{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T14:55:45Z","timestamp":1778338545591,"version":"3.51.4"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Psycholinguistics is an interdisciplinary area of research that bridges elements of linguistics with various branches of psychology. One of its goals is to identify and explain the links that exist between our psyche and the language we speak. In this research, we are expanding upon previous research that we did using several different Natural Language Processing (NLP) techniques to identify persons of interest from a scenario that was generated by a large language model (LLM). We used a different approach to this topic, which allowed us to develop a more nuanced method of reverse engineering and breaking down the psycholinguistic features of each suspect. Through the application of n-grams paired with deception, emotion, and subjectivity over time, we were able to identify and measure cues that can be used to better identify persons of interest from a larger pool of candidates. That dataset was smaller and somewhat limited in scope. We successfully identified the guilty parties from the fictional murder case using a combination of Latent Dirichlet Allocation, word vectors, and pairwise correlations. This research was larger in scope, number of potential suspects, and in the diversity of the corpus used. We were able to determine the guilty parties identified in ground truth using our methodology in this case specifically by focusing on entity to topic correlation, deception detection, and emotion analysis.<\/jats:p>","DOI":"10.3389\/frai.2025.1669542","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T05:38:34Z","timestamp":1761025114000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A psycholinguistic NLP framework for forensic text analysis of deception and emotion"],"prefix":"10.3389","volume":"8","author":[{"given":"Jonathan","family":"Adkins","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Al Bataineh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anthos","family":"Khanal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"ref1","first-page":"15","article-title":"Linguistic cues to deception: identifying political trolls on social media","author":"Addawood","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"426","DOI":"10.3390\/fi16110426","article-title":"Identifying persons of interest in digital forensics using NLP-based AI","volume":"16","author":"Adkins","year":"2024","journal-title":"Future Internet"},{"key":"ref3","first-page":"270","article-title":"Extracting data from unstructured crime text to represent in structured occurrence nets using natural language processing","author":"Alshammari","year":"2024"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/B0-08-044854-2\/04157-2","article-title":"History of psycholinguistics","volume-title":"The Encyclopedia of language and linguistics","author":"Altmann","year":"2006"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"221","DOI":"10.3390\/a16050221","article-title":"Detecting deception using natural language processing and machine learning in datasets on covid-19 and climate change","volume":"16","author":"Brzic","year":"2023","journal-title":"Algorithms"},{"key":"ref6","first-page":"4647","article-title":"Empath: understanding topic signals in large-scale text","author":"Fast","year":"2016"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/01638530701739181","article-title":"On lying and being lied to: a linguistic analysis of deception in computer-mediated communication","volume":"45","author":"Hancock","year":"2007","journal-title":"Discourse Process."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.fmre.2021.11.004","article-title":"Subjective or objective: how the style of text in computational advertising influences consumer behaviors?","volume":"2","author":"Huang","year":"2022","journal-title":"Fundam. 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