Tools and AI in stylistic analysis
A systematic literature review
Abstract
This research constitutes a systematic literature review that investigates the application of tools and Artificial Intelligence (AI) in stylistic analysis. The objective of the evaluation is to identify the various types of tools and AI utilized in stylistics and to assess their respective advantages and disadvantages in facilitating language analysis. Since the selection of data sources is critical, the relevance of each article was carefully assessed to ensure analytical accuracy and applicability of the findings. Utilizing the Systematic Literature Review (SLR) methodology, nine pertinent articles published between 2015 and 2025 were examined according to specific inclusion criteria. The study’s findings reveal that tools such as Voyant Tools, WordSmith, LIWC, and StyloMetrix are extensively employed to analyze linguistic and stylometric characteristics across different text types, including poetry, emails, and texts generated by AI. While AI has demonstrated its ability to improve efficiency, objectivity, and analytical depth, it continues encountering difficulties in capturing linguistic subtleties and cultural contexts. This study advocates for a collaborative approach between humans and AI to achieve more thorough and precise stylistic analyses in education, literature, and forensic linguistics.
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References
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Howedi, F., Mohd, M., Aborawi, Z. A., & Jowan, S. A. (2020). Authorship attribution of short historical arabic texts using stylometric features and a KNN classifier with limited training data. Journal of Computer Science, 16(10), 1334–1345. https://doi.org/10.3844/jcssp.2020.1334.1345
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Kairaitytė-Užupė, A., Ramanauskaitė, E., & Rudžionis, V. (2023). Scientific information analysis using text analysis tool “Voyant Tools.” Information & Media, 97, 25–48. https://doi.org/10.15388/im.2023.97.57
Kumar, S. M. (2025). AI and the evolution of creative writing: From generative text to literary innovation. International Journal of Scientific Research and Engineering Trends, 11(2), 2814–2817. https://doi.org/10.61137/ijsret.vol.11.issue2.468
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Liu, Y., Wang, G., & Wang, H. (2024). Reimagining literary analysis: Utilizing Artificial Intelligence to classify modernist French poetry. Information, 15(2), 70. https://doi.org/10.3390/info15020070
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Rapi, M. (2025). Interpreting literature through an AI lens (pp. 97–122). https://doi.org/10.4018/979-8-3373-1057-2.ch005
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Söğüt, S. (2024). Generative artificial intelligence in EFL writing: A pedagogical stance of pre-service teachers and teacher trainers. Focus on ELT Journal, 58–73. https://doi.org/10.14744/felt.6.1.5
Syafar, D. N., & Febrina, R. (2019). Computational linguistics models and language technologies for Indonesian. JURNAL ARBITRER, 6(1), 45–52. https://doi.org/10.25077/ar.6.1.45-52.2019
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Tripto, N. I., & Ali, M. E. (2023). The Word2vec graph model for author attribution and genre detection in literary analysis. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2310.16972
Wachyudi, K. (2022). Penggunaan Voyant Tools dalam pembelajaran bahasa Inggris. Educatio, 8(4), 1661–1668. https://doi.org/10.31949/educatio.v8i4.3427
Wijayono, A., & Putra, V. G. V. (2018). Implementation of image analysis techniques for various textile identification. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.1810.06423
Yadav, D. (2024). The role of artificial intelligence in literary analysis: A computational approach to understand literary styles. International Journal of Emerging Knowledge Studies, 03(09), 558–565. https://doi.org/10.70333/ijeks-03-09-006
Zawacki‐Richter, O., Kerres, M., Bedenlier, S., Bond, M., & Buntins, K. (2019). Systematic reviews in educational research. In Springer eBooks. Springer Nature. https://doi.org/10.1007/978-3-658-27602-7
AlAfnan, M. A., & MohdZuki, S. F. (2023). Do artificial intelligence chatbots have a writing style? An investigation into the stylistic features of ChatGPT-4. Journal of Artificial Intelligence and Technology. https://doi.org/10.37965/jait.2023.0267
