Analyzing Unstructured Data: Natural Language Processing Frameworks, Challenges, Approaches, and Applications

Today’s world data is about 80-90% unstructured, which makes it very complex to analyze, interpret and understand. Meaningful insights, patterns are hidden in the data, discovering knowledge and converting to structured form is important. It is also important to interpret in various forms and process the data. In this paper, we have presented an overview which includes various texts, their representation formats and finds Natural Language Processing (NLP), an essential component for interpreting the unstructured form. NLP components including its history, components, architecture, and challenges are well illustrated. Presented Research dives the evolution of NLP from rule-based systems to modern Deep Learning (DL) approaches. Key features and characteristics such as Natural Language Understanding (NLU), Natural Language Generation (NLG), and machine translation are discussed. The architectural framework of NLP systems is presented, furthermore, we address research and industrial challenges in NLP, emphasizing the need for robust methodologies, domain-specific changes, and ethical considerations.