Enhancing Sentiment Analysis Using MCNN-BRNN Model with BERT

In today's digital era, where social media data proliferates, deciphering the emotional undertones embedded in text has become imperative. This paper unveils a pioneering sentiment analysis methodology, synergizing BERT's contextual embeddings with a Multichannel Convolutional Neural Network (MCNN) incorporated within an intricate deep neural network framework. Our proposed strategy adeptly unravels subtle emotional subtleties, capturing the complexity of expressions such as sarcasm and negation. A comprehensive set of experiments conducted across four distinct datasets substantiates the superiority of our approach over existing techniques, as evidenced by marked improvements in pivotal performance metrics including F1-score, accuracy, and precision. In a comparative analysis, our MCNN-BRNN model demonstrates exceptional prowess, outstripping the performance of other embedding methodologies like Word2Vec and Glove. Furthermore, when juxtaposed with the findings from previous studies, our model exhibits stellar performance, underscoring its effectiveness. This research significantly propels the field of sentiment analysis forward, finding applicability in diverse domains ranging from social media monitoring to product review analysis and customer feedback assessment. The BERT-Bi-GRU architecture embedded within our model with (MCNN) addresses complex challenges in natural language processing, seamlessly integrating global context with local features. This highlights the indispensable role of advanced deep-learning techniques in enhancing the precision and effectiveness of sentiment analysis.