Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories
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21 mai 2024
- ISIDORE Id: 10670/1.98954e...
- hal: hal-04558160
- doi: 10.31234/osf.io/m97gx
HALSHS : archive ouverte en Sciences de l’Homme et de la Société - notices sans texte intégral
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Mots-clés
ADHD Attention Deficit Hyperactivity Disorder psycholinguistics Natural language processing NLP stylometry Psycholinguistique Traitement automatique du langage TAL Stylométrie Trouble du déficit de l'attention avec ou sans hyperactivité TDAHSujets proches
Methods of analysis Analysis and chemistry Analytical methods Chemical analysis Analysis methods Analysis and examinationCiter ce document
Florian Cafiero et al., « Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories », HALSHS : archive ouverte en Sciences de l’Homme et de la Société - notices sans texte intégral, ID : 10.31234/osf.io/m97gx
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Résumé
This study explores the potential of stylometric analysis in identifying Self-Defining Memories (SDMs) authored by individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) versus a control group. A sample of 198 SDMs were written by 66 adolescents and were then analysed using Support Vector Classifiers (SVC). The analysis included a variety of linguistic features such as character 3-grams, function words, sentence length, or lexical richness among others. It also included metadata about the participants (gender, age) and their SDMs (self-reported sentiment after recalling their memories). The results reveal a promising ability of linguistic analysis to accurately classify SDMs, with perfect prediction (F1=1.0) in the contextually simpler setup of text-by-text prediction, and satisfactory levels of precision (F1 = 0.77) when predicting individual by individual. Such results highlight the significant role that linguistic characteristics play in reflecting the distinctive cognitive patterns associated with ADHD. While not a substitute for professional diagnosis, textual analysis offers a supportive avenue for early detection and a deeper understanding of ADHD.