Algaraady, J., & Mahyoob, M. (2025). Exploring ChatGPT’s potential for augmenting post-editing in machine translation across multiple domains: Challenges and opportunities. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1526293
Andini, R. S., Lorise, G., Kurniawan, S., Panggabean, E. Y., Khansa, S. N., & Rangkuti, R. (2023). Forensic stylistic analysis on University of Airlangga’s veterinary medicine student’s suicide notes. Titian: Jurnal Ilmu Humaniora, 07(02), 302–315. https://doi.org/https://doi.org/10.22437/titian.v7i2.29381
Battles, P. (2019). Using N-gram analysis to map intertextual networks in old English verse. Digital Philology: A Journal of Medieval Cultures, 8(2), 155–191. https://doi.org/10.1353/dph.2019.0022
Bories, A.-S., Plecháč, P., & Ruiz Fabo, P. (Eds.). (2022). Computational stylistics in poetry, prose, and drama. De Gruyter. https://doi.org/10.1515/9783110781502
Chan, C. K. Y., & Wong, H. Y. H. (2025). Assessing the ability of generative AI in English literary analysis through Bloom’s Taxonomy. Discover Computing, 28(1), 127. https://doi.org/10.1007/s10791-025-09572-8
de Castro, M. C. (2018). O uso da Lista de Consistência Detalhada (Detailed Consistency List) do WordSmith Tools© 6.0 para a investigação do perfil estilístico de quatro tradutores de Heart of Darkness para o Espanhol / The use of the detailed consistency list of Wordsmith Tools . Texto Livre Linguagem e Tecnologia, 11(1), 1–23. https://doi.org/10.17851/1983-3652.11.1.1-23
Eder, M., & Górski, R. L. (2022). Stylistic fingerprints, POS-tags, and Inflected Languages: A case study in Polish. Journal of Quantitative Linguistics, 30(1), 86–103. https://doi.org/10.1080/09296174.2022.2122751
Febrina, E., Sitepu, B., Manao, K. C., Orinda, N., Sitompul, B., Situmeang, A. L., Gaol, S. L., Riah, I., Surbakti, U., & Ranggkuti, R. (2023). A stylistic forensic analysis of Mahira’s suicade notes. Journal on Education, 06(01), 10580–10585. https://doi.org/https://doi.org/10.31004/joe.v6i1.4792
Gomathi, R. D., Murugan, J., Kavitha, P., & Gomathi, B. S. (2025). AI-driven literary analysis: Exploring the impact of artificial intelligence on text interpretation and criticism. Journal of Machine and Computing, 1124–1139. https://doi.org/10.53759/7669/jmc202505089
Hamidi, G. D., Bestari, F. A., Situmorang, A., & Rakhmawati, N. A. (2021). Sentiment analysis on the ratification of Penghapusan Kekerasan Seksual Bill on Twitter. Jurnal Teknik Informatika dan Sistem Informasi, 7(3), 655 –. https://doi.org/10.28932/jutisi.v7i3.4051
Herrmann, J. B., Jacobs, A. M., & Piper, A. (2021). Computational stylistics. In Handbook of Empirical Literary Studies (pp. 451–486). De Gruyter. https://doi.org/10.1515/9783110645958-018
Hosni, J. Al. (2024). Stylometric analysis of AI Chatbot-Generated emails: Are students losing their linguistic fingerprint? Journal of English Language Teaching and Applied Linguistics, 6(3), 33–42. https://doi.org/10.32996/jeltal.2024.6.3.5
Howedi, F., Mohd, M., Aborawi, Z. A., & Jowan, S. A. (2020). Authorship attribution of short historical arabic texts using stylometric features and a KNN classifier with limited training data. Journal of Computer Science, 16(10), 1334–1345. https://doi.org/10.3844/jcssp.2020.1334.1345
Huda, M., & Suwahyu, I. (2024). Peran Artificial Intelligence (AI) dalam pembelajaran pendidikan agama islam. REFERENSI ISLAMIKA Jurnal Studi Islam, 2(2), 53–61. https://doi.org/10.61220/ri.v2i2.005
Jose, J. & Simritha, R. (2024). Sentiment analysis and topic classification with LSTM Networks and TextRazor. International Journal of Data Informatics and Intelligent Computing, 3(2), 42–51. https://doi.org/10.59461/ijdiic.v3i2.115
Kairaitytė-Užupė, A., Ramanauskaitė, E., & Rudžionis, V. (2023). Scientific information analysis using text analysis tool “Voyant Tools.” Information & Media, 97, 25–48. https://doi.org/10.15388/im.2023.97.57
Kumar, S. M. (2025). AI and the evolution of creative writing: From generative text to literary innovation. International Journal of Scientific Research and Engineering Trends, 11(2), 2814–2817. https://doi.org/10.61137/ijsret.vol.11.issue2.468
Li, M., & Li, D. (2025). Human expertise vs AI efficiency. In Translation Studies in the Age of Artificial Intelligence (pp. 150–171). Routledge. https://doi.org/10.4324/9781003482369-8
