A Bibliometric Review of Natural Language Processing Applications in Psychology from 1991 to 2023.
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| Title: | A Bibliometric Review of Natural Language Processing Applications in Psychology from 1991 to 2023. |
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| Authors: | Chen, Guanyu (AUTHOR), Tan, Bin (AUTHOR), Laham, Nicolas (AUTHOR), Tracey, Terence J. G. (AUTHOR), Lapinski, Scott (AUTHOR), Liu, Yan (AUTHOR) |
| Source: | Basic & Applied Social Psychology. Mar/Apr2025, Vol. 47 Issue 2, p105-119. 15p. |
| Subjects: | Natural language processing, Sentiment analysis, Psychological research, Marketing, Bibliometrics |
| Abstract: | Natural language processing (NLP) has emerged as a promising approach in psychological research. However, prior reviews often focused on specific subject areas and reported varying findings regarding the use of NLP methods. To address this gap in the literature, we conducted a comprehensive review of NLP applications in psychological research. Our study includes (1) a large-scale bibliometric review of 4,909 papers (1991–2023) and (2) a focused methodological review of the 100 most-cited articles. Results revealed exponential growth in NLP applications since 2012, with Health, Education, and Marketing as dominant topics. Sentiment analysis was the most common technique, deep learning—particularly pre-trained models—gained popularity, and automated text analysis tools were widely used due to their ease of implementation. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Natural language processing (NLP) has emerged as a promising approach in psychological research. However, prior reviews often focused on specific subject areas and reported varying findings regarding the use of NLP methods. To address this gap in the literature, we conducted a comprehensive review of NLP applications in psychological research. Our study includes (1) a large-scale bibliometric review of 4,909 papers (1991–2023) and (2) a focused methodological review of the 100 most-cited articles. Results revealed exponential growth in NLP applications since 2012, with Health, Education, and Marketing as dominant topics. Sentiment analysis was the most common technique, deep learning—particularly pre-trained models—gained popularity, and automated text analysis tools were widely used due to their ease of implementation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01973533 |
| DOI: | 10.1080/01973533.2024.2433720 |
