Sentiment Analysis of Medical Comments Based on Character Vector Convolutional Neural Networks

Abstract

With the development of information technology, most of the medical institutions have established the web medical platform, and it will have a large number of patients’ evaluating textual information. These subjective text messages contain emotional information, such as views, opinions and attitudes of patients. It takes a lot of manpower and time to analyze and evaluate positive and negative evaluations by manual methods. Therefore, this paper presents a method of emotion evaluation of medical reviews based on character-level vector convolution neural network. Aiming at the problem of text input noise caused by word segmentation and polysemy, ignoring the structure information of sentence in traditional convolution neural network model, this paper proposes a segmentation pooling convolution neural network model based on character-level vector. Using the improved skip-gram model to train the character vector, and using different convolution kernels of different sizes to extract the sentence features, this proposed model then use the method of segmentation pooling to preserve the maximum eigenvalues of each part ofthe sentence. The experiments show that the accuracy of the proposed model in the emotional analysis of medical texts is about 12% higher than that ofthe traditional convolutional neural network model. In the actual task of emotion analysis of medical texts, the accuracy of the model is as high as 88.2 %.