Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
10.1109/ACCESS.2017.DOI

* Both authors contributed equally

Corresponding author: R. Chandra (e-mail: rohitash.chandra@unsw.edu.au).

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

ROHITASH CHANDRA *1  VENKATESH KULKARNI *2  School of Mathematics and
Statistics, University of New South Wales, Sydney, Australia  Department of
Mechanical Engineering, Indian Institute of Technology Guwahati, Assam, India

###### Abstract

It is well known that translations of songs and poems not only breaks rhythm
and rhyming patterns, but also results 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 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 by
western scholars and 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 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 compare selected translations (mostly 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 use novel sentence embedding models to provide
semantic analysis for selected chapters and verses across translations.
Finally, we use the aforementioned models for sentiment and semantic analyses
and provide visualisation of results. Our results show that although the style
and vocabulary in the respective Bhagavad Gita translations vary widely, the
sentiment analysis and semantic similarity shows that the message conveyed are
mostly similar across the translations.

###### Index Terms:

Language models; deep learning; sentiment analysis; semantic analysis; BERT;
comparative religion

=-15pt

##  I Introduction

Comparative religion is a field that encompasses systematic comparison of the
doctrines and practices, philosophy, theology, and spiritual viewpoints of the
world’s religions [1, 2, 3]. The area of philosophy of religion [4, 5]
encompasses the study of central themes and ideas in religions and associated
cultural traditions that relate to philosophical topics such as metaphysics,
epistemology, and ethics. On the other hand, religious philosophy [6] is an
area that encompasses philosophical topics but from the view of the religion
of interest which is particularly driven by believers, such as Hindu
philosophy, Christian theology, Jewish and Islamic philosophy. However, at
times, both groups of scholars can contribute to the subject. Hindu philosophy
[7, 8, 9] features central themes of ethics [10], nature of consciousness [8],
karma [11, 12], and the quest for understanding ultimate reality known as
Brahman [9]. Hindu philosophy is also referred as Indian philosophy [13, 14].
The Upanishads are ancient texts of Hindu philosophy which have been the
foundations of the six major schools, where vedanta [15] and yoga [16] have
been the most prominent. Hindu philosophy has an intersection between
philosophy of religion and religious philosophy given a diverse interest and
examination by western scholars apart from Hindu philosophers [17, 14]. The
Bhagavad Gita is considered as one of the main holy texts of Hinduism which is
part of the Mahabharata which is known as one of the oldest and largest epics
written in verse [18, 19, 20]. The Bhagavad Gita which translates as the song
of God is also known as a text that summarizes and known as the core of Hindu
philosophy [14]. The philosophy of karma, known as karma yoga is attributed to
the Bhagavad Gita [21, 22, 23]. The transmission of the Bhagavad Gita has been
through oral tradition for thousands of years, from the event of the
Mahabharata war in India, with recent estimates around 5100 years age[24, 25];
however, the exact dates have been debated in the literature with some
scholars dating it to around 2500 - 3500 years ago [26, 27]. We need to note
that scholars believe that the Mahabharata was written in ancient Sanskrit
where as the Bhagavad Gita has been written in modern Sanskrit; hence, some
scholars argue that Bhagavad Gita was later inserted into the epic Mahabharata
[28, 20]. The original text, which has been composed in Sanskrit has been ever
since translated into various languages. The Bhagavad Gita consists of 18
chapters which features a series of questions and answers between Lord Krishna
and Arjuna with a range of topics that includes the philosophy of Karma. The
Mahabharata war lasted for 18 days and hence the organisation of the Gita is
symbolic. The Bhagavad Gita is written in a poetic style so that it can be
sung and remembered for generations given the absence of a writing system.
Over the last few centuries, there has been a lot of effort in translation of
the Bhagavad Gita. There has been major focus on English translation and
commentary by philosophers and scholars of comparative religion, particularly
from India, Europe and United States.

Analysis of religious texts have been prominent, as highlighted, most
translations of the Bhagavad Gita and related texts come with interpretations
and commentary regarding philosophy and how the verses relate to issues at
present [29]. The Bhagavad Gita has been predominately used as a text and
inspired interpretations from areas of self-help and management, psychology
[30], economics [31], politics [32] and leadership[33]. However, these
commentaries dwell with general message of the texts rather than semantic or
syntactic analysis.

Religious linguistics refers to the study of language used in the domain of
religion and primarily aims at identifying those structures and functions of
religious language (lexicon, syntax, phonology, morphology, etc). In
principle, religious subject matters could encompass a variety of agents,
states of affairs or properties—such as God, deities, angels, miracles,
redemption, grace, holiness, sinfulness [34]. Recent research suggests that a
register of religious language such as prayer induces certain psychological
effects in the brain [35] which makes components of religious language a
cognitive register. Stein [36] extracted 14 multi-word expressions using local
grammar and semantic classes from Bhagavad Gita capturing some central themes
in the text. Bawa et al. [37] provided a review on machine translation for
English, Hindi and Sanskrit languages. Rajput et al. [38] performed a
statistical analysis of the Bhagavad Gita text for its translations in four
languages: English, French, Hindi and Sanskrit. These works, have focused in
linguistic and statistical measures which opens the road for machine learning
and natural language processing for analysis of religious and philosophical
texts.

