Cross-Domain Aspect Extraction using Adversarial Domain Adaptation

Abstract

Aspect extraction, the task of identifying and categorizing aspects or features in text, plays a crucial role in sentiment analysis. However, aspect extraction models often struggle to generalize well across different domains due to domain-specific language patterns and variations.  In order to tackle this challenge, we propose an approach called "Cross-Domain Aspect Extraction using Adversarial-Based Domain Adaptation". Our model combines the power of pre-trained language models, such as BERT, with adversarial training techniques to enable effective aspect extraction in diverse domains. The model learns to extract domain-invariant aspects by incorporating a domain discriminator, making it adaptable to different domains. We evaluate our model on datasets from multiple domains and demonstrate its effectiveness in achieving cross-domain aspect extraction. The results of our experiments reveal that our model outperforms baseline techniques, resulting in significant gains in aspect extraction across various domains. Our approach opens new possibilities for domain adaptation in aspect extraction tasks, providing valuable insights for sentiment analysis in diverse domains.