Research on Text Sentiment Semantic Optimization Method Based on Supervised Contrastive Learning
[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.
Text Sentiment AnalysisSupervised LearningContrastive LearningRepresentation LearningSemantic Space Optimization
万方数据
维普
