基于有监督对比学习的文本情感语义优化方法研究  

Research on Text Sentiment Semantic Optimization Method Based on Supervised Contrastive Learning

作　　者：熊曙初 李轩 吴佳妮 周赵宏 孟晗 Xiong Shuchu;Li Xuan;Wu Jiani;Zhou Zhaohong;Meng Han(School of Computer Science,Hunan University of Technology and Business,Changsha 410205,China;School of Frontier Crossover Studies,Hunan University of Technology and Business,Changsha 410205,China)

机构地区：[1]湖南工商大学计算机学院,长沙410205 [2]湖南工商大学前沿交叉学院,长沙410205

出　　处：《数据分析与知识发现》2024年第6期69-81,共13页Data Analysis and Knowledge Discovery

基　　金：国家社会科学基金项目(项目编号:21BTQ088)的研究成果之一。

摘　　要：【目的】解决因中文独特表达与词义变迁现象导致的文本特征抽取偏移与模糊语义分离困难等问题。【方法】提出一种有监督对比学习语义优化方法。首先使用预训练模型生成语义向量;其次设计有监督联合自监督方法构造对比样本对;最后构建有监督对比损失进行语义空间度量与优化。【结果】在ChnSentiCorp数据集上,经所提方法优化后的5种主流神经网络模型F1值分别实现了2.77~3.82个百分点的提升。【局限】受限于硬件资源,未构建数量更大的对比学习样本对。【结论】语义优化方法可以有效解决特征抽取偏移与模糊语义分离困难等问题,为文本情感分析任务提供新的研究思路。[Objective]This study aims to solve problems such as text feature extraction bias and difficult separation of ambiguous semantics caused by the unique expressions and semantic drift phenomenon in Chinese.[Methods]This paper proposes a supervised contrastive learning semantic optimization method,which first uses a pre-trained model to generate semantic vectors,then designs a supervised joint self-supervised method to construct contrastive sample pairs,and finally constructs a supervised contrastive loss for semantic space measurement and optimization.[Results]On the ChnSentiCorp dataset,the five mainstream neural network models optimized by this method achieved F1 value improvements of 2.77%-3.82%.[Limitations]Due to limited hardware resources,a larger number of contrastive learning sample pairs were not constructed.[Conclusions]The semantic optimization method can effectively solve problems such as text feature extraction bias and difficult separation of ambiguous semantics,and provide new research ideas for text sentiment analysis tasks.

关 键 词：文本情感分析 有监督学习 对比学习 表示学习 语义空间优化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] G350[自动化与计算机技术—计算机科学与技术]