Abstract

This technical article explores the evolution and current state of Natural Language Processing (NLP), focusing on its fundamental components, sentiment analysis capabilities, language generation techniques, and implementation considerations. The article examines the transformation of NLP through transformer-based architectures, discussing advancements in text preprocessing, tokenization methods, and named entity recognition. It analyzes the progression of sentiment analysis from basic lexicon-based approaches to sophisticated neural architectures, highlighting improvements in contextual understanding and emotional context detection. The article also investigates modern language generation systems, their architectural innovations, and practical applications. Additionally, it addresses critical implementation considerations, including computational requirements, data quality concerns, and ethical implications, providing insights into the deployment challenges and solutions in real-world NLP applications.