title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¼Ð¾Ð´ÑÐ»Ñ-ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¿Ð¾Ð»Ð° Ð°Ð²ÑÐ¾ÑÐ° Ð¿Ð¾ ÑÐµÐºÑÑÑ ÐºÐ¾Ð¼Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
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: Development of a software module-classifier for determining the authors gender based on comment text
Creators: name: Ð ÑÐ¶Ð¾Ð²Ð°, ÐÐ¸Ð»Ð°Ð½Ð°
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: 2024
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐµÐºÑÑÐ°
Ð´Ð²ÑÑÐ·ÑÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·
Ð¿Ð¾Ð»Ð¾Ð²Ð°Ñ Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð²
machine learning
text classification
bilingual analysis
gender identification
hyperparameter optimization

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description: The purpose of this work is to develop a software module-classifier that allows you to determine the gender of the author from the text of the comment in Russian and English. The object of the study is textual information presented in the form of comments in Russian and English. The subject of the research is machine learning methods and algorithms used for preprocessing, vectorization and classification of texts to determine the gender of the author. The work included analysis of existing solutions and research, review and selection of suitable data sets, implementation of various text preprocessing methods, and use of text vectorization methods. Various machine learning models have been researched and tested. As a result, the optimal models for Russian and English texts were selected: naive Bayes for Russian and logistic regression for English. During the development process, a web application was created that integrates selected machine learning models and provides users with the ability to classify texts in real time. The web application has undergone comprehensive testing, including server testing, unit testing, testing and evaluation of model performance. The use of gender determination algorithms helps improve the efficiency of many processes. In particular, in marketing and advertising, this allows you to customize advertising campaigns taking into account the gender characteristics of the audience, in cybersecurity it helps to identify fraudulent schemes, and on social media platforms it improves the personalization of content.