Regular Expressions: Understanding and Application for Processing Text Big Data
Soo-kyung Lim, Byeongkwu Kang · 중국어문학논집 · 2025
In the realm of contemporary big data research, this study investigates advanced preprocessing and analytical strategies for effective Information Extraction and insight generation from large-scale text data. EmEditor serves as the primary platform due to its robust handling of extensive textual datasets and its powerful Regular Expressions (regex) capabilities. By leveraging EmEditor’s regex functionalities, the paper introduces practical methodologies for optimizing text data preprocessing and analysis, ultimately accelerating workflow efficiency and clarity. These regex-driven approaches foster greater productivity and efficacy in language-related studies as well as broader data-driven research endeavors, thereby underscoring their value in the efficient management of big data.