Comprehensive Sentiment Analysis of Religious Content Naive Bayes Algorithm Model
DOI:
https://doi.org/10.30865/mib.v8i1.7062Keywords:
Sentiment Analysis, Naive Bayes Algorithm, Religious, Twitter, Interfaith Dialogue, Digital Humanities, Faith, Social ImplicationsAbstract
This paper delves into sentiment analysis of online religious content utilizing the Naive Bayes algorithm to decipher the array of sentiments present in religious discussions. By tailoring this algorithm to the complexities of religious language, the study reveals hidden sentiments, offering valuable insights for researchers, policymakers, and communities. The findings demonstrate that the sentiment analysis model performs robustly, with a precision of 84.78%, a recall of 82.98%, and a balanced F1 Score of 83.87%, indicating high accuracy in sentiment identification and effectiveness in capturing a significant portion of actual sentiments. The overall accuracy of the model stands at 75.10%, affirming its successful adaptation to the intricacies of religious discourse. These results not only deepen our understanding of sentiment analysis in the realm of faith and spirituality but also have practical implications for enhancing interfaith dialogue, fostering mutual understanding, and guiding decision-making in religious and social organizations. This research makes a significant contribution to the growing field of sentiment analysis, providing a methodological framework for exploring the nuanced sentiment landscape within the domain of faith and spirituality.
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