Cross domain sentiment analysis using transfer learning

Gupta, Bharat; Awasthi, Shivam; Singh, Parshant; Ram, Likhama; Kumar, Pramod; Prasad, Bakshi Rohit; Agarwal, Sonali

doi:10.1109/iciinfs.2017.8300363

Cited by 3 publications

(1 citation statement)

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“…One of the most challenging problems in domain adaption is how to reduce or even eliminate the differences between the source domain and target domain [2]. In CDSA, these differences are reflected as domain discrepancies owing to the distinct expression of the reviewers' emotions from various domains [3]. In recent years, adversarial domain adaption [4] has been widely used to reduce different domains' distribution discrepancies.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%