Abstract
Cross-domain sentiment classification aims at transferring the knowledge of the source domain with rich annotation resource to the scarcely labeled target domain or even without labels. Existing models fail to automatically capture simultaneously the three related topics, namely sentiment-only topic, i.e. containing domain-independent sentiment words or pivots in literature, domain-only topic, i.e. containing domain-specific words, and function word topic containing such as stop words. We propose a two-stage framework for tackling this problem. The first stage consists of a topic attention network specialized in discovering topics mentioned above. The second stage utilizes the learned knowledge from the first stage for learning a sentiment classification model with the consideration of context. A new sentiment-domain dual-task adversarial training strategy is designed and utilized in both stages. Experiments on a real-world product review dataset show that our proposed model outperforms the state-of-the-art model.
The work described in this paper is substantially supported by a grant from the Asian Institute of Supply Chains and Logistics, the Chinese University of Hong Kong.
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Notes
- 1.
Word2vec is available in https://code.google.com/archive/p/word2vec/.
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Lai, KP., Ho, J.CS., Lam, W. (2020). Cross-Domain Sentiment Classification Using Topic Attention and Dual-Task Adversarial Training. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_46
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