Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature

TL;DR

This paper introduces a graph neural network framework that utilizes syntactic features and positional cues to improve sentiment analysis accuracy in social media and e-commerce texts.

Contribution

It presents a novel composite framework combining syntactic structures, positional information, and graph-based neural networks for enhanced opinion mining.

Findings

Significant improvement in sentiment classification accuracy.

Effective integration of syntactic and positional features.

Outperforms existing methods in evaluations.

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.