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Cited by 142Computational and Text Analysis MethodsSentiment Analysis and Opinion MiningMental Health via Writing

The DOI matches its Crossref record; 169 of the 169 references it cites were found in a registry or an index.

Using natural language processing to analyse text data in behavioural science

Feuerriegel, Stefan, Maarouf, Abdurahman, Bär, Dominik, Geissler, Dominique, Schweisthal, Jonas, Pröllochs, Nicolas, Robertson, Claire E., Rathje, Steve, Hartmann, Jochen, Mohammad, Saif M., Netzer, Oded, Siegel, Alexandra A., Plank, Barbara, Van Bavel, Jay J.·2025·Nature Reviews Psychology
DOI 10.1038/s44159-024-00392-z
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Crossref · 10.1038/s44159-024-00392-z

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169 of 169 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 168 references

Research works printed but not linked: 19

  1. Manning, C. & Schütze, H. Foundations of Statistical Natural Language Processing (MIT Press, 1999).
  2. Just, J., Ströhle, T., Füller, J. & Hutter, K. AI-based novelty detection in crowdsourced idea spaces. Innovation 6, 359–386 (2023).
  3. Yu, C. H., Jannasch-Pennell, A. & DiGangi, S. Compatibility between text mining and qualitative research in the perspectives of grounded theory, content analysis, and reliability. Qualitative Rep. 16, 730–744 (2011).
  4. Murphy, G. The Big Book of Concepts. (MIT Press, 2004).
  5. Mohammad, S. M., Sobhani, P. & Kiritchenko, S. Stance and sentiment in tweets. ACM Trans. Internet Technol. Argumentati. Soc. Media 17, 3 (2017).
  6. Le, Q. & Mikolov, T. Distributed representations of sentences and documents. In Proc. Int. Conf. Machine Learning 1188–1196 (PMLR, 2014)
  7. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. & Dean, J. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (eds. Burges, C. J. et al.) 3111–3119 (Curran Associates Inc., 2013).
  8. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding.In Proc. Conf. North American Chapter of the Assoc. Computational Linguistics (eds. Burstein, J., Doran, C. & Solorio, T.) 4171–4186 (ACL, 2019).
  9. Stone, P. J., Dunphy, D. C. & Smith, M. S. The General Inquirer: A Computer Approach to Content Analysis (The MIT Press, 1966).
  10. Baccianella, S., Esuli, A. & Sebastiani, F. SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In Proc. Seventh International Conference on Language Resources and Evaluation (LREC'10) (eds. Calzolari, N., et al.) http://www.lrec-conf.org/proceedings/lrec2010/pdf/769_Paper.pdf (European Language Resources Association, 2010).
  11. Atreja, S., Ashkinaze, J., Li, L., Mendelsohn, J. & Hemphill, L. Prompt design matters for computational social science tasks but in unpredictable ways. Preprint at https://doi.org/10.48550/arXiv.2406.11980 (2024). · DOI
  12. Balloccu, S., Schmidtová, P., Lango, M. & Dusek, O. Leak, cheat, repeat: data contamination and evaluation malpractices in closed-source LLMs. In Proc. Conf. European Chapter of the Assoc. Computational Linguistics (eds. Graham, Y. & Purver, M.) 67–93 (ACL, 2024).
  13. Blei, D. M., Ng, A. Y. & Jordan, M. I. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003).
  14. Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. Int. Conf. Knowledge Discovery and Data Mining (eds. Simoudis, E. et al.) 226–231 (AAAI, 1996).
  15. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
  16. Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. & Blei, D. Reading tea leaves: how humans interpret topic models. In Adv. Neural Inf. Process. Syst. (eds. Bengio, Y. et al.) 288–296 (Curran Associates Inc., 2009).
  17. Kosar, A., Pauw, Gde & Daelemans, W. Comparative evaluation of topic detection: humans vs. LLMs. Comput. Linguist. Neth. J. 13, 91–120 (2024).
  18. Yu, Y., Chen, L., Jiang, J. & Zhao, N. Measuring patent similarity with word embedding and statistical features. Data Anal. Knowl. Discov. 3, 53–59 (2019).
  19. Lederer, W. & Küchenhoff, H. A short introduction to the SIMEX and MCSIMEX. Newsl. R. Proj. 6, 26–31 (2006).

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Metadata

Authors
Feuerriegel, S., Maarouf, A., Bär, D., Geissler, D., Schweisthal, J., Pröllochs, N., Robertson, C. E., Rathje, S., Hartmann, J., Mohammad, S. M., Netzer, O., Siegel, A. A., Plank, B., & Van Bavel, J. J.
Affiliations
  • Munich Center for Machine Learning (DE)
  • Justus-Liebig-Universität Gießen (DE)
  • New York University (US)
  • Technical University of Munich (DE)
  • National Research Council Canada (CA)
  • Columbia University (US)
  • University of Colorado Boulder (US)
Corresponding author
Feuerriegel, Stefan
ORCID
Venue
Nature Reviews Psychology
Publisher
Springer Science and Business Media LLC
Published
2025-01-02
Volume
4
Issue
2
Pages
96-111
ISSN
2731-0574 (ISSN-L)
DOI
10.1038/s44159-024-00392-z
OpenAlex
W4405971183
Cited by
140
References
168
Citations by year
2026: 84 · 2025: 56
FWCI
30.57
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Nature Reviews Psychology (publishedVersion)
  • NPARC (submittedVersion)
Topics
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
Keywords
natural language processing, text analysis, behavioral science

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