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Hierarchical Adversarial Training for Multi-domain Adaptive Sentiment Analysis

  • Zhao XuEmail author
  • Lorenzo von Ritter
  • Giuseppe Serra
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 880)

Abstract

Extracting useful insights with sentiment analysis is of increasing importance due to the growing availability of user-generated content. Sentiment analysis usually involves multiple different domains, and the labeled data is often difficult to obtain. In this paper we propose a hierarchical adversarial neural network (HANN) for adaptive sentiment analysis. Unlike most existing deep learning based methods, the proposed method HANN is able to share information between multiple domains bidirectionally, not just transfers information from source domain to target domain in one direction only. In particular, the HANN method is inspired by the ideas of hierarchical Bayesian modeling and generative adversarial networks. We introduce each domain a distinct encoder to model the domain-specific distribution of the latent features. The learning procedures on different domains are coupled by a discriminator network to propagate the information, which can be viewed as adversarial networks in a supervised context by forcing the discriminator to identify domain labels. The proposed method HANN not only captures the distinct properties of each domain, but also shares common information across multiple domains. We demonstrate the superior performance of our method on real data including the Amazon review dataset and the Sanders Twitter sentiment dataset.

Notes

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 766186.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.NEC Laboratories EuropeHeidelbergGermany
  2. 2.Technical University of MunichGarchingGermany
  3. 3.University of BirminghamBirminghamUK

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