Different from existing studies, domain adaptation in sentiment regression is more challenging because the rating range in one domain might be different from that in the other domain. In this study, we propose a novel approach to domain adaptation for sentiment regression. Specifically, our approach employs an auxiliary Long Short-Term Memory (LSTM) layer to learn the auxiliary representation from the source domain, and simultaneously join the auxiliary representation into the main LSTM layer for the target domain regression setting. In the learning process, the LSTM regression models for the source and target domains are jointly learned. Empirical studies demonstrate that our joint learning approach performs significantly better than several strong baselines.
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Acknowledgments
This research work has been partially supported by three NSFC grants, No. 61375073, No. 61672366 and No. 61331011.
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Xu, J., Yin, H., Li, S., Zhou, G. (2017). Supervised Domain Adaptation for Sentiment Regression. In: Cheng, X., Ma, W., Liu, H., Shen, H., Feng, S., Xie, X. (eds) Social Media Processing. SMP 2017. Communications in Computer and Information Science, vol 774. Springer, Singapore. https://doi.org/10.1007/978-981-10-6805-8_16
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DOI: https://doi.org/10.1007/978-981-10-6805-8_16
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