现代信息科技2026，Vol.10Issue(18)：80-85,92,7.DOI:10.19850/j.cnki.2096-4706.2026.18.014
融合层级语义与多尺度卷积的短文本情感分析模型
A Short Text Sentiment Analysis Model Integrating Hierarchical Semantics and Multi-Scale Convolution
周洁1
作者信息
- 1. 浙江万里学院 物流与电子商务学院,浙江 宁波 315100
- 折叠
摘要
Abstract
To address the problems of fragmented semantics and insufficient feature utilization in sentiment analysis of short texts in vertical domains,this paper proposes MSBERT,a classification model that integrates hierarchical semantics and Multi-Scale Convolution(MSC).Built on a lightweight Transformer architecture,the model performs adaptive weighted fusion of the encoding layers through a hierarchical attention mechanism,combines multi-scale 1D convolution to extract multi-granularity local features,and achieves collaborative modeling of global and local semantics.Experimental results on a dataset of 11 987 takeaway reviews show that the model achieves an F1-score of 0.883 0 in binary classification,outperforming SVM,LSTM,and BERT baseline models.The results demonstrate that the proposed method has strong sentiment recognition and generalization capability in small-scale data scenarios.关键词
短文本情感分析/BERT/层级注意力/多尺度卷积/特征融合/评论数据Key words
short text sentiment analysis/BERT/hierarchical attention/Multi-Scale Convolution/feature fusion/review data分类
信息技术与安全科学引用本文复制引用
周洁..融合层级语义与多尺度卷积的短文本情感分析模型[J].现代信息科技,2026,10(18):80-85,92,7.
