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Analysis of the emotional coloring of text using machine and deep learning methods

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dc.contributor.author Abdykerimova, Lazzat
dc.contributor.author Abdikerimova, Gulzira
dc.contributor.author Konyrkhanova, Assem
dc.contributor.author Nurova, Gulsara
dc.contributor.author Bazarova, Madina
dc.contributor.author Bersugir, Mukhamedi
dc.contributor.author Kaldarova, Mira
dc.contributor.author Yerzhanova, Akbota
dc.date.accessioned 2024-11-21T11:40:46Z
dc.date.available 2024-11-21T11:40:46Z
dc.date.issued 2024
dc.identifier.issn 2088-8708
dc.identifier.other DOI: 10.11591/ijece.v14i3.pp3055-3063
dc.identifier.uri http://rep.enu.kz/handle/enu/19171
dc.description.abstract The presented scientific article is a comprehensive study of machine learning and deep learning methods in the context of emotion recognition in text data. The main goal of the study is to conduct a comprehensive analysis and comparison of various machine learning and deep learning methods to classify emotions in text. During the work, special attention was paid to the analysis of traditional machine learning algorithms, such as multinomial naive Bayes (MNB), multilayer perceptron (MLP), and support vector machine (SVM), as well as the use of deep learning methods based on long short-term memory (LSTM). The experimental part of the study involves the analysis of different data sets covering a variety of text styles and contexts. The experimental results are analyzed in detail, identifying the advantages and limitations of each method. The article provides practical recommendations for choosing the optimal method depending on the specific tasks and context of the application. The data obtained is important for the development of intelligent systems that can effectively adapt to the emotional aspects of interaction with users. Overall, this work makes a significant contribution to the field of emotion recognition in text and provides a basis for further research in this area. ru
dc.language.iso en ru
dc.publisher International Journal of Electrical and Computer Engineering ru
dc.relation.ispartofseries Vol. 14, No. 3, June 2024, pp. 3055-3063;
dc.subject Deep learning ru
dc.subject Emotional coloring ru
dc.subject Long short-term memory ru
dc.subject Multilayer perceptron ru
dc.subject Multinomial naive ru
dc.subject Bayes ru
dc.subject Support vector machine ru
dc.title Analysis of the emotional coloring of text using machine and deep learning methods ru
dc.type Article ru


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