An implementation of social network group classification model based on recurrent and convolution neural networks
In this article, the authors consider the problem of classifying communities in the social network VKontakte. The application of neural networks for the classification of user groups according to the degree of radicality is investigated. In the work, a model of a recurrent long short term memory (LSTM) neural network is built using modern software technologies and methodologies. The resulting model is trained on a test dataset and is also evaluated using the selected metrics, such as F1, accuracy and loss. A convolutional neural network model is also built and evaluated. Subsequently, these models are compared with each other. The model which is based on convolutional neural networks had the higher value of metrics, than one based on the LSTM model. Methods for preprocessing data, as well as using the Keras framework for Python with Tensorflow backend for building neural network classifiers are proposed. The resulting convolutional network model is applicable to the core dataset when searching for radical communities on social networks. The research of the model is carried out and the analysis of the results is presented.