摘要
针对目前网络评论文本情感分类准确性不高的问题,提出一种基于BERT和双向门控循环单元(BiGRU)的改进模型,使用能够表征文本丰富语义特征的BERT模型进行词向量表示,结合能够长期保留文本上下文关联信息的BiGRU神经网络提高模型的分类效果,并在此基础上引入注意力机制,突出文本中更能表达分类结果的情感词权重,提高情感分类的准确率。将上述模型分别在Acllmdb_v1和酒店评论两个公开数据集上进行测试,实验结果表明,该模型在中、英文文本情感分类任务中都获得了良好的性能。
Aiming at the problem that the accuracy of text emotion classification of online comment is not high,an improved model based on BERT and bidirectional gated recurrent unit(BiGRU)is proposed.The word vector representation is carried out by using the BERT model which can represent the rich semantic features of the text.The classification effect of the model is im‐proved by combining the BiGRU neural network which can retain the text context related information for a long time.On this ba‐sis,the attention mechanism is introduced,to highlight the weight of emotional words which can better express the classification results in the text,and improve the accuracy of emotional classification.The above model was tested on Acllmdb_v1 data set and hotel reviews data set,which are public data set.The experimental results show that the model achieves good performance in both Chinese and English text emotion classification tasks.
作者
李芸
潘雅丽
肖冬
Li Yun;Pan Yali;Xiao Dong(School of Electronics Information,Hangzhou Dianzi University,Hangzhou 310018,China;Zhejiang Provincial Key Laboratory of Equipment Electronics,Hangzhou 310018,China)
出处
《电子技术应用》
2023年第2期9-14,共6页
Application of Electronic Technique
