# Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language framework

arXiv: https://arxiv.org/abs/2201.03115 · HTML: https://mlsanity.com/paper/2201.03115/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 144 references that are in the map.
- First posted on arXiv 9 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [Stopwords in Technical Language Processing](https://mlsanity.com/paper/2006.02633/) (2020)
- [COVID-19 sentiment analysis via deep learning during the rise of novel cases](https://mlsanity.com/paper/2104.10662/) (2021)
- [SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic](https://mlsanity.com/paper/2006.10842/) (2020)

## Built on by (most cited first, 12 of 12)

- [Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gita](https://mlsanity.com/paper/2205.11020/) (2022)
- [An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis](https://mlsanity.com/paper/2303.07201/) (2023)
- [Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron](https://mlsanity.com/paper/2303.00135/) (2023)
- [Evaluation of Google Translate for Mandarin Chinese translation using sentiment and semantic analysis](https://mlsanity.com/paper/2409.04964/) (2024)
- [An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses](https://mlsanity.com/paper/2503.21393/) (2025)
- [A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models](https://mlsanity.com/paper/2408.16942/) (2024)
- [HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models](https://mlsanity.com/paper/2501.05482/) (2025)
- [Longitudinal Abuse and Sentiment Analysis of Hollywood Movie Dialogues using Language Models](https://mlsanity.com/paper/2501.13948/) (2025)
- [Machine Learning for Detection and Analysis of Novel LLM Jailbreaks](https://mlsanity.com/paper/2510.01644/) (2025)
- [Large language model for Bible sentiment analysis: Sermon on the Mount](https://mlsanity.com/paper/2401.00689/) (2024)
- [Detoxify: A framework for abusive text transformation using LLMs](https://mlsanity.com/paper/2507.10177/) (2025)
- [Abusive music and song transformation using GenAI and LLMs](https://mlsanity.com/paper/2601.15348/) (2026)

## Abstract (arXiv)

It is well known that translations of songs and poems not only break rhythm and rhyming patterns, but can also result in loss of semantic information. The Bhagavad Gita is an ancient Hindu philosophical text originally written in Sanskrit that features a conversation between Lord Krishna and Arjuna prior to the Mahabharata war. The Bhagavad Gita is also one of the key sacred texts in Hinduism and is known as the forefront of the Vedic corpus of Hinduism. In the last two centuries, there has been a lot of interest in Hindu philosophy from western scholars; hence, the Bhagavad Gita has been translated in a number of languages. However, there is not much work that validates the quality of the English translations. Recent progress of language models powered by deep learning has enabled not only translations but a better understanding of language and texts with semantic and sentiment analysis. Our work is motivated by the recent progress of language models powered by deep learning methods. In this paper, we present a framework that compares selected translations (from Sanskrit to English) of the Bhagavad Gita using semantic and sentiment analyses. We use hand-labelled sentiment dataset for tuning state-of-art deep learning-based language model known as bidirectional encoder representations from transformers (BERT). We provide sentiment and semantic analysis for selected chapters and verses across translations. Our results show that although the style and vocabulary in the respective translations vary widely, the sentiment analysis and semantic similarity shows that the message conveyed are mostly similar.