Narrative self refers to the collection of life stories that gives meaning to life events, helps regulating emotions and forms a coherent self-image. Traumatic events may deeply impact the narrative self and contribute to posttraumatic stress disorder (PTSD). Currently, the study of life narratives relies on the coding of semi-structured interviews by judges assessing the structural and thematic aspects of narratives, such as autobiographical reasoning (Adler et al., 2017; Mc Lean et al., 2020). However, the length and richness of traumatic narratives increase coding time and subjectivity. To overcome these issues, we developed a semi-automatic method for analyzing autobiographical reasoning. Our study, in the framework of Programme 13-Novembre, focuses on autobiographical reasoning processes in 153 narratives of individuals exposed and unexposed to the November 13, 2015, attacks, one year after the event, through three dimensions: interpretations, exploratory processes and meaning-making. Using TXM 0.8.3 textual data analysis software, we automatically projected colored linguistic markers (words, expressions) to ease identification of these three narrative dimensions. Coding by judges on Likert scales is then quicker and more reliable. The relevance and usefulness of this method will be discussed, particularly in the context of future analyses planned for the longitudinal aspect of the study.