Most of the existing sentiment analysis methods based on commodity comments texts seldom consider the aspect features,and the related analysis models can't consider both the long-term context dependence and the local text features,thus affecting the accuracy of sentiment analysis.To solve the problem,a sentiment analysis method based on bidirectional gated recurrent network(BiGRU)and capsule network is proposed.Firstly,the method uses the approach based on the word frequency statistics to extract the aspect features from the comment texts,and integrate them into the word vector representation to effectively improve the expression ability of the word vector.Then,the BiGRU network is used to extract the long-term context features of the texts,and Capsule Network is used to extract local features of the comment texts,thus realizing high-precision text sentiment analysis based on aspect.The experimental results on real datasets show that the proposed method is superior to bidirectional long-short term memory(BiLSTM),CNN-LSTM,TextCNN and other sentiment classification models in Accuracy,Precision,Recall and F1 score.
关键词
深度学习/方面特征/情感分析/评论文本
Key words
deep learning/aspect features/sentiment analysis/comment text
维普
万方数据