Research on Emotion Analysis and Psychoanalysis Application With Convolutional Neural Network and Bidirectional Long Short-Term Memory

Abstract

This study mainly focuses on the emotion analysis method in the application of psychoanalysis based on sentiment recognition. The method is applied to the sentiment recognition module in the server, and the sentiment recognition function is effectively realized through the improved convolutional neural network and bidirectional long short-term memory (C-BiL) model. First, the implementation difficulties of the C-BiL model and specific sentiment classification design are described. Then, the specific design process of the C-BiL model is introduced, and the innovation of the C-BiL model is indicated. Finally, the experimental results of the models are compared and analyzed. Among the deep learning models, the accuracy of the C-BiL model designed in this study is relatively high irrespective of the binary classification, the three classification, or the five classification, with an average improvement of 2.47% in Diary data set, 2.16% in Weibo data set, and 2.08% in Fudan data set. Therefore, the C-BiL model designed in this study can not only successfully classify texts but also effectively improve the accuracy of text sentiment recognition.