用句法结构增强情感分析，提升细粒度评价识别能力
Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature
- 将句法结构转为矩阵，通过图卷积与注意力机制提取特征
- 利用描述词位置信息，显著提升情感分类准确率
- 适合需要细粒度情感分析的电商评论场景
随着社交媒体和电子商务生态的快速发展，意见挖掘已成为自然语言处理中的关键研究方向。该领域中一个重点是提取文本中针对特定元素的细微评价。本文提出一种融合话题描述词位置线索的复合框架，将句法结构转化为矩阵形式，利用图神经网络中的卷积与注意力机制提取关键特征。通过引入描述词相对于词汇项的位置相关性，增强了输入序列的连贯性。实验验证表明，该图中心化方法显著提升了评价分类的性能，表现优于现有方法。
原文摘要 · Abstract (English)
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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