OPBERT-SEGCN
Enhancing Aspect-Based Sentiment Analysis with Sentiment Links and Opinion-Infused BERT Model
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This book introduces a novel approach that revolutionizes the way sentiment knowledge is integrated and transferred within ABSA models. It proposes a pioneering method that employs sentiment links within a dependency graph, acting as conduits for the efficient transfer of sentiment knowledge from sentiment and opinion nodes to aspect nodes. This innovative mechanism significantly enhances the model's ability to capture sentiment information, leading to more accurate sentiment predictions. Furthermore, ""OPBERT-SEGCN"" introduces the Opinion-Infused BERT (OPBERT) model, which generates text rep...
This book introduces a novel approach that revolutionizes the way sentiment knowledge is integrated and transferred within ABSA models. It proposes a pioneering method that employs sentiment links within a dependency graph, acting as conduits for the efficient transfer of sentiment knowledge from sentiment and opinion nodes to aspect nodes. This innovative mechanism significantly enhances the model's ability to capture sentiment information, leading to more accurate sentiment predictions. Furthermore, ""OPBERT-SEGCN"" introduces the Opinion-Infused BERT (OPBERT) model, which generates text representations that boost the identification of opinion words. This integration of deep learning and sentiment analysis provides a powerful tool for understanding the nuances of public opinion and policy analysis, hospitality and tourism, and financial services. Rich with experimental evidence from five publicly available datasets, the book demonstrates the superior performance of the proposed mechanisms over existing models. It is an essential read for researchers, data scientists, and practitioners in the field of natural language processing and sentiment analysis, offering insights into the future of understanding and reacting to human opinions and reactions at a granular level.
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