Integrating Neurolinguistic Programming and Natural Language Processing to Analyze Selected Morphosyntactic Features in Individuals Diagnosed with Post-Traumatic Stress Disorder

Abstract

 Language is one of the most powerful tools for expressing emotions, thoughts and experiences.  The language under analysis can show how people handle trauma and the patient who are psychologically sick. Further, Morphosyntactic features encompass how meaning is constructed in language. The data observed in patients with PTSD showed some morphosyntactic features which may act as linguistic markers of trauma. Merging Natural Language Processing with Neurolinguistic Programming gives text analysis path with psychological and linguistic perspectives.  The consideration of Neurolinguistic Programming as a technique in combination with Natural Language Processing offers a dual approach to both diagnostics and therapy of PTSD.

The methodology involved in the research has been enhanced a noninvasive, automated, and large-scale diagnosis of PTSD using a combination of computational, psychological, and linguistic approaches. The objectives of this study were to apply Natural Language Processing techniques to analyze text data from individuals with PTSD, find linguistic markers associated with the disorder, identify the models linked to PTSD-related morphosyntactic features and investigate how psychology, linguistics, and computer science can be integrated to enhance the understanding of PTSD. To fulfill the objectives, the study has been used mix method approach.

The findings show the enhancement in understanding of language expressions of PTSD patients, and support to identify more clear language by using Natural language processing techniques. This study contributes to the developing field of digital mental health by bridging the linguistics, neurolinguistics programming and natural language processing. Moreover, the finding are expected to enhance the classification and identification of PTSD-related linguistic markers.