A Bibliometric Review of Natural Language Processing Applications in Psychology from 1991 to 2023
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Bibliographic record
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.011 | 0.051 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it