AN ADVANCED NATURAL LANGUAGE PROCESSING FRAMEWORK FOR AUTOMATED TEXT ANALYSIS AND UNDERSTANDING

Abstract

Natural Language Processing (NLP) has emerged as a key technology for enabling machines to understand, interpret, and generate human language. The increasing volume of unstructured textual data from digital platforms necessitates advanced frameworks for automated text analysis. This paper presents an advanced NLP framework designed to enhance text understanding through intelligent pre-processing, feature representation, and learning models. The proposed framework integrates linguistic analysis with machine learning and deep learning techniques. It supports tasks such as text classification, sentiment analysis, and semantic understanding. The framework emphasizes scalability, accuracy, and adaptability to diverse domains. Experimental evaluation demonstrates improved performance compared to conventional NLP approaches. Results indicate significant gains in precision and contextual understanding. The framework is suitable for real-world applications such as information retrieval and decision support systems. Overall, the proposed solution advances automated text analytics.