Fine-Tuning Transformers for Sentiment Analysis

Author(s)	Raghav Girish Dashrath, Shravani Sanjay Deshmukh, Atharv Rajendra Bhaleghare
Country	India
Abstract	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.
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-04
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.42227
Short DOI	https://doi.org/g9hscs

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research