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Linguistic Inquiry and Word Count (LIWC): Pronounced “Luke,” . . . and Other Useful Facts
Abstract
Linguistic Inquiry and Word Count (LIWC; Pennebaker, Booth, & Francis, 2007) is a word counting software program that references a dictionary of grammatical, psychological, and content word categories. LIWC has been used to efficiently classify texts along psychological dimensions and to predict behavioral outcomes, making it a text analysis tool widely used in the social sciences. LIWC can be considered to be a tool for applied natural language processing since, beyond classification, the relative uses of various LIWC categories can reflect the underlying psychology of demographic characteristics, honesty, health, status, relationship quality, group dynamics, or social context. By using a comparison group or longitudinal information, or validation with other psychological measures, LIWC analyses can be informative of a variety of psychological states and behaviors. Combining LIWC categories using new algorithms or using the processor to assess new categories and languages further extend the potential applications of LIWC.
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Tripto, N., Uchendu, A., Le, T., Setzu, M., Giannotti, F., & Lee, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.916 · Cited by 6
The wicked and the logical: Facilitating integrative learning among introductory computing students
Stone, J., & Cruz, L. (2021) · Teaching & Learning Inquiry · doi:10.20343/teachlearninqu.9.1.13 · Cited by 5
Tong, S. T., DeTone, A., Frederick, A., & Odebiyi, S. (2025) · Communication Education · doi:10.1080/03634523.2025.2477479 · Cited by 4
Metadata
- Authors
- Chung, C. K., & Pennebaker, J. W.
- Venue
- Applied Natural Language Processing
- Publisher
- IGI Global
- Published
- 2012
- Pages
- 206-229
- OpenAlex
- W2489406233
- Cited by
- 2,151
- References
- 13
- FWCI
- 251.54
- Type
- book chapter
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.