Natural Language Processing Through Transfer Learning: A Case Study on
  Sentiment Analysis
Aman Yadav Abhishek Vichare
Artificial intelligence and machine learning have significantly bolstered the
technological world. This paper explores the potential of transfer learning in
natural language processing focusing mainly on sentiment analysis. The models
trained on the big data can also be used where data are scarce. The claim is
that, compared to training models from scratch, transfer learning, using
pre-trained BERT models, can increase sentiment classification accuracy. The
study adopts a sophisticated experimental design that uses the IMDb dataset of
sentimentally labelled movie reviews. Pre-processing includes tokenization and
encoding of text data, making it suitable for NLP models. The dataset is used
on a BERT based model, measuring its performance using accuracy. The result
comes out to be 100 per cent accurate. Although the complete accuracy could
appear impressive, it might be the result of overfitting or a lack of
generalization. Further analysis is required to ensure the model's ability to
handle diverse and unseen data. The findings underscore the effectiveness of
transfer learning in NLP, showcasing its potential to excel in sentiment
analysis tasks. However, the research calls for a cautious interpretation of
perfect accuracy and emphasizes the need for additional measures to validate
the model's generalization.