Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature

• URL: http://arxiv.org/abs/2409.14000v1
• Date: Sat, 21 Sep 2024 03:30:59 GMT
• Title: Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature
• Authors: Linxiao Wu, Yuanshuai Luo, Binrong Zhu, Guiran Liu, Rui Wang, Qian Yu,
• Abstract summary: This research advances a composite framework that amalgamates the positional cues of topical descriptors. Trials have substantiated that this integrated graph-centric scheme markedly elevates the efficacy of evaluative categorization.
• Score: 12.588486071926388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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