Systematic Study of NLP Learning Models and Performance Evaluation - D., Jennifer; K., Valarmathi; E., Murali; R., Devi; V., Sathiya (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Latest research advancements in the field of deep learning have significantly elevated natural language processing like sentiment analysis, speech recognition, text classification and Named Entity Recognition. NLP task like sentence classification involves categorizing sentences into predefined classes based on their content. Sentiment analysis, also known as opinion mining, employs NLP and machine learning to identify sentiment in text (positive, negative, or neutral) for understanding opinions and emotions. This paper offers a comprehensive exploration of advanced sentiment analysis approaches employing BERT. Bidirectional Encoder Representations from Transformers (BERT) excels at capturing contextual word relationships, making it suitable for sentiment analysis. The study encompasses Deep Learning as well as Machine Learning approaches, analyzing 40 research papers. Out of these, 21 utilize BERT for text classification, while others employ general ML techniques. The paper compares BERT with other language models, investigates into proprietary BERT-based models, and outlines challenges and research gaps in sentiment analysis.