Classification of Emoji in Text Documents of Users in Social Networks Using Machine Learning

This paper presents a study conducted to classify emotions in social media texts using machine learning. With the rapid increase in the amount of data in digital media, efficient and highly accurate methods for analyzing the emotional content of texts are needed. It is shown that modern deep learning algorithms, natural language processing methods, and feature extraction methods work well for emotion analysis tasks. Data preprocessing methods such as lemmatization, normalization, and text cleaning are given special attention since they are crucial to improving the quality of analysis. The paper also discusses the performance metrics such as precision, recall, and F1-score to demonstrate the advantages of the proposed methods. Furthermore, the current challenges in processing unstructured social media data such as user review analysis and social trend detection are discussed. By leveraging multimodal data and modern context-aware models, the work places special emphasis on improving emotion classification. It is important to leverage advanced machine learning methods to improve text analysis in the dynamic context of social media.

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