TBLC-rAttention: A Deep Neural Network Model for Recognizing the Emotional Tendency of Chinese Medical Comment

In the current paper, a hybrid depth neural network model, TBLC-rAttention, aiming at Chinese text emotion recognition, is proposed to identify the emotional tendency of the Chinese medical reviews. The model includes the following steps: acquiring and preprocessing the Chinese corpus; mapping the preprocessed text into the word vectors; using Bi-directional Long Short-Term Memory network (Bi-LSTM) with the attention mechanism to acquire the context semantic features of the text; using Convolutional Neural Network (CNN) to obtain local semantics features on the basis of the context semantic features; and inputting the final feature vectors into the classification layer to complete the task of emotion recognition and the classification of the Chinese medical reviews. In this experiment, the corpus data is the comments of 999 cold medicine on a large e-commerce platform. All corpus are divided into three types, including high praise, medium praise and bad review. Classical machine learning models (SVM, NB) and neural network models (CNN, LSTM, Bi-LSTM, BiLSTM-Attention and RCNN) are performed as the comparison benchmarks to assess the category performance of TBLC-rAttention model. All the results were obtained when the training accuracy and test accuracy were stable after 1000 cycles of repeated calculation. The results show that TBLC-rAttention can get better text feature than the reference models, and the text classification accuracy reaches to 99%. In conclusion, the TBLC-rAttention model can identify semantic feature information to the greatest extent. In addition, this study also completes the numerical quantification of the predicted results.