Methodology for solving problems of classification of appeals/requests of citizens to the “hotline” of the President of the Russian Federation

Abstract

The use of neural networks for the classification of text data is an important area of digital transformation of socio-economic systems. The article is devoted to the description of the methodology for classifying citizens' appeals. The proposed technique involves the use of a convolutional neural network. The stages of processing citizens' appeals in the amount of 7000 appeals are described. In order to reduce the dimension of the problem, methods of filtering and removing stop words were applied. The resulting data set allows you to choose the best classifier in terms of accuracy, specificity, sensitivity. Training and test samples were used, as well as cross-validation. The article shows the effectiveness of using this method to distribute requests on 15 topics of citizens' appeals to the “hotline” of the President of the Russian Federation. Automating the classification of received appeals by topic allows them to be processed quickly for further study by the relevant departments. The purpose of the study is automation of the distribution of citizens' appeals to the President's hotline by category based on the use of modern machine learning methods. Materials and methods. The development of software that automates the process of distributing citizens into categories is carried out using a convolutional neural network written in the Python programming language. Results. With the help of the prepared data set, the pre-trained model of NL BERT and sciBERT was trained by the deep learning method. The model shows an accuracy of 86% in the estimates of quality metrics. Conclusion. A pre-trained model was trained using a convolutional neural model using a prepared data set. Even if the forecast does not match the real category, the model gives a minor error, correctly determines the category of the appeal. The results obtained can be recommended for practical application by authors of scientific publications, scientific institutions, editors and reviewers of publishing houses.