Large language model for Bible sentiment analysis: Sermon on the Mount

TL;DR

This study applies large language models to analyze sentiments in different translations of the Bible's Sermon on the Mount, revealing variations in emotional tone and vocabulary across versions and passages.

Contribution

It introduces sentiment analysis to compare multiple Bible translations at chapter and verse levels, highlighting differences in emotional expression and vocabulary.

Findings

Different sentiments like humour, optimism, and empathy vary across translations.

Significant vocabulary differences exist among the Bible versions.

Sentiment analysis reveals emotional nuances in religious texts.

Abstract

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.