Spiritual versus religious: a Natural Language Processing perspective

The method of Natural Language Processing (NLP) is used to analyze the literature on spirituality and religion. Specifically, the corpus produced in the spirituality/religion related scholarly literatures are used to train unsupervised neural network models (Word2Vec) that learn the extent to which...

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Auteurs: Alpaslan, Can M. (Auteur) ; Mitroff, Ian I. 1938- (Auteur)
Type de support: Électronique Article
Langue:Anglais
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Publié: International Association of Management, Spirituality & Religion 2024
Dans: Journal of management, spirituality & religion
Année: 2024, Volume: 21, Numéro: 1, Pages: 63-82
Classifications IxTheo:AD Sociologie des religions
AG Vie religieuse
ZG Sociologie des médias; médias numériques; Sciences de l'information et de la communication
Sujets non-standardisés:B Spirituality
B Stochastic Neighbor Embedding
B Natural Language Processing
B Word2Vec
B Aufsatz in Zeitschrift
B Religion
Accès en ligne: Volltext (lizenzpflichtig)
Description
Résumé:The method of Natural Language Processing (NLP) is used to analyze the literature on spirituality and religion. Specifically, the corpus produced in the spirituality/religion related scholarly literatures are used to train unsupervised neural network models (Word2Vec) that learn the extent to which words associate syntactically and semantically with one another. These models provide insights into what scholars mean when they use such terms as spiritual, religious, and spiritual-but-not-religious. For instance, they reveal that in the scholarly literature the term spiritual is used more often in contexts that describe an individual's experiences, emotions, and feelings, whereas the term religious is used more often in contexts that highlight an individual's identity and affiliations. The results also suggest that NLP methods may help scholars to perform reasonably meaningful vector operations (e. g., spiritual minus religious) that can be used to explore quickly and efficiently the syntactic and semantic patterns in a large corpus.
ISSN:1942-258X
Contient:Enthalten in: Journal of management, spirituality & religion
Persistent identifiers:DOI: 10.51327/TUFP3116