Characterization of near death experiences using text mining analyses: A preliminary study

• Published in: PloS one Vol. 15; no. 1; p. e0227402
• Main Authors: Charland-Verville, Vanessa, Ribeiro de Paula, Demetrius, Martial, Charlotte, Cassol, Helena, Antonopoulos, Georgios, Chronik, Blaine Alexander, Soddu, Andrea, Laureys, Steven
• Format: Journal Article Web Resource
• Language: English
• Published: United States Public Library of Science 30.01.2020; Public Library of Science (PLoS)
• Subjects: Afterlife; Analysis; Astronomy; Automation; Biology and Life Sciences; Brain research; Characterization; Cluster analysis; Clustering; Coma; Computer and Information Sciences; Consciousness; Data Mining; Death; Empirical analysis; Humans; Life Change Events; Medical research; Medicine and Health Sciences; Mortality; Narratives; Near death experiences; Neurosciences & behavior; Neurosciences & comportement; Nondestructive testing; Phenomenology; Physics; Physiology; Questionnaires; Research and Analysis Methods; Sciences sociales & comportementales, psychologie; Self Report; Semantic analysis; Semantics; Social & behavioral sciences, psychology; Social Sciences; Utopianism; Visual perception; Belgium; Ontario Canada; Canada
• DOI: 10.1371/journal.pone.0227402
• ISSN: 1932-6203

The notion that death represents a passing to an afterlife, where we are reunited with loved ones and live eternally in a utopian paradise, is common in the reports of people who have encountered a "Near-Death Experience" (NDE). NDEs are thoroughly portrayed by the media but empirical studies are rather recent. The definition of the phenomenon as well as the identification of NDE experiencers is still a matter of debate. To date, NDEs' identification and description in studies have mostly derived from answered items in questionnaires. However, questionnaires' content could be restricting and subject to personal interpretation. We believe that in addition to their use, user-independent statistical text examination of freely expressed NDEs narratives is of prior importance to help capture the phenomenology of such a subjective and complex phenomenon. Towards that aim, we included 158 participants with a firsthand retrospective narrative of their self-reported NDE that we analyzed using an automated text-mining method. The output revealed the top words expressed by experiencers. In a second step, a hierarchical clustering analysis was conducted to visualize the relationships between these words. It revealed three main clusters of features: visual perceptions, emotions and spatial components. We believe the user-independent and data-driven text mining approach used in this study is promising by contributing to the building a rigorous description and definition of NDEs.