三维心理学知识鼓励图表注意力网络利用基于方面的情感分析
Xuefeng Shi1, Weiping Ding2, Min Hu3,4
1School of Computer Science and Artificial Intelligence, Nantong University, Seyuan Road, Nantong, 226019, Jiangsu, China.
Scientific reports
|July 27, 2025
概括
这项研究引入了一种基于方面情绪分析 (ABSA) 的新型网络,该网络利用多维情绪特征. 拟议的VADGAT模型通过整合心理学知识和SenticNet来增强情绪分析,优于现有的方法.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 基于方面的情感分析 (ABSA) 是NLP的一个关键子任务,用于预测特定术语的情感极性.
- 现有的ABSA方法通常依赖于单维的外部情感知识,限制了情感理解.
- 需要采用多维方法来捕获复杂的情绪信息.
研究的目的:
- 提出一个新的ABSA网络,VADGAT,利用三维情感特征.
- 通过整合心理学知识和Sentic.Net来增强情感表达.
- 提高基于方面的情绪分析的准确性和全面性.
主要方法:
- 开发了一个面向面向的模板,用于微调预先训练的语言模型.
- 构建了一个依赖图,其中有三个独立的相邻矩阵,包含了三维情感.
- 使用图表注意网络 (GAT) 来提取关系,以及SenticNet来增强情绪强度.
- 集成了一个有意的影子网络来完善情绪信息选,使用Jensen-Shannon分歧进行了优化.
主要成果:
- 拟议的VADGAT网络有效地提取了多维情绪特征.
- SenticNet集成增强了隐含情绪信息的检测.
- 影子网络改善了对相关情绪数据的选.
- 在五个数据集上进行了广泛的实验,证明了比最先进的方法更优越的性能.
结论:
- 通过结合多维情绪知识,VADGAT网络在基于方面的情绪分析方面取得了重大进展.
- 该方法有效地解决了先前ABSA研究中单维知识利用的局限性.
- 这些发现突出了整合各种情感知识来源的潜力,以实现更强大的情绪分析.
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