Text Classification of Public Feedbacks Using Convolutional Neural Network Based on Differential Evolution Algorithm
Online feedback is an effective way of communication between government departments and citizens.However, the daily high number of public feedbacks has increased the burden on government administrators.The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction.In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator.In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted.At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.
public feedback deep learning text classification convolutional neural network differential evolution algorithm
Yong Chen Xiaoling Huang Zhijia Yan Shuai Zhang Yishuai Cai
School of Information Zhejiang University of Finance and Economics Hangzhou,China School of International Education,Zhejiang University of Finance and Economics Hangzhou, China School of Information Technology Zhejiang Yuying College of Vocational Technology Hangzhou,China
国际会议
郑州
英文
398-404
2018-09-21（万方平台首次上网日期，不代表论文的发表时间）