An RCG-based study of aspect-level sentiment analysis for hybrid attention

Baoqing Lu; Hongzhi Yu; Fucheng Wan; Chen Min; Bin Wei; Yulin Fen

doi:10.1117/12.3035779

16 August 2024 An RCG-based study of aspect-level sentiment analysis for hybrid attention

Baoqing Lu, Hongzhi Yu, Fucheng Wan, Chen Min, Bin Wei, Yulin Fen

Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 1323004 (2024) https://doi.org/10.1117/12.3035779
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China

Abstract

Aspect-based sentiment analysis (ABSA) is a crucial granular task within sentiment analysis, focusing on the precise identification of sentiment orientations for specific aspects within text. Recognizing that identical context words can express opposing sentiment polarities in different situations, it's essential to delve into the nuanced interactions between target and context words. This study introduces an RCG-based Hybrid Attention Network, a novel architecture that adeptly utilizes lexical attention mechanisms to extract lexical features and fortify the relationship between aspects and their corresponding target words. To assess the efficacy of our proposed approach, we conducted experiments on a well-known public dataset. The results show a significant 3.37% enhancement in accuracy and a 1.38% improvement in Macro-F1 scores compared to related methods, affirming the superiority of our technique in enhancing the performance of aspect-level sentiment analysis.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Baoqing Lu, Hongzhi Yu, Fucheng Wan, Chen Min, Bin Wei, and Yulin Fen "An RCG-based study of aspect-level sentiment analysis for hybrid attention", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 1323004 (16 August 2024); https://doi.org/10.1117/12.3035779

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