Transformers in Sentiment Analysis: A Paradigm Shift in Deep Learning Research

Abstract

It was the Transformers which changed the paradigm for the sentiment analysis, sending shock waves to deep learning with its architecture and unprecedented effectiveness. An attention mechanism abstracts the significant features of the input in the self-attention layer, leading to a reconsideration of both pre-trained models, such as BERT, RoBERTa and GPT, at all levels of the sentiment analysis pipeline. These models utilize self-attention mechanisms, allowing them to capture syntactic and semantic dependencies more effectively than recurrent and convolutional networks, leading to significant improvements in various NLP tasks. A rigorous methodology was applied, including fine-tuning pre-trained transformers on heterogeneous datasets and comparing their performance against state-of-the-art methods. The numerous experimentation showed the improvements in terms of accuracy, precision, and recall in some domains such as customer reviews, social media sentiments, and financial data analysis. The study reveals key findings demonstrating the adaptability of the models to solve domain-specific challenges with transfer learning and their efficiency in handling imbalanced datasets. More than that, the paper also describes the trade-offs between computational cost, scalability, and other aspects to consider when implementing transformers in practice. With effective syntactic and semantic embeddings being learned by transformer-based models, this study demonstrates that such deep learning-based architectures redefine performance standards for sentiment analysis tasks and serve as promising basis for building even better interpretable superclass models, suggesting their tremendous potential in shaping current and future research trends in both natural language processing and beyond.