ENHANCING SENTIMENT ANALYSIS ACCURACY THROUGH TRANSFER LEARNING AND PRE-TRAINED LANGUAGE MODELS

Sentiment analysis, a pivotal task in natural language processing, aims to extract and classify sentiments expressed in textual data.Traditional sentiment analysis models often struggle with domain-specific nuances, limited labelled data, and contextual intricacies, leading to reduced accuracy.This paper proposes a novel approach to enhance sentiment analysis accuracy through the fusion of transfer learning techniques and pre-trained language models.Leveraging transfer learning, knowledge from a source domain with abundant data is transferred to a target domain with limited labelled data, thereby alleviating data scarcity issues.Additionally, pre-trained language models, such as BERT (Bidirectional Encoder Representations from Transformers), are employed to capture rich contextual information, facilitating better sentiment understanding.The experimental results on benchmark datasets demonstrate the effectiveness of the proposed method, showcasing significant improvements in sentiment analysis accuracy compared to conventional approaches.This research contributes to advancing sentiment analysis methodologies, particularly in scenarios with limited labelled data, and offers insights into the synergistic benefits of transfer learning and pre-trained language models.