Comparative Study of BERT-CNN, TRANS-BLSTM, and RoBERTa Models for Sentiment Analysis

In the field of Natural Language Processing (NLP), various milestones and improvements have been made due to the advent of deep learning, mainly in sentiment analysis of text data. BERT has excelled through its capability to comprehend emotional context. Nonetheless, there are some limitations with BERT like long inference times as well as interpretability challenges. Therefore, combinations of such architectures with BERT, for instance TRANS-BLSTM, BERT-CNN and RoBERTa have been studied. These hybrid models aim at leveraging on the strengths that BERT possesses while making sentiment analysis tasks more efficient and interpretable. This investigation demonstrates ongoing attempts to develop the better NLP models that can comprehend human emotions reflected in text data more effectively.