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The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis

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  • Haoran Zhu
  • Lei Lei

Abstract

Text Classification (TC) is the process of assigning several different categories to a set of texts. This study aims to evaluate the state of the arts of TC studies. Firstly, TC-related publications indexed in Web of Science were selected as data. In total, 3,121 TC-related publications were published in 760 journals between 2000 and 2020. Then, the bibliographic information was mined to identify the publication trends, important contributors, publication venues, and involved disciplines. Besides, a thematic analysis was performed to extract topics with increasing/decreasing popularity. The findings showed that TC has become a fast-growing interdisciplinary area, and that emerging research powers such as China are playing increasingly important roles in TC research. Moreover, the thematic analysis showed increased interest in topics concerning advanced classification algorithms, performance evaluation methods, and the practical applications of TC. This study will help researchers recognize the recent trends in the area.

Suggested Citation

  • Haoran Zhu & Lei Lei, 2022. "The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis," SAGE Open, , vol. 12(2), pages 21582440221, April.
  • Handle: RePEc:sae:sagope:v:12:y:2022:i:2:p:21582440221089963
    DOI: 10.1177/21582440221089963
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    References listed on IDEAS

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    1. Ching-Hsue Cheng & Hsien-Hsiu Chen, 2019. "Sentimental text mining based on an additional features method for text classification," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-17, June.
    2. Jaeyoung Kim & Janghyeok Yoon & Eunjeong Park & Sungchul Choi, 2020. "Patent document clustering with deep embeddings," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 563-577, May.
    3. Moshe Koppel & Jonathan Schler & Shlomo Argamon, 2009. "Computational methods in authorship attribution," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 9-26, January.
    4. Nourah F. Bin Hathlian & Alaaeldin M. Hafez, 2017. "Subjective Text Mining for Arabic Social Media," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(2), pages 1-13, April.
    5. Shaher H. Zyoud & Ahed H. Zyoud, 2021. "Coronavirus disease-19 in environmental fields: a bibliometric and visualization mapping analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 8895-8923, June.
    6. Nektaria Potha & Efstathios Stamatatos, 2019. "Improving author verification based on topic modeling," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(10), pages 1074-1088, October.
    7. repec:uts:ppaper:2013:3 is not listed on IDEAS
    8. El‐Sayed Atlam & Kazuhiro Morita & Masao Fuketa & Jun‐ichi Aoe, 2011. "A new approach for Arabic text classification using Arabic field‐association terms," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(11), pages 2266-2276, November.
    9. Maryam Ebrahimpour & Tālis J Putniņš & Matthew J Berryman & Andrew Allison & Brian W-H Ng & Derek Abbott, 2013. "Automated Authorship Attribution Using Advanced Signal Classification Techniques," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-12, February.
    10. Sebnem Cansun & Engin Arik, 2018. "Political science publications about Turkey," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 169-188, April.
    11. Paul Donner, 2017. "Document type assignment accuracy in the journal citation index data of Web of Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 219-236, October.
    12. Jaime A. Teixeira da Silva & Judit Dobránszki, 2018. "Multiple versions of the h-index: cautionary use for formal academic purposes," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 1107-1113, May.
    13. Dragomir R. Radev & Mark Thomas Joseph & Bryan Gibson & Pradeep Muthukrishnan, 2016. "A bibliometric and network analysis of the field of computational linguistics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(3), pages 683-706, March.
    14. El-Sayed Atlam & Kazuhiro Morita & Masao Fuketa & Jun-ichi Aoe, 2011. "A new approach for Arabic text classification using Arabic field-association terms," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(11), pages 2266-2276, November.
    15. Daphne R. Raban & Avishag Gordon, 2020. "The evolution of data science and big data research: A bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1563-1581, March.
    16. Efstathios Stamatatos, 2009. "A survey of modern authorship attribution methods," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 538-556, March.
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