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Mohammad Ali Soltani * 
, Peyman MamSharifi , Reza Salehi Chegeni , Ali Safari , Hamidreza Keshavarz , Daniel Rezaei Rouzbahani , Majid Afshari , MohamadSajad Ghafouri , Mojtaba Mohammadi , Fateme Tavakoli
, Peyman MamSharifi , Reza Salehi Chegeni , Ali Safari , Hamidreza Keshavarz , Daniel Rezaei Rouzbahani , Majid Afshari , MohamadSajad Ghafouri , Mojtaba Mohammadi , Fateme Tavakoli
Department of Measurement, Allameh Tabataba’i University, Tehran, Iran. , mohalisoltani@gmail.com
Abstract: (18 Views)
Background: The Linguistic Inquiry and Word Count (LIWC) is recognized as one of the most prominent tools for text analysis. By utilizing computational algorithms and linguistic processing techniques, it can identify and examine various elements within textual data, such as emotions, cognitive processes, attitudes, vocabulary, language styles, and patterns of social interaction. The absence of a localized Persian version of this tool served as one of the primary motivations for conducting the present study.
Aims: The aim of this study was to develop and examine the psychometric properties of the Persian Linguistic Inquiry and Word Count system (P-LIWC).
Methods: The present study employed a descriptive-correlational design. The process of developing the LIWC categories consisted of several stages. The development of the dictionary began with the initial translation of words from the original English version. Then, to maximize word coverage in each category in Persian, a large corpus of Persian texts was analyzed using text analysis methods. In the next step, the translated words were evaluated by psychologist judges, and for the approved words, their lemmas were determined by linguists to identify different word forms. After completing these stages, the reliability of the category and subcategory dictionaries was examined using Cronbach’s alpha and the Kuder–Richardson 20 coefficients. To assess external validity, the equivalence between the Persian version (P-LIWC) and the original English LIWC-2022 was analyzed.
Results: The results showed that all categories of the Persian Linguistic Inquiry and Word Count system were significantly correlated with the final English version (LIWC-2022) (p<0.01), indicating that the instrument possesses good validity and reliability.
Conclusion: Based on the findings of this study, it can be concluded that the software has the potential to be applied across a wide range of research domains and in the analysis of Persian textual data. Furthermore, it can be effectively utilized for examining the content of therapy session transcripts from clients in psychological clinics.
Aims: The aim of this study was to develop and examine the psychometric properties of the Persian Linguistic Inquiry and Word Count system (P-LIWC).
Methods: The present study employed a descriptive-correlational design. The process of developing the LIWC categories consisted of several stages. The development of the dictionary began with the initial translation of words from the original English version. Then, to maximize word coverage in each category in Persian, a large corpus of Persian texts was analyzed using text analysis methods. In the next step, the translated words were evaluated by psychologist judges, and for the approved words, their lemmas were determined by linguists to identify different word forms. After completing these stages, the reliability of the category and subcategory dictionaries was examined using Cronbach’s alpha and the Kuder–Richardson 20 coefficients. To assess external validity, the equivalence between the Persian version (P-LIWC) and the original English LIWC-2022 was analyzed.
Results: The results showed that all categories of the Persian Linguistic Inquiry and Word Count system were significantly correlated with the final English version (LIWC-2022) (p<0.01), indicating that the instrument possesses good validity and reliability.
Conclusion: Based on the findings of this study, it can be concluded that the software has the potential to be applied across a wide range of research domains and in the analysis of Persian textual data. Furthermore, it can be effectively utilized for examining the content of therapy session transcripts from clients in psychological clinics.
Article number: 5
Keywords: Linguistic Inquiry and Word Count System, natural language processing, psychometrics, text mining.