Liu, Y., Wang, G., & Wang, H. (2024). Reimagining literary analysis: Utilizing Artificial Intelligence to classify modernist French poetry. Information, 15(2), 70. https://doi.org/10.3390/info15020070
Lahay, M., Ibrahim, R. A., Djuaeni, N., & Damhuri. (2024). Studi komparatif rima dan saja’ dalam Stilistika. AL-KILMAH, 3(1), 51–62. https://doi.org/10.58194/alkilmah.v3i1.1856
Nafisah, N., Sauri, S., & Nurbayan, Y. (2022). Al-akhṭā’u al-ṣautiyah fī qirā’ati al-nuṣūsi al-‘Arabiyyah bi istikhdāmi taṭbīqāti Praat. ALSUNIYAT: Jurnal Penelitian Bahasa, Sastra, Dan Budaya Arab, 5(1), 30–45. https://doi.org/10.17509/alsuniyat.v5i1.41710
Okulska, I., Stetsenko, D., Kołos, A., Karlińska, A., Głąbińska, K., & Nowakowski, A. (2023b). StyloMetrix: An open-source multilingual tool for representing stylometric vectors. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2309.12810
Omar, L. I., & Salih, A. A. (2024). Systematic review of English/Arabic machine translation postediting: Implications for AI application in translation research and pedagogy. Informatics, 11(2), 23. https://doi.org/10.3390/informatics11020023
Patawari, M. Y. (2019). Stylometry: Statistical approach into film style. Capture Jurnal Seni Media Rekam, 10(2), 71–90. https://doi.org/10.33153/capture.v10i2.2451
Prabowo, B., & Asmarani, R. (2025). Generative literature: The role of artificial intelligence in the creative writing process. Allure Journal, 5(1), 1–9. https://doi.org/10.26877/allure.v5i1.19959
Rapi, M. (2025). Interpreting literature through an AI lens (pp. 97–122). https://doi.org/10.4018/979-8-3373-1057-2.ch005
Rujeedawa, M. I. H., Pudaruth, S., & Malele, V. (2025). Unmasking AI-generated texts using linguistic and stylistic features. International Journal of Advanced Computer Science and Applications, 16(3). https://doi.org/10.14569/IJACSA.2025.0160321
Salsabila, T. H., Indrawati, T. M., & Fitrie, R. A. (2024). Meningkatkan efisiensi pengambilan keputusan publik melalui kecerdasan buatan. Deleted Journal, 1(2), 21. https://doi.org/10.47134/pjise.v1i2.2401
Sobchuk, O., & Šeļa, A. (2024). Computational thematics: Comparing algorithms for clustering the genres of literary fiction. Humanit Soc Sci Commun, 11, 438. https://doi.org/10.1057/s41599-024-02933-6
Söğüt, S. (2024). Generative artificial intelligence in EFL writing: A pedagogical stance of pre-service teachers and teacher trainers. Focus on ELT Journal, 58–73. https://doi.org/10.14744/felt.6.1.5
Syafar, D. N., & Febrina, R. (2019). Computational linguistics models and language technologies for Indonesian. JURNAL ARBITRER, 6(1), 45–52. https://doi.org/10.25077/ar.6.1.45-52.2019
Tomczyk, P., Brüggemann, P., Mergner, N., & Petrescu, M. (2024). Are AI tools better than traditional tools in literature searching? Evidence from E-commerce research. Journal of Librarianship and Information Science. https://doi.org/10.1177/09610006241295802
Tripto, N. I., & Ali, M. E. (2023). The Word2vec graph model for author attribution and genre detection in literary analysis. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2310.16972
Wachyudi, K. (2022). Penggunaan Voyant Tools dalam pembelajaran bahasa Inggris. Educatio, 8(4), 1661–1668. https://doi.org/10.31949/educatio.v8i4.3427
Wijayono, A., & Putra, V. G. V. (2018). Implementation of image analysis techniques for various textile identification. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.1810.06423
Yadav, D. (2024). The role of artificial intelligence in literary analysis: A computational approach to understand literary styles. International Journal of Emerging Knowledge Studies, 03(09), 558–565. https://doi.org/10.70333/ijeks-03-09-006
Zawacki‐Richter, O., Kerres, M., Bedenlier, S., Bond, M., & Buntins, K. (2019). Systematic reviews in educational research. In Springer eBooks. Springer Nature. https://doi.org/10.1007/978-3-658-27602-7
Published
2025-12-31
How to Cite
ALHUBILAH, Fahmi; SOPIAN, Asep.
Tools and AI in stylistic analysis.
Pioneer: Journal of Language and Literature, [S.l.], v. 17, n. 2, p. 189-205, dec. 2025.
ISSN 2655-8718.
Available at: <https://unars.ac.id/ojs/index.php/pioneer/article/view/6415>. Date accessed: 29 jan. 2026.
doi: https://doi.org/10.36841/pioneer.v17i2.6415.
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