May 30, 2024Applied and Computational EngineeringOpen Access

Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models

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Aspect category sentiment analysis achieves superior accuracy over existing deep learning baselines by isolating aspect-specific user opinions.
Assessment using BERT model embeddings converts aspect tasks into language inference problems while applying a cross-attention mechanism on text.
Highlights how fine-grained text mining enables e-commerce platforms to extract precise customer feedback across diverse product features reliably.

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Implication

Computational evaluation demonstrates improved sentiment polarity classification across benchmark datasets, highlighting the benefit of cross-attention mechanisms.

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Zhan et al. (2024) studied this question.

synapsesocial.com/papers/68e67960b6db6435876039bf https://doi.org/10.54254/2755-2721/71/2024ma0055

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Implication

Computational evaluation demonstrates improved sentiment polarity classification across benchmark datasets, highlighting the benefit of cross-attention mechanisms.

Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models

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Discussion

Implication

Cite This Study

Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models

Key Points

Discussion

Implication

Cite This Study

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