Unsupervised Domain Adaptation via Weighted Sequential Discriminative Feature Learning for Sentiment Analysis

Abstract

Unsupervised domain adaptation (UDA) presents a significant challenge in sentiment analysis, especially when faced with differences between source and target domains. This study introduces Weighted Sequential Unsupervised Domain Adaptation (WS-UDA), a novel sequential framework aimed at discovering more profound features and improving target representations, even in resource-limited scenarios. WS-UDA utilizes a domain-adversarial learning model for sequential discriminative feature learning. While recent UDA techniques excel in scenarios where source and target domains are closely related, they struggle with substantial dissimilarities. This potentially leads to instability during shared-feature learning. To tackle this issue, WS-UDA employs a two-stage transfer process concurrently, significantly enhancing model stability and adaptability. The sequential approach of WS-UDA facilitates superior adaptability to varying levels of dissimilarity between source and target domains. Experimental results on benchmark datasets, including Amazon reviews, FDU-MTL datasets, and Spam datasets, demonstrate the promising performance of WS-UDA. It outperforms state-of-the-art cross-domain unsupervised baselines, showcasing its efficacy in scenarios with dissimilar domains. WS-UDA’s adaptability extends beyond sentiment analysis, making it a versatile solution for diverse text classification tasks.