Large language model for Bible sentiment analysis: Sermon on the Mount
Mahek Vora Tom Blau Vansh Kachhwal Ashu M. G. Solo Rohitash Chandra
The revolution of natural language processing via large language models has
motivated its use in multidisciplinary areas that include social sciences and
humanities and more specifically, comparative religion. Sentiment analysis
provides a mechanism to study the emotions expressed in text. Recently,
sentiment analysis has been used to study and compare translations of the
Bhagavad Gita, which is a fundamental and sacred Hindu text. In this study, we
use sentiment analysis for studying selected chapters of the Bible. These
chapters are known as the Sermon on the Mount. We utilize a pre-trained
language model for sentiment analysis by reviewing five translations of the
Sermon on the Mount, which include the King James version, the New
International Version, the New Revised Standard Version, the Lamsa Version, and
the Basic English Version. We provide a chapter-by-chapter and verse-by-verse
comparison using sentiment and semantic analysis and review the major
sentiments expressed. Our results highlight the varying sentiments across the
chapters and verses. We found that the vocabulary of the respective
translations is significantly different. We detected different levels of
humour, optimism, and empathy in the respective chapters that were used by
Jesus to deliver his message.