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Characterization of near death experiences using text mining analyses: A preliminary study
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 ra...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6992169/ https://www.ncbi.nlm.nih.gov/pubmed/31999716 http://dx.doi.org/10.1371/journal.pone.0227402 |
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author | Charland-Verville, Vanessa Ribeiro de Paula, Demetrius Martial, Charlotte Cassol, Helena Antonopoulos, Georgios Chronik, Blaine Alexander Soddu, Andrea Laureys, Steven |
author_facet | Charland-Verville, Vanessa Ribeiro de Paula, Demetrius Martial, Charlotte Cassol, Helena Antonopoulos, Georgios Chronik, Blaine Alexander Soddu, Andrea Laureys, Steven |
author_sort | Charland-Verville, Vanessa |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6992169 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-69921692020-02-20 Characterization of near death experiences using text mining analyses: A preliminary study Charland-Verville, Vanessa Ribeiro de Paula, Demetrius Martial, Charlotte Cassol, Helena Antonopoulos, Georgios Chronik, Blaine Alexander Soddu, Andrea Laureys, Steven PLoS One Research Article 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. Public Library of Science 2020-01-30 /pmc/articles/PMC6992169/ /pubmed/31999716 http://dx.doi.org/10.1371/journal.pone.0227402 Text en © 2020 Charland-Verville et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Charland-Verville, Vanessa Ribeiro de Paula, Demetrius Martial, Charlotte Cassol, Helena Antonopoulos, Georgios Chronik, Blaine Alexander Soddu, Andrea Laureys, Steven Characterization of near death experiences using text mining analyses: A preliminary study |
title | Characterization of near death experiences using text mining analyses: A preliminary study |
title_full | Characterization of near death experiences using text mining analyses: A preliminary study |
title_fullStr | Characterization of near death experiences using text mining analyses: A preliminary study |
title_full_unstemmed | Characterization of near death experiences using text mining analyses: A preliminary study |
title_short | Characterization of near death experiences using text mining analyses: A preliminary study |
title_sort | characterization of near death experiences using text mining analyses: a preliminary study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6992169/ https://www.ncbi.nlm.nih.gov/pubmed/31999716 http://dx.doi.org/10.1371/journal.pone.0227402 |
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