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Artificial intelligence and machine learning have significantly bolstered thetechnological world. This paper explores the potential of transfer learning innatural language processing focusing mainly on sentiment analysis. The modelstrained on the big data can also be used where data are scarce. The claim isthat, compared to training models from scratch, transfer learning, usingpre-trained BERT models, can increase sentiment classification accuracy. Thestudy adopts a sophisticated experimental design that uses the IMDb dataset ofsentimentally labelled movie reviews. Pre-processing includes tokenization andencoding of text data, making it suitable for NLP models. The dataset is usedon a BERT based model, measuring its performance using accuracy. The resultcomes out to be 100 per cent accurate. Although the complete accuracy couldappear impressive, it might be the result of overfitting or a lack ofgeneralization. Further analysis is required to ensure the model's ability tohandle diverse and unseen data. The findings underscore the effectiveness oftransfer learning in NLP, showcasing its potential to excel in sentimentanalysis tasks. However, the research calls for a cautious interpretation ofperfect accuracy and emphasizes the need for additional measures to validatethe model's generalization.

Natural Language Processing Through Transfer Learning: A Case Study on Sentiment Analysis