Affiliation:
1. California State University, Northridge, USA
2. University of Southern California, USA
Abstract
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.
Publisher
International Association of Management Spirituality & Religion
Subject
Organizational Behavior and Human Resource Management,Religious studies,Strategy and Management,Social Sciences (miscellaneous)
Cited by
2 articles.
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