A Super Resolution Generative Adversarial Network with Bidirectional Encoder Representations from Transformers for Aspect-based Sentiment Analysis - Vidya M. S. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Aspect-based sentiment analysis (ABSA) determines the sentiment polarity associated with features stated in a sentence or text. Current sentiment analysis algorithms based on aspect categories frequently need to account for the implicit context of aspect-category information. Existing models may yield acceptable results but often require more topic expertise. Current sentiment analysis algorithms based on aspect categories frequently need to account for the implicit context of aspect-category information. Existing models may yield acceptable results, but they often lack topic expertise. We introduce a novel technique that employs Super Resolution Generative Adversarial Networks (SRGAN) and Bidirectional Encoder Representations from Transformers (BERT) to solve the complexities of this problem and improve sentiment analysis accuracy. We offer a synergistic framework in this study that employs SRGANs to enhance the resolution and clarity of text representations, followed by incorporating BERT’s contextual embeddings for aspect-based sentiment analysis. By integrating SRGAN’s ability to build high-resolution text representations with BERT’s contextualized language understanding, SRGANs-BERT overcomes the issues of aspect extraction, sentiment polarity identification, and context-dependent sentiment comprehension. The combination of SRGANs with BERT suggests a path forward for aspect-based sentiment analysis, with applications including customer feedback analysis, market research, and social media sentiment monitoring. We illustrate the usefulness of our suggested strategy through rigorous testing on benchmark data sets. Our findings show that combining SRGANs and BERT significantly improves aspect-based sentiment analysis’s efficacy.