| Autor: |
Puvvula, Deepika, Rodda, Sireesha |
| Předmět: |
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| Zdroj: |
Mathematical Modelling of Engineering Problems; Oct2024, Vol. 11 Issue 10, p2849-2858, 10p |
| Abstrakt: |
Aspect-based sentiment analysis aims to identify sentiment polarities towards specific aspects in textual data. Despite extensive aspect based sentiment analysis research, accurately capturing nuanced semantics remains challenging. This paper presents a novel transformer-based sequence prediction approach using generative pretrained transformer model that overcomes current limitations. The generative pretrained transformer model is an expert in understanding the context of text and aids in identifying minute nuances in text that are useful for sentiment analysis. A hotel review dataset is leveraged for rigorous analysis. Key technical innovations include a Latent Dirichlet Allocation-based aspect extraction method capable of grouping into representative topics, followed by sophisticated generative pretrained transformer model fine-tuning optimized for the granularity and semantic nuances of sentiments and aspects. Extensive quantitative experiments demonstrate 90% train and 84% test accuracy, outperforming previous state-of-the-art convolutional neural network models. Qualitative evaluations further showcase capabilities in modeling interdependent aspect semantics neglected by existing works. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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