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

Area of Science:

• Psychology
• Neuroscience
• Cognitive Science

Background:

• Near-death experiences (NDEs) are frequently reported phenomena, often involving beliefs in an afterlife.
• Existing research on NDEs relies heavily on questionnaires, which may limit the scope and introduce subjective interpretation.
• A standardized definition and identification method for NDE experiencers remain subjects of debate.

Purpose of the Study:

• To explore the phenomenology of Near-Death Experiences (NDEs) through user-independent text analysis of subjective narratives.
• To identify common themes and features within freely expressed NDE accounts.
• To contribute to a more rigorous description and definition of NDEs using data-driven methods.

Main Methods:

• Analysis of 158 firsthand retrospective narratives from individuals reporting NDEs.
• Application of an automated text-mining method to identify prevalent words in NDE accounts.
• Hierarchical clustering analysis to visualize relationships between identified keywords.

Main Results:

• Text mining identified key terms frequently used by NDE experiencers.
• Hierarchical clustering revealed three primary clusters of NDE features: visual perceptions, emotions, and spatial components.
• These findings suggest a structured underlying phenomenology to NDEs.

Conclusions:

• A user-independent, data-driven text mining approach offers a promising method for studying subjective experiences like NDEs.
• The identified clusters provide empirical support for common elements within NDE narratives.
• This methodology can advance the development of a more precise definition and understanding of Near-Death Experiences.