Text classification using deep learning techniques: a bibliometric analysis and future research directions

IF 4.5 Q1 MANAGEMENT Benchmarking-An International Journal Pub Date : 2023-08-18 DOI:10.1108/bij-07-2022-0454
Gaurav Sarin, P. Kumar, M. Mukund
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Abstract

PurposeText classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.Design/methodology/approachThe authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.FindingsThe study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.Originality/valueThe study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.
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使用深度学习技术的文本分类:文献计量学分析及未来研究方向
目的文本分类是组织中广泛接受和采用的一种挖掘和分析非结构化和半结构化数据的技术。随着技术计算的进步,深度学习在学者和专业人士中越来越受欢迎,用于进行挖掘和分析操作。在这项工作中,作者研究了使用深度学习技术在文本分类领域进行的研究,以识别进行研究的差距和机会。设计/方法/方法作者采用了基于文献计量学的方法,结合可视化技术来发现新的见解和发现。作者从Scopus全球数据库中收集了20年的数据来进行这项研究。作者讨论了深度学习技术在文本分类中的商业应用。研究结果概述了文本分类和深度学习领域的各种出版物来源。该研究还列出了在这一领域工作的杰出作者及其所在国家的名单。作者还根据引用次数和研究国家列出了被引用次数最多的文章列表。利用词云、网络图、专题图等可视化技术对协作网络进行识别。原创性/价值在本文中进行的研究有助于理解研究差距,这是对文献的原创性贡献。据作者所知,在文本分类和深度学习领域还没有进行详细的深入研究。该研究为学者和专业人士提供了研究该领域的机会,具有很高的价值。
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来源期刊
CiteScore
10.40
自引率
16.10%
发文量
154
期刊介绍: Benchmarking is big news for companies committed to total quality programmes. Its enthusiastic reception by many prominent business figures has created high levels of interest in a technique which promises big rewards for co-operating partners. Yet, like total quality itself, it must be understood in its proper context, and implemented single mindedly if it is to be effective - this journal helps companies to decide if benchmarking is right for them, and shows them how to go about it successfully.
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