Natural language processing (NLP) is an area of artificial intelligence that
focuses in processing and modelling language [39, 40, 41]. NLP is typically
implemented with language models where deep learning has been prominent for
tasks such as topic modelling, language translation, speech recognition,
semantic and sentiment analysis [40]. Sentiment analysis provides an
understanding of human emotions and affective states [42, 43, 44] which has
been prominent in understanding customer behaviour [45], health and medicine
[46], stock market predictions [47], and modelling election outcomes [48].
Deep learning methods such as long-short term memory (LSTM) network models
have been prominently used as language models due to their capability to model
temporal sequences with long-term dependencies [49]. Attention based mechanism
in LSTM models have shown significant improvements [50], and further progress
has been made using Transformer models which incorporate attention into
encoder-decoder LSTM framework [51, 52]. Bidirectional Encoder Representations
from Transformer (BERT) [53] model released by Google has become prominent
pre-trained language model with knowledge from a large data corpus. BERT
features more than 300 million model parameters for language modeling tasks
and has potential to provide good semantic and sentiment analysis. BERT
employs masked language modeling (MLM) for pre-training and has been one of
the most successful pre-trained models. In our earlier work, we used BERT-
based framework for sentiment analysis of COVID-19 related tweets during the
rise of novel cases in India [54]. We also used BERT for modelling US 2020
presidential elections with sentiment analysis from Tweets in order to predict
state-wise winners [55]. Thus, BERT can be used for better understanding the
difference using sentiment analysis amongst translations in philosophical
texts such as the Bhagavad Gita.

The Upanishads are a set of Hindu philosophical texts related to the Bhagavad
Gita in terms of topics and style. The Upanishads have been translated more
than 150 times from period of 1783 to 1994 where western scholars make up a
significant portion of translators and commentators [17]. This shows the
impact and interest of Hindu texts in the west; hence, analysing the style and
quality of the translations is essential. It is important to evaluate some of
the key translations with the others in terms of sentiments and semantics. The
Bhagavad Gita, has been written originally in verse as a song and it is well
known that translating such literary works not only breaks rhyming patterns,
but can also change meaning and message behind the verses [56].

This paper is motivated by the recent progress of language models powered by
deep learning methods. We compare selected translations of the Bhagavad Gita
by focusing on sentiment analysis and semantic similarity of the verses. We
compare selected translations with a focus on translations directly from
Sanskrit to English and one that uses an intermediate language (Gujarati). We
use novel sentence embedding based on BERT which provides semantic analyses by
encoding and representing the verses using sentence embedded vectors. In the
case of sentiment analysis, the pre-trained BERT model is further tuned using
hand labelled Twitter-based training data that captures 10 sentiments. We
visualise selected Bhagavad Gita chapter-wise and verse-wise sentiments across
the selected translations.

The rest of the paper is organised as follows. In Section 2, we provide
further details about background and historical development behind the
composition of the Bhagavad Gita. Section 3 presents the methodology with
details regarding data processing and model development. Section 4 presents
the results that feature data analysis and model prediction. Section 5
provides a discussion and Section 6 concludes the paper.

##  II Background

###  II-A Historical developments

The information in Hindu texts was passed through oral transmission for
thousands of years before being written in Sanskrit [57, 58, 59]. Knowledge
about natural and astronomical events have been preserved through oral
transitions [25] which are linked to the Indus valley civilisation (Harappan)
with mature period dated 8000 - 9000 years BCE (before the common era) by
recent studies [60]. There are studies that show that the earliest text of
Hinduism, known as the Rig Veda contains accounts of the Saraswati River [61]
being active. Latest research shows that the Sarawati River started building
sediments and drying 10,000 years ago [62]. The written history and dating of
the composition of the texts are continuously being updated, since the
transmission of knowledge was done orally for thousands of years before being
written. A major challenge has been in translating Vedic (ancient) Sanskrit
which is the language of the Rig Veda [63, 64]. The Bhagavad Gita is written
in modern Sanskrit which has been easier to translate [65] when compared to
the Rig Veda [66].

###  II-B Bhagavad Gita

The Bhagavad Gita features a discourse on duty and karma between Arjuna and
Lord Krishna at the battlefield of Kurukshetra. It describes an event at the
beginning of the Mahabharata war where Arjuna who is the lead archer of the
land has to fight his close family members for rightful heir to the throne of
Hastinapur [67] which is now modern day Delhi. Arjuna is the lead archer on
the side of the Pandavas with Lord Krishna as his charioteer, who is also a
close friend and relative. The Pandavas went on war with the Kauravas who were
their close relatives (cousins) for right to the throne of Hastinapur and
Indraprastha. Arjuna, after facing his relatives, friends and gurus on the
opposing side of the battlefield gets deeply disturbed and emotional and
begins pondering about philosophy of life and essence of dharma (ethics).
Arjuna makes a decision not to go to war and asks a series of questions with
his charioteer and mentor, Lord Krishna who is known as the avatar (born as
human) of Lord Vishnu [68] who in the western religious notion would be
equivalent to the term God. Due to the extremeness of the situation which
Arjuna faces about duty and ethics, the set of questions from Arjuna and
answers by Lord Krishna makes Bhagavad Gita the forefront of Hindu philosophy
and is known as the central sacred text of Hinduism [69, 70].

###  II-C The Sanskrit language

Sanskrit is a language known for the richness of its vocabulary and layered
meanings which induces subtle differences when translated to other languages
[71, 72]. Sanskrit is the language for the key Hindu texts such as the
Bhagavad Gita, