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

TL;DR

This paper demonstrates that transfer learning with pre-trained BERT models can achieve high accuracy in sentiment analysis, using the IMDb dataset, but highlights concerns about overfitting and the need for further validation.

Contribution

It provides empirical evidence of transfer learning's effectiveness in sentiment analysis and discusses the importance of cautious interpretation of perfect accuracy results.

Findings

BERT-based transfer learning achieved 100% accuracy on IMDb sentiment dataset.

Transfer learning improves sentiment classification performance over training from scratch.

Overfitting and generalization issues need further investigation.

Abstract

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.