Fine-Tuning Transformers for Sentiment Analysis

This paper explores advanced techniques for fine-tuning pre-trained transformer models such as BERT and GPT for sentiment analysis tasks, with a particular focus on handling domain-specific language in customer feedback. We propose a novel adaptive transfer learning framework that combines contextual embedding augmen tation with progressive domain adaptation to improve sentiment classification accuracy across diverse domains. Our experimental results demonstrate that our proposed methods achieve state-of-the-art performance on benchmark datasets, with significant improvements in handling domain-specific terminology and contextual nuances in customer feedback. We also introduce a new approach to cross domain generalization through contrastive domain adaptation that shows promising results for zero-shot adaptation to new domains.