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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.
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]
Copyright of Basic & Applied Social Psychology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: A Bibliometric Review of Natural Language Processing Applications in Psychology from 1991 to 2023.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Guanyu%22">Chen, Guanyu</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tan%2C+Bin%22">Tan, Bin</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Laham%2C+Nicolas%22">Laham, Nicolas</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tracey%2C+Terence+J%2E+G%2E%22">Tracey, Terence J. G.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lapinski%2C+Scott%22">Lapinski, Scott</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Yan%22">Liu, Yan</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Basic+%26+Applied+Social+Psychology%22">Basic & Applied Social Psychology</searchLink>. Mar/Apr2025, Vol. 47 Issue 2, p105-119. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+research%22">Psychological research</searchLink><br /><searchLink fieldCode="DE" term="%22Marketing%22">Marketing</searchLink><br /><searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Basic & Applied Social Psychology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=183195937
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/01973533.2024.2433720
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 105
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      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Sentiment analysis
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      – SubjectFull: Psychological research
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      – SubjectFull: Marketing
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      – SubjectFull: Bibliometrics
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              Text: Mar/Apr2025
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              Y: 2025
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