Unifying Adversarial Adaptation and Maximum Mean Discrepancy for Enhanced Cross-Domain Aspect Sentiment Classification - Suneetha, Sampathirao; Row , S. Viziananda (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Cross-domain sentiment analysis is a fundamental challenge in NLP with applications in diverse areas such as product reviews, customer feedback, and social media monitoring. In this paper, we propose a comprehensive approach for Cross-Domain Aspect-Based Sentiment Analysis (CD-ABSA) that integrates aspect extraction and sentiment classification. Leveraging pre-trained Bidirectional Encoder Representations from Transformers (BERT) models, our methodology presents a novel framework that utilizes Adversarial Domain Adaptation, incorporating Maximum Mean Discrepancy (MMD) to facilitate the adaptation of sentiment classifiers from one domain to another, specifically in the restaurants, laptops, books, and clothes domains. Our approach ensures accurate sentiment analysis across diverse domains by reducing the distribution gap through adversarial domain adaptation and MMD(ADA-MMD). We evaluate our model using accuracy and F1-Scores and show its superior performance compared to existing methods. This research represents a significant step towards domain-agnostic sentiment analysis by combining aspect extraction and sentiment classification within a unified framework, providing practical solutions for scenarios with limited or no domain-specific labeled data.