Type of Study: Research |
Subject:
Special
Received: 2025/10/11 | Accepted: 2026/02/9 | Published: 2026/07/23
Received: 2025/10/11 | Accepted: 2026/02/9 | Published: 2026/07/23
References
1. سلطانی، محمد علی؛ مام شریفی، پیمان؛ صالحی چگنی، رضا؛ صفری، علی؛ ارشادی منش، سودابه؛ کشاورز، حمیدرضا؛ و همکاران. (1403). توسعه و بررسی ویژگی های روان سنجی نسخه فارسی تحقیق زبانی و شمارش کلمات (P-LIWC) طبقات هیجانات و فرآیندهای شناختی. مجله علوم روانشناختی، ۲۳ (۱۳۷)، ۱۹-۱. http://dx.doi.org/10.52547/JPS.23.137.997
2. کاویانی، حسین؛ پور ناصح، مهرانگیز؛ و گلفام، ارسلان. (1384). تقابل واژههای هیجان و شناخت در لغتنامههای زبان فارسی. تازههای علوم شناختی، ۷ (۲)، ۲۹-۳۷. http://icssjournal.ir/article-1-134-fa.html
3. مام شریفی، پیمان؛ سهرابی، فرامرز؛ برجعلی، احمد؛ زمان پور، عنایت اله؛ و حسین ثابت، فریده. (۱۴۰۵). واکاوی روانی-زبانی ابعاد شناختی، هیجانی و جنسی هرزهنگاری در شبکههای اجتماعی اینستاگرام، تلگرام و ایکس. مجله علوم روانشناختی، ۲۵ (۱۶۰)، ۱۸-۱. http://dx.doi.org/10.66224/jps.25.160.1
4. کاویانی، حسین؛ موسوی، اشرفسادات؛ و گلفام، ارسلان. (1386). کاوش در واژههای روانشناختی (هیجان، شناخت، آسیبشناسی، درمان و شخصیت)، در لغتنامه (فرهنگ) های زبان فارسی. زبان و زبانشناسی، 3(5)، 89-102. https://lsi-linguistics.ihcs.ac.ir/article_1606.html
5. Andy, A., & Andy, U. (2021). Understanding Communication in an Online Cancer Forum: Content Analysis Study. JMIR cancer, 7(3), e29555. https://doi.org/10.2196/29555
6. Bahgat, M., Wilson, S., & Magdy, W. (2022, June). LIWC-UD: classifying online slang terms into LIWC categories. In Proceedings of the 14th ACM Web Science Conference 2022 (pp. 422-432). https://doi.org/10.1145/3501247.3531572
7. Bjekić, J., Lazarević, L. B., Živanović, M., & Knežević, G. (2014). Psychometric evaluation of the Serbian dictionary for automatic text analysis-LIWCser. Psihologija, 47(1), 5-32. https://doi.org/10.2298/PSI1401005B
8. Boyd, R. L., Ashokkumar, A., Seraj, S., & Pennebaker, J. W. (2022). The development and psychometric properties of LIWC-22. Austin, TX: University of Texas at Austin. https://www.liwc.app/help/psychometrics-manuals
9. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901. https://doi.org/10.48550/arXiv.2005.14165
10. Del Pilar Salas-Zárate, M., López-López, E., Valencia-García, R., Aussenac-Gilles, N., Almela, Á., & Alor-Hernández, G. (2014). A study on LIWC categories for opinion mining in Spanish reviews. Journal of Information Science, 40(6), 749-760. http://dx.doi.org/10.1177/0165551514547842
11. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. https://doi.org/10.48550/arXiv.1810.04805
12. Dwyer, A., de Almeida Neto, A., Estival, D., Li, W., Lam-Cassettari, C., & Antoniou, M. (2021). Suitability of Text-Based Communications for the Delivery of Psychological Therapeutic Services to Rural and Remote Communities: Scoping Review. JMIR mental health, 8(2), e19478. https://doi.org/10.2196/19478
13. Felzmann, H., Villaronga, E. F., Lutz, C., & Tamò-Larrieux, A. (2019). Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns. Big Data & Society, 6(1), 2053951719860542. https://doi.org/10.1177/2053951719860542
14. Ji, Q., & Raney, A. A. (2020). Developing and validating the self-transcendent emotion dictionary for text analysis. PLoS ONE, 15(9), Article e0239050. https://doi.org/10.1371/journal.pone.0239050
15. Kaviani, H., Pournaseh, M., & Golfam, A. (2005). Emotion versus Cognition Words in Persian Dictionaries. Advances in Cognitive Sciences, 7 (2), 29-37. (In Persian) http://icssjournal.ir/article-1-134-fa.html
16. Kaviani, H., Mousavi, A., & Golfam, A. (2007). A Study on Psychology-Related Words in Persian Dictionaries. Language and Linguistics, 3(5), 89-102. (In Persian) https://lsi-linguistics.ihcs.ac.ir/article_1606.html
17. Kilic, I. Y., & Pan, S. (2022, June). Incorporating LIWC in neural networks to improve human trait and behavior analysis in low resource scenarios. In Proceedings of the Thirteenth Language Resources and Evaluation Conference (pp. 4532-4539). https://aclanthology.org/2022.lrec-1.482
