关键词: 情感分析, 卷积神经网络, 双向长短期记忆网络, 注意力机制, 句子表示

Abstract:

Aiming at the problem that traditional deep learning algorithms do not fully consider text features and input optimization for sentiment analysis. A two-layer CNN-BiLSTM model combining attention mechanism and sentence ordering（DASSCNN-BiLSTM） is proposed. Firstly, the emotional dictionary is used to sort the document data by emotional polarity to obtain optimized document data. Secondly, the optimized document data is input into the first layer model（composed of CNN and BiLSTM） to generate sentence representations. Finally, the sentence representation is input into the second layer model（composed of BiLSTM and attention mechanism） to generate document representations as the basis for classification, solves the problem of input optimization and fully captures the semantic information between sentences, and improves the accuracy of sentiment classification. Experiments show that the model has a significant improvement in classification accuracy compared to the existing methods, and has a good Mean Squared Error（MSE） value, which can be well applied to general sentiment analysis tasks.

Key words: sentiment analysis, Convolutional Neural Network（CNN）, Bi-directional Long Short-Term Memory（CBiLSTM）, attention mechanism, sentence representation