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Data wrangling, the process of cleaning, transforming, and mapping raw data into a usable format, is a fundamental step in linguistic research. With the exponential growth of digital text and spoken language data, computational methods have become essential for managing and analyzing large datasets. This paper explores the latest computational techniques in data wrangling for linguistics, highlighting advancements in machine learning, natural language processing (NLP), and automated data cleaning technologies. These innovations enhance the efficiency and accuracy of data processing, enabling researchers to uncover patterns and generate insights previously unattainable. Additionally, the paper addresses the challenges inherent in data wrangling, including the integration of diverse data sources, the complexity of real-time data processing, and the importance of maintaining data quality and compliance with privacy regulations. Through a detailed examination of current methodologies and emerging trends, this research underscores the critical role of data wrangling in advancing linguistic studies and offers practical solutions for overcoming common obstacles in the field. The findings suggest that continued advancements in AI-driven tools and automated solutions will significantly impact the future of linguistic data analysis, making it more accessible and effective.
Ahmad et al. (Sat,) studied this question.