18. Koutsoumpis, A., Oostrom, J. K., Holtrop, D., van Breda, W., Ghassemi, S., & de Vries, R. E. (2022). The kernel of truth in text-based personality assessment: A meta-analysis of the relations between the Big Five and the Linguistic Inquiry and Word Count (LIWC).Psychological Bulletin, 148(11-12), 843–868. https://doi.org/10.1037/bul0000381
19. Lake, B. M., & Murphy, G. L. (2023). Word meaning in minds and machines. Psychological Review, 130(2), 401–431. https://doi.org/10.1037/rev0000297
20. Lin, Y., Yu, R., & Dowell, N. (2020). LIWCs the Same, Not the Same: Gendered Linguistic Signals of Performance and Experience in Online STEM Courses. Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part I, 12163, 333–345. https://doi.org/10.1007/978-3-030-52237-7_27
21. Lopez, I., Quillivic, R., Evans, H., & Arriaga, R. I. (2019). Denouncing sexual violence: A cross-language and cross-cultural analysis of# MeToo and# BalanceTonPorc. In Human-Computer Interaction–INTERACT 2019: 17th IFIP TC 13 International Conference, Paphos, Cyprus, September 2–6, 2019, Proceedings, Part II 17 (pp. 733-743). Springer International Publishing. https://dx.doi.org/10.1007/978-3-030-29384-0_44
22. Lyu, S., Ren, X., Du, Y., & Zhao, N. (2023). Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons. Frontiers in psychiatry, 14, 1121583. https://doi.org/10.3389/fpsyt.2023.1121583
23. Soltani, M. A., MamSharifi, P., Salehi Chegeni, R., Safari, A., Ershadi Manesh, S., Keshavarz, H., ... & Tavakoli, F. (2024). The development and psychometric properties of the persian linguistic inquiry and word count (P-LIWC): Emotions and cognitive processes categories. Journal of Psychological Science, 23(137), 1-19. (In Persian). http://dx.doi.org/10.52547/JPS.23.137.997
24. MamSharifi, P., Sohrabi, F., Borjali, A., Zamanpour, E., HosseinSabet, F. (2026). The Psycho–Linguistic Analysis of Cognitive, Emotional, and Sexual Dimensions of Pornography in Social Networks: Instagram, Telegram, and X. Journal of Psychological Science. 25(160), 1-18. (In Persian). http://dx.doi.org/10.66224/jps.25.160.1
25. Meier, T., Boyd, R. L., Pennebaker, J. W., Mehl, M. R., Martin, M., Wolf, M., & Horn, A. B. (2019). “LIWC auf Deutsch”: The development, psychometrics, and introduction of DE-LIWC2015. PsyArXiv, (a). https://doi.org/10.17605/OSF.IO/TFQZC
26. Park, C., Shim, M., Eo, S., Lee, S., Seo, J., Moon, H., & Lim, H. (2022). Empirical analysis of parallel corpora and in-depth analysis using LIWC. Applied Sciences, 12(11), 5545. https://doi.org/10.3390/app12115545
27. Pennebaker, J. W., Francis, M. E., & Booth, R. J. (2001). Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates, 71(2001), 2001.
28. Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. http://hdl.handle.net/2152/31333
29. Piolat, A., Booth, R. J., Chung, C. K., Davids, M., & Pennebaker, J.W. (2011). La version française du dictionnaire pour le liwc: Modalités de construction et exemples d’utilisation [The French dictionary for LIWC: Modalities of construction and examples of use]. Psychologie Française, 56(3), 145–159. https://doi.org/10.1016/j.psfr.2011.07.002
30. Sasahara, K., Okuda, S., & Igarashi, T. (2021). Text mining approach to quantify consumer psychology and behavior in COVID-19 pandemic. In Proceedings of the Annual Conference of JSAI, JSAI2021. https://confit.atlas.jp/guide/event/jsai2021/subject/1D3-OS-3b-04/detail
31. Utomoa, P. A., & Karyawatia, A. E. (2021). Sentiment analysis of tribal, religion, and race with LIWC. Jurnal Elektronik Ilmu Komputer Udayana p-ISSN, 2301, 5373. https://jurnal.harianregional.com/jlk/full-64361
32. Windsor, L. C., Cupit, J. G., & Windsor, A. J. (2019). Automated content analysis across six languages. PloS one, 14(11), e0224425. https://doi.org/10.1371/journal.pone.0224425
33. Zhao, N., Jiao, D., Bai, S., & Zhu, T. (2016). Evaluating the Validity of Simplified Chinese Version of LIWC in Detecting Psychological Expressions in Short Texts on Social Network Services. PloS one, 11(6), e0157947. https://doi.org/10.1371/journal.pone.0157947
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This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)