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arXiv · 2409.14000

Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature

Linxiao Wu·Yuanshuai Luo·Binrong Zhu·Guiran Liu·Rui Wang·Qian Yu
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Abstract

Amidst the swift evolution of social media platforms and e-commerce ecosystems, the domain of opinion mining has surged as a pivotal area of exploration within natural language processing. A specialized segment within this field focuses on extracting nuanced evaluations tied to particular elements within textual contexts. This research advances a composite framework that amalgamates the positional cues of topical descriptors. The proposed system converts syntactic structures into a matrix format, leveraging convolutions and attention mechanisms within a graph to distill salient characteristics. Incorporating the positional relevance of descriptors relative to lexical items enhances the sequential integrity of the input. Trials have substantiated that this integrated graph-centric scheme markedly elevates the efficacy of evaluative categorization, showcasing preeminence.

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Linxiao Wu, Yuanshuai Luo, Binrong Zhu, Guiran Liu, Rui Wang, Qian Yu. 2024-09-21. Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature. https://arxiv.org/abs/2409.14000

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