title: ÐÐ¾Ð¼Ð±Ð¸Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð² Ð·Ð°Ð´Ð°ÑÐµ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐµÐºÑÑÐ¾Ð² Ð¿Ð¾ ÑÐ¼Ð¾ÑÐ¸Ð¾Ð½Ð°Ð»ÑÐ½Ð¾ÑÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Artificial neural networks and genetic algorithm combination in the problem of text classification by emotionality
Creators: name: ÐÐ¾Ð»Ð¾Ð½Ð´ÐµÐ²ÑÐºÐ°Ñ, ÐÐ¸ÐºÑÐ¾ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼
ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¿Ð¾Ð´Ð±Ð¾Ñ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ°
combination
genetic algorithm
convolutional neural network
classification
selection of hyperparameter

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description: This article considers artificial neural networks used for classifying texts, describes the essence of approaches to combining, and analyzes existing implementations of combining. The main operators of the genetic algorithm and its differences with traditional optimization methods are described. The architecture of a convolutional neural network is described with detailed characteristics of each layer, and the main hyperparameters of the network are noted. A prototype of a system for solving the problem of binary classification of texts based on their emotional color, based on a convolutional neural network, has been developed. The article presents the network testing and selection of the genetic algorithm fitness function based on it. A genetic algorithm is Implemented to predict the number of network filters needed to obtain a given or maximum percentage of recognizability under a set time limit. The results of the research can be used in areas related to online content analysis.