Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gita

TL;DR

This study applies advanced language models like BERT to perform topic modelling on Hindu texts, revealing high thematic similarity between the Upanishads and the Bhagavad Gita with improved coherence over traditional models.

Contribution

It introduces a novel application of deep learning language models for topic modelling in Hindu philosophical texts, highlighting their effectiveness and high thematic overlap.

Findings

High cosine similarity (73%) between texts' topics.

BERT-based models outperform conventional models in coherence.

Clear overlapping of themes visualized through embeddings.

Abstract

A distinct feature of Hindu religious and philosophical text is that they come from a library of texts rather than single source. The Upanishads is known as one of the oldest philosophical texts in the world that forms the foundation of Hindu philosophy. The Bhagavad Gita is core text of Hindu philosophy and is known as a text that summarises the key philosophies of the Upanishads with major focus on the philosophy of karma. These texts have been translated into many languages and there exists studies about themes and topics that are prominent; however, there is not much study of topic modelling using language models which are powered by deep learning. In this paper, we use advanced language produces such as BERT to provide topic modelling of the key texts of the Upanishads and the Bhagavad Gita. We analyse the distinct and overlapping topics amongst the texts and visualise the link of selected texts of the Upanishads with Bhagavad Gita. Our results show a very high similarity between the topics of these two texts with the mean cosine similarity of 73%. We find that out of the fourteen topics extracted from the Bhagavad Gita, nine of them have a cosine similarity of more than 70% with the topics of the Upanishads. We also found that topics generated by the BERT-based models show very high coherence as compared to that of conventional models. Our best performing model gives a coherence score of 73% on the Bhagavad Gita and 69% on The Upanishads. The visualization of the low dimensional embeddings of these texts shows very clear overlapping among their topics adding another level of validation to our results.