title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¼Ð¾Ð´ÑÐ»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÑÐ¾Ð½Ð°Ð»ÑÐ½Ð¾ÑÑÐ¸ ÑÐµÐºÑÑÐ¾Ð²ÑÑ ÑÐ¾Ð¾Ð±ÑÐµÐ½Ð¸Ð¹ Ñ Ð¿ÐµÑÐµÑÐµÐºÐ°ÑÑÐ¸Ð¼Ð¸ÑÑ ÐºÐ»Ð°ÑÑÐ°Ð¼Ð¸
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 program module for sentiment analysis with intersectingclasses
Creators: name: ÐÐ°Ð²ÑÐ¾Ð²Ð°, ÐÑÐ±Ð¾Ð²Ñ
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
Ð¼ÑÐ»ÑÑÐ¸ÐºÐ»Ð°ÑÑÐ¾Ð²Ð°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ¾Ð½Ð°Ð»ÑÐ½Ð¾ÑÑÐ¸
Ð¿ÐµÑÐµÑÐµÐºÐ°ÑÑÐ¸ÐµÑÑ ÐºÐ»Ð°ÑÑÑ
Ð¼ÑÐ³ÐºÐ¾Ðµ ÑÐµÑÐµÐ½Ð¸Ðµ
machine learning
multiclass classification
tonality analysis
overlapping classes
soft decision

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description: The subject of the study is the tonality of texts. The purpose of the research is development and implementing a methodology for multiclass tonality analysis that takes into account the intersection of emotional classes. Developed methodology consists in creating a model separating multiclass texts from single-class ones, a model classifying single-class texts, and a model classifying multi -class texts. The developed technique showed the best values of the F-measure in comparison with classical neural network models using a soft decision for text classification. The main focus of the work is on the analysis existing approaches and tools for analyzing tonality applied to comments on social media, in particular on YouTube. Traditional methods of classifying comments are usually limited to defining general categories of sentiment, such as positive, neutral, and negative reviews. This classification does not allow deep analysis of specific emotions, which significantly reduces the quality of understanding user reviews and moods.The results of the work can be applied in the field of analysis comments on social media platforms.