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.
