Abstract: Sentiment analysis is a fundamental challenge in natural language processing. However, existing methods often fail to capture the nuanced expressions of words across different contexts during sentence modeling. To address this limitation, this paper proposes a novel text sentiment analysis model HDCL that integrates hypergraph enhancement with dual contrastive learning. This model strengthens the explicit representation of sentiment through label-aware data augmentation. By employing a hypergraph structure, it captures high-order latent associations between text semantics, while dual contrastive learning is utilized to optimize sentence representations, resulting in richer and more accurate sentiment representation. Experimental results on real-world datasets demonstrate that the proposed model outperforms several benchmark models. Additionally, ablation studies and hyperparameter analysis further validate the effectiveness of the proposed model for sentiment analysis tasks.

Key words: text sentiment analysis, hypergraph, dual contrastive learning

摘要： 情感分析是自然语言处理领域中一项具有挑战性的任务。现有的情感分析方法在语句的建模过程中往往会忽略词汇在不同环境下的差异化表达。针对此类问题，提出一种基于超图增强结合双重对比学习的文本情感分析模型（hypergraph enhancement and dual contrastive learning，HDCL）。该模型通过标签感知的数据增强，强化情感的显式表达。使用超图结构学习文本语义之间的高阶潜在关联，并以双重对比学习优化语句表征，以实现丰富、准确的语句情感表示。该模型在真实数据集上的实验结果相较于其他基准模型有所提升，同时设计的消融实验与超参数分析进一步说明了提出的模型对于情感分析任务的有效性。

关键词: 文本情感分析, 超图, 双重对比学习