Emotion Analysis Method of Teaching Evaluation Texts Based on Deep Learning in Big Data Environment

Accurate emotion analysis of teaching evaluation texts can help teachers effectively improve the quality of education and teaching. In order to improve the precision and accuracy of emotion analysis, this paper proposes an emotion recognition and analysis method based on deep learning model. First, LTP tool is used to effectively process the teaching evaluation texts data set to improve the completeness and reliability of the data. Based on bidirectional long short-term memory (BiLSTM) network, an emotion analysis model is constructed to enhance the long-term memory ability of the model, so as to learn the emotion feature information more fully. On the basis of this model, the attention interaction mechanism module is introduced to pay attention to the important information in the attribute sequence, mine the deeper emotion feature information, and further ensure the accuracy of emotion recognition of teaching evaluation texts. Experimental simulation results show that the accuracy and precision of emotion recognition of the proposed method are 0.9123 and 0.8214, which can meet the needs of accurate emotion analysis of complex teaching evaluation texts.