Text Mining using Big Data

Bys: Ahmed, Rafeeq and Ahmad, Tanvir

This book presents a comprehensive study on text mining using big data analytics with a novel fuzzy logic-based approach. As the volume of unstructured textual data grows exponentially across digital platforms, extracting meaningful knowledge from large-scale datasets has become a significant challenge. The book introduces advanced methodologies that integrate big data frameworks with fuzzy set theory to address uncertainty, ambiguity, and vagueness inherent in textual information. It explores preprocessing techniques, feature extraction, semantic analysis, and scalable mining strategies designed for high-dimensional data environments. A fuzzy inference model is proposed to enhance classification, clustering, and decision-making accuracy in complex text datasets. Experimental evaluations demonstrate improved performance compared to traditional crisp-based models. This work serves as a valuable resource for researchers, academicians, and practitioners working in data science, artificial intelligence, and big data analytics, providing both theoretical foundations and practical implementations for intelligent text mining systems.

• Format: Paperback
• Language: English
• ISBN: 6209795307
• ISBN13: 9786209795305
• Release Date: March 2026
• Publisher: LAP Lambert Academic Publishing
• Length: 156 Pages
• Weight: 0.48 lbs.
• Dimensions: 0.4" x 6.0" x 9.0"