Global-Local Dynamic Adversarial Learning for Cross-Domain Sentiment Analysis

Juntao Lyu, Zheyuan Zhang, Shufeng Chen and Xiying Fan ()
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Juntao Lyu: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Zheyuan Zhang: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Shufeng Chen: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Xiying Fan: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Mathematics, 2023, vol. 11, issue 14, 1-11

Abstract: As one of the most widely used applications in domain adaption (DA), Cross-domain sentiment analysis (CDSA) aims to tackle the barrier of lacking in sentiment labeled data. Applying an adversarial network to DA to reduce the distribution discrepancy between source and target domains is a significant advance in CDSA. This adversarial DA paradigm utilizes a single global domain discriminator or a series of local domain discriminators to reduce marginal or conditional probability distribution discrepancies. In general, each discrepancy has a different effect on domain adaption. However, the existing CDSA algorithms ignore this point. Therefore, in this paper, we propose an effective, novel and unsupervised adversarial DA paradigm, Global-Local Dynamic Adversarial Learning ( GLDAL ). This paradigm is able to quantitively evaluate the weights of global distribution and every local distribution. We also study how to apply GLDAL to CDSA. As GLDAL can effectively reduce the distribution discrepancy between domains, it performs well in a series of CDSA experiments and achieves improvements in classification accuracy compared to similar methods. The effectiveness of each component is demonstrated through ablation experiments on different parts and a quantitative analysis of the dynamic factor. Overall, this approach achieves the desired DA effect with domain shifts.

Keywords: adversarial domain adaption; cross-domain sentiment analysis; global-local dynamic adversarial learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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