Religion, Cognition, and Emotion: What Can Automated Text Analysis Tell Us About Culture?

As cultural conflicts are intensifying locally and internationally in the aftermath of COVID-19 pandemic, fine-tuned investigation of culture/religion, especially that of the marginalized populations, holds the potential to reduce disparity and suffering in the global village. This study used 3 text...

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Bibliographic Details
Published in:The Humanistic psychologist 2022-06, Vol.50 (2), p.213-233
Main Authors: Sundararajan, Louise, Ting, Rachel Sing-Kiat, Hsieh, Shu-Kai, Kim, Seong-Hyeon
Format: Article
Language:English
Subjects:
Belief & doubt
Chinese Cultural Groups
Cognition
Cognition & reasoning
Computer Programming
COVID-19
Emotions
Female
Human
Male
Minority Groups
Racial and Ethnic Differences
Religion
Religious Beliefs
Social exclusion
Test Construction
Text Analysis
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Summary:As cultural conflicts are intensifying locally and internationally in the aftermath of COVID-19 pandemic, fine-tuned investigation of culture/religion, especially that of the marginalized populations, holds the potential to reduce disparity and suffering in the global village. This study used 3 textual analysis programs-Topic Modeling, C-LIWC, and SSWC-Chinese-to shed light on the differences in cognition and emotion between two communities with radically different religious beliefs (Bimo and Christianity) among the Yi ethnic minority in Southwest China. Findings from these programs replicated the manual coding results of the previous study, and confirmed the prediction that cultural differences in cognition and emotion between the Yi-Bimo and the Yi-Christian fall along the divide between strong-ties and weak-ties rationality (Sundararajan, 2020a). Demonstrating an edge of advantage over manual coding, this machine-assisted analysis lends convergent validity to the previous study, and presents a more nuanced picture of diversity in emotion and cognition among the Chinese, with practical implications for future research and intervention for the marginalized populations.
ISSN:0887-3267
1547-3333
DOI:10.1037/hum0000201