This paper examines the role of ChatGPT and Natural Language Processing (NLP) in extracting actionable insights from unstructured data. It highlights transformer-based models’ ability to scale across domains such as business intelligence, healthcare, engineering, and cybersecurity. The study discusses strengths like contextual precision and flexibility, alongside challenges including hallucination, bias, and domain adaptation.
Keywords
ChatGPT, Natural Language Processing (NLP), Unstructured Data Analysis, Transformer Models, Artificial Intelligence Applications
Conclusion
ChatGPT and NLP offer transformative capabilities for unstructured data analysis. While not replacements for human expertise, they serve as powerful tools for scalable, context-aware decision-making. Future directions include integration with knowledge graphs, explainable AI, and multimodal systems to enhance reliability and interpretability.
References
Includes 11+ references from IEEE, Smart Learning Environments, J Big Data, and AI & Ethics covering ChatGPT, transformer models, and domain-specific NLP applications.
