Characterization of near death experiences using text mining analyses: A preliminary study
V. Charland-Verville, D. Ribeiro de Paula, C. Martial, H. Cassol, G. Antonopoulos, B. Chronik, A. Soddu, S. Laureys
PLoS ONE January 30, 2020 DOI: 10.1371/journal.pone.0227402 via Semantic Scholar
Summary
Near-Death Experiences (NDEs) are commonly portrayed as passing to an afterlife, but empirical research is recent and their definition remains debated. Questionnaires used to identify NDEs may be restrictive and subjective. To address this, researchers analyzed freely expressed narratives from 158 participants who reported a firsthand NDE, using automated text-mining. The analysis identified the most common words and, through hierarchical clustering, revealed three main clusters of features: visual perceptions, emotions, and spatial components. The authors suggest that this user-independent, data-driven approach can help build a more rigorous description and definition of NDEs.
Study at a glance
| Characteristics | Observational study Peer reviewed |
|---|---|
| Sample size | 158 |
| Population | People who self-reported a firsthand retrospective narrative of a Near-Death Experience |
| Keywords | Psychology Medicine |
| Citations | 20 |
| Key finding | Automated text-mining of NDE narratives revealed three main clusters of features: visual perceptions, emotions, and spatial components. |
Abstract
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.