全面考察文本分类技术及其研究应用:观察与实验启示

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Kamal Taha , Paul D. Yoo , Chan Yeun , Dirar Homouz , Aya Taha
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引用次数: 0

摘要

文本数据的指数级增长给管理和分析带来了巨大挑战,主要原因是存储和处理成本高昂。文本分类是文本挖掘的一个重要方面,它通过对文本数据进行有效的分类和组织,提供了强大的解决方案。这些技术使个人、研究人员和企业能够从大量文本中获得有意义的模式和见解。本调查论文介绍了一种专门为基于研究领域的文本分类而设计的综合分类法。该分类法分为多个层次:基于研究领域的类别、基于研究领域的子类别、基于方法论的技术、方法论子技术和研究领域应用。我们采用了双重评估方法:实证和实验。在实证方面,我们根据四个关键标准对文本分类技术进行评估。在实验方面,我们对同一方法技术中的方法子技术和同一研究领域子类别中的方法子技术进行比较和排序。这种结构化的分类法与全面的评估相结合,提供了对文本分类算法及其应用的细致入微的了解,使研究人员能够根据精确的、特定领域的见解做出明智的决定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comprehensive survey of text classification techniques and their research applications: Observational and experimental insights

The exponential growth of textual data presents substantial challenges in management and analysis, notably due to high storage and processing costs. Text classification, a vital aspect of text mining, provides robust solutions by enabling efficient categorization and organization of text data. These techniques allow individuals, researchers, and businesses to derive meaningful patterns and insights from large volumes of text. This survey paper introduces a comprehensive taxonomy specifically designed for text classification based on research fields. The taxonomy is structured into hierarchical levels: research field-based category, research field-based sub-category, methodology-based technique, methodology sub-technique, and research field applications. We employ a dual evaluation approach: empirical and experimental. Empirically, we assess text classification techniques across four critical criteria. Experimentally, we compare and rank the methodology sub-techniques within the same methodology technique and within the same overall research field sub-category. This structured taxonomy, coupled with thorough evaluations, provides a detailed and nuanced understanding of text classification algorithms and their applications, empowering researchers to make informed decisions based on precise, field-specific insights.

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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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