燕山大学学报2026，Vol.50Issue(4)：369-376,8.DOI:10.3969/j.issn.1007-791X.2026.04.010
基于轻量级预训练模型的微博情感分析算法研究
Lightweight pre-training model-based Weibo sentiment analysis algorithm research
摘要
Abstract
The challenge of extracting complex textual features from Weibo posts has been tackled through the proposal of a text-sentiment analysis algorithm built on a lightweight pre-trained model.First,the loss function was refined by introducing a neural-network dropout regularization algorithm,through which supervised data augmentation was achieved.Second,at the algorithmic level,a dual-channel network combining convolution and temporal attention was incorporated,so that the efficiency of long-text feature extraction was enhanced.Finally,to mitigate the feature loss introduced by deep architectures,residual connections were employed to improve robustness.Experiments were carried out on a public hotel-review dataset and a Weibo-comment dataset.The results indicated that,relative to the baseline model,classification accuracy was increased by 1.33 and 1.41 percentage points on the two datasets,respectively,thereby demonstrating that the proposed algorithm effectively strengthens the feature-capturing capability and sentiment-analysis accuracy of lightweight models.关键词
情感分析/轻量级预训练模型/自然语言处理/深度学习/文本处理Key words
sentiment analysis/lightweight pre-trained model/natural language processing/deep learning/text processing分类
信息技术与安全科学引用本文复制引用
刘浩然,孙子鉴,杨子朋,崔晗,翟聆妤..基于轻量级预训练模型的微博情感分析算法研究[J].燕山大学学报,2026,50(4):369-376,8.基金项目
国家自然科学基金资助项目(62106214) （62106214）
河北省自然科学基金资助项目(D2024203008) （D2024203008）
