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A Comprehensive Survey on Text Mining From Theory to Practice
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Author(s): Danial Zare (University of Alcala, Alcala de Henares, Spain), Luis Fernandez-Sanz (University of Alcala, Alcala de Henares, Spain), Vera Pospelova (University of Alcala, Alcala de Henares, Spain)and Ines López-Baldominos (University of Alcala, Alcala de Henares, Spain)
Copyright: 2025
Pages: 62
Source title:
Modern Methods for AI-Integrated Language Curriculum
Source Author(s)/Editor(s): Nayef Jomaa (University of Technology and Applied Sciences, Oman)
DOI: 10.4018/979-8-3693-9606-3.ch009
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Abstract
Text mining refers to the process of extracting useful information from large volumes of unstructured text data. This paper presents a comprehensive survey of text mining, covering foundational theories and practical applications across various domains within the field of Natural Language Processing (NLP). The study begins by examining the core challenges and historical development of text mining, providing context through an exploration of major areas where text mining techniques have significantly evolved. We offer an in-depth, step-by-step analysis of key algorithms in the text mining pipeline, beginning with data collection and preprocessing, moving through feature generation and selection, and highlighting their roles in transforming raw text into valuable insights. This survey serves as a guide for researchers and practitioners by detailing methodological approaches and considerations at each stage of the text mining process. Additionally, Pseudocode and Python implementations of algorithms are provided, facilitating the application of these methods in real-world scenarios.
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