Enhancing Aspect Based Sentiment Analysis Using BERT and Capsule Networks

Aspect-Based Sentiment Analysis (ABSA) is a vital tool in the field of natural language processing, used to uncover the complex sentiments expressed in textual data. This is particu-larly significant in today's data-driven landscape, where opinions and reviews wield substantial influence in shaping business strategies and understanding consumer behavior. Traditional ABSA approaches, often reliant on conventional machine learning or standard neural network architectures, encounter challenges related to context sensitivity and comprehensive semantic com-prehension, leading to suboptimal performance in accurately identifying and interpreting sentiments. Addressing these limi-tations, our research introduces a novel method that combines the power of Bidirectional Encoder Representations from Trans-formers (BERT) in conjunction with the dynamic capabilities of Capsule Networks. This integration aims to substantially elevate the semantic understanding and contextual analysis in ABSA. Leveraging the MAMS dataset, our meticulously trained model underwent evaluation using a diverse array of product reviews and social media posts, annotated for various aspects and cor-responding sentiments. The experimental findings highlight the effectiveness of our suggested model, demonstrating a significant enhancement in precision, recall, and the Fl-score across several datasets. Notably, our model achieved an impressive accuracy rate of 82.6 %, surpassing traditional methods. In conclusion, our study charts a new frontier in ABSA, where the synergistic alliance of BERT and Capsule Networks not only enhances accuracy but also deepens the understanding of sentiments at an aspect level. This not only provides invaluable insights into consumer opinions and market trends but also demonstrates the potential for transformative advancements in sentiment analysis methodologies.