A Comparative Study of BERT, RoBERTa, and Bi-LSTM for Sentiment Analysis of Social Media Data in Diverse Domains

Sentiment Analysis (SA) has become a very critical activity for gaining valuable information from the huge amount of user-generated content that is available on social media sites. Yet, sentiment classification has proved to be difficult due to reasons like language informality, contextual uncertainty, noise in the text, abbreviations, emojis, etc. The goal of this research is to conduct a comparative assessment of Bi-directional Encoder Representations Transformer (BERT) and Robustly Optimized BERT Pretraining Approach (RoBERTa) models for SA on various social media domains through one common experimental setup. The suggested experimental setup would include text processing, feature extraction using Word2Vec, feature selection using Mutual Information (MI), and training and testing of the models on the Sentiment140 Dataset with 1.6 million tweets. Results from experiments indicate that the RoBERTa-based framework attains the highest accuracy of 98.56% along with the highest precision, recall, F1 score, and 5.46 seconds of training time and outperforms Bi-LSTM and BERT in terms of classification efficacy and computational efficiency. The novelty of the proposed study lies in ensuring a fair comparison through identical experimental settings while integrating Word2Vec-based feature representation with Mutual Information-based feature selection to enhance classification performance. The suggested framework is a highly reliable and scalable solution for performing accurate sentiment classification and helps in selecting the right model.