计算机工程与应用2025，Vol.61Issue(11)：216-226,11.DOI:10.3778/j.issn.1002-8331.2403-0082
中文短文本情感分类:融入位置感知强化的Transformer-TextCNN模型研究
Chinese Short Text Sentiment Classification:Research on Transformer-TextCNN Model with Location-Aware Enhancement
摘要
Abstract
Aiming at the problem of insufficient acquisition of text location information and key features in the current Chinese short text sentiment classification model,a Transformer-TextCNN sentiment classification model integrated with location-aware enhancement is proposed.Firstly,BERT's learnable absolute position encoding and sinusoidal position coding are used to enhance the position perception ability of the model.Then,the global context understanding ability of Transformer and the local feature capture ability of TextCNN are combined to extract the global and local features of Chi-nese short text respectively.Finally,the emotional feature output service of position perception reinforcement and feature collaboration is constructed to realize the accurate classification of Chinese short text emotion.The experimental results show that the accuracy of the model on the video barrage data set reaches 90.23%,and the accuracy on the SMP2020 data set reaches 87.38%.Compared with the optimal baseline model,the accuracy rate is increased by 1.98 and 0.44 percent-age points on the video barrage dataset and SMP2020 dataset,respectively,and better classification results are achieved in the Chinese short text sentiment classification task.关键词
文本情感分类/BERT/Transformer/textCNN/位置编码Key words
text sentiment classification/BERT/Transformer/textCNN/positional encoding分类
信息技术与安全科学引用本文复制引用
李浩君,王耀东,汪旭辉..中文短文本情感分类:融入位置感知强化的Transformer-TextCNN模型研究[J].计算机工程与应用,2025,61(11):216-226,11.基金项目
国家自然科学基金面上项目(62077043) （62077043）
浙江省哲学社会科学规划交叉学科重点支持课题(22JCXK05Z). （22JCXK05Z）
