高技术通讯2025，Vol.35Issue(10)：1069-1077,9.DOI:10.3772/j.issn.1002-0470.2025.10.004
融合情感簇的混合神经网络短文本情感分类模型
Hybrid neural network short text sentiment classification model integrating sentiment clusters
摘要
Abstract
Aiming at the problem of incomplete feature extraction and lack of position structure information in the main-stream model of deep learning of text classification,a short text sentiment classification model based on sentiment clustering and fusion of multiple neural networks(SC MN)is proposed.This method first generates word vectors through bidirectional encoder representations from Transformers(BERT)pretraining models,and performs senti-ment clustering and sentiment weight enhancement;then it uses the bidirectional long short-term memory(BiL-STM)network with attention mechanism to capture the context features of the text,and uses the capsual network(CapsNet)network to extract the local semantic features with sentence structure information and complete the clas-sification.Based on publicly available datasets and self crawled datasets,the model in this paper is compared with the mainstream classification model of deep learning,and the ablation experiments of different components are con-ducted.The results show that compared with other methods,the accuracy of the model in this paper has achieved an average increase of 5.5％.It is confirmed that different components can bring effective gains to the model and improve the effect of text emotion classification.关键词
文本情感分类/情感簇/胶囊网络/双向长短期记忆网络/注意力机制Key words
text sentiment classification/sentiment clustering/capsule network/bidirectional long short-term memory/attention mechanism引用本文复制引用
谢修娟,刘雪娟..融合情感簇的混合神经网络短文本情感分类模型[J].高技术通讯,2025,35(10):1069-1077,9.基金项目
国家自然科学基金(62101270)和江苏省青蓝工程基金(202004)资助项目. （62101270）
