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...
| Authors: | ; |
|---|---|
| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Journals Online & Print: | Drawer...
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| Fernleihe: | Fernleihe für die Fachinformationsdienste |
| Published: |
International Association of Management, Spirituality & Religion
2024
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| In: |
Journal of management, spirituality & religion
Year: 2024, Volume: 21, Issue: 1, Pages: 63-82 |
| IxTheo Classification: | AD Sociology of religion; religious policy AG Religious life; material religion ZG Media studies; Digital media; Communication studies |
| Further subjects: | B
Spirituality
B Stochastic Neighbor Embedding B Natural Language Processing B Word2Vec B Aufsatz in Zeitschrift B Religion |
| Online Access: |
Volltext (lizenzpflichtig) |
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| 520 | |a 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. | ||
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