NATURAL LANGUAGE PROCESSING (NLP) AND SENTIMENT ANALYSIS

Abstract

This article discusses the theoretical foundations, key concepts, and practical application areas of Natural Language Processing (NLP). As an integrative field of artificial intelligence and computational linguistics, NLP is aimed at solving problems related to the automatic analysis, understanding, and generation of text and speech.[1:12] The article summarizes the typical tasks of NLP, including tokenization, lemmatization, morphological and syntactic analysis, named entity recognition, sentiment analysis, machine translation, question-answering systems, and text summarization. [7:15]In addition, development trends ranging from classical approaches to modern deep learning and transformer architectures, as well as the significance of NLP solutions in education, healthcare, banking and finance, media monitoring, call centers, and public services are analyzed. The study substantiates the necessity of considering data quality, language resources, ethical issues, and security requirements in the implementation of NLP technologies.