Understanding Human Language through Natural Language Processing: A Comprehensive Review of Models, Applications, Challenges, and Future Directions

Abstract

Natural Language Processing (NLP) has become a central field in artificial intelligence, enabling computers to understand, analyze, and generate human language across different real-world applications. Despite rapid progress in transformer-based models and large language models, existing studies still report several challenges related to limited datasets, model adaptability, ethical concerns, bias, interpretability, and domain-specific implementation. This review aims to synthesize recent developments in NLP, analyze major application areas, and identify key limitations and future research directions. The study follows an integrative review approach by examining recent literature related to NLP techniques, models, applications, challenges, and evaluation measures. The review covers different domains, including healthcare, education, sentiment analysis, bioinformatics, cybersecurity, and the Metaverse. The findings show that transformer-based models such as BERT, GPT-3, and ChatGPT have significantly improved performance in tasks such as text classification, sentiment analysis, information extraction, and language generation. However, the analysis also indicates that issues such as low-resource languages, data privacy, model bias, lack of standardization, and limited reproducibility remain major barriers. The study concludes that future NLP research should focus on ethical AI development, domain-specific model adaptation, multimodal integration, open datasets, and stronger interdisciplinary collaboration to improve the reliability and practical impact of NLP systems.