We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Analysis of the emotional coloring of text using machine and deep learning methods.
- Authors
Abdykerimova, Lazzat; Abdikerimova, Gulzira; Konyrkhanova, Assem; Nurova, Gulsara; Bazarova, Madina; Bersugir, Mukhamedi; Kaldarova, Mira; Yerzhanova, Akbota
- 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.
- Subjects
DEEP learning; ARTIFICIAL intelligence; MACHINE learning; TEXT recognition; EMOTION recognition; SUPPORT vector machines
- Publication
International Journal of Electrical & Computer Engineering (2088-8708), 2024, Vol 14, Issue 3, p3055
- ISSN
2088-8708
- Publication type
Article
- DOI
10.11591/ijece.v14i3.pp3055-3063