Natural Language Processing (NLP): Large Language Models (LLMs), Sentiment Analysis, and Machine Translation

Natural Language Processing (NLP) has experienced exponential growth over the past decade, primarily driven by the development of Large Language Models (LLMs) based on transformer architectures. These models have demonstrated unprecedented abilities in understanding and generating human language, significantly improving performance in applications such as sentiment analysis and machine translation. This paper provides a comprehensive overview of NLP, explores the evolution and architecture of LLMs, and examines their deployment in sentiment analysis and neural machine translation. It discusses training methodologies, evaluation metrics, limitations, and ethical considerations associated with LLMs. Additionally, the paper analyzes emerging research trends and proposes future directions to enhance model efficiency, fairness, interpretability, and multilingual capabilities. By synthesizing current literature, empirical results, and theoretical insights, this study highlights both the transformative potential and the challenges inherent in modern NLP systems.