华侨大学学报（自然科学版）2026，Vol.47Issue(2)：164-174,11.DOI:10.11830/ISSN.1000-5013.202505006
多粒度特征融合的分层式机器学习情感分析
Hierarchical Machine Learning Sentiment Analysis of Multi-Granularity Feature Fusion
摘要
Abstract
To address the insufficient utilization of semantic features in esisting sentiment analysis methods for multi-category text classification tasks,a hierarchical sentiment analysis model based on multi-granularity feature fusion is proposed.First,the extreme gradient boosting(XGBoost)algorithm and support vector ma-chine(SVM)are employed in parallel for basic classification,each generating probability distributions across 10 categories.Then,logistic regression is adopted as a meta-classifier to perform feature-level fusion of the du-al channel output results.Finally,the model is validated on a public dataset containing 62 774 comments across 10 categories.Experimental results show that the HML-MGFF model achieves an average accuracy improve-ment of 15.6％over traditional single-classifier models,and 4.6％over four other composite models.关键词
多粒度特征融合/分层式机器学习/极限梯度提升/支持向量机/逻辑回归Key words
multi-granularity feature fusion/hierarchical machine learning/limit gradient lifting/support vec-tor machine/logistic regression分类
信息技术与安全科学引用本文复制引用
赵金鑫,郭荣新,施一帆..多粒度特征融合的分层式机器学习情感分析[J].华侨大学学报（自然科学版）,2026,47(2):164-174,11.基金项目
国家自然科学基金青年基金资助项目(62306122) （62306122）
福建省科技项目引导性项目(2023H0012) （2023H0012）
