A Sentiment Analysis Method for Teaching Evaluation Texts Using Attention Mechanism Combined with CNN-BLSTM Model

Abstract

In view of the problems that most existing emotion analysis models ignore the relationship between emotions and are not suitable for students, an emotion analysis model of teaching evaluation text based on deep learning is proposed. Firstly, combining the advantages of CNN extracting phrase features and BLSTM extracting sequence features, the CNN-BLSTM model is constructed to effectively enhance the extraction ability of text information. Then, the attention mechanism is used to adaptively perceive the context information, extract the text features that affect students’ emotion, and construct the CNN-BLSTM-AT model. Finally, the CNN-BLSTM-AT model is used to analyze the students’ emotion types in the dataset, and the mini-batch gradient descent method is used for model training. The experiment uses the weibo_senti_100k dataset to demonstrate the performance of the proposed model. The results show that adding the attention mechanism can improve the accuracy of the model by about 0.23. Also, its recall rate is not less than 0.57 and the minimum value of F1 is 0.748, which is better than other comparison models, thus demonstrating the effectiveness of the proposed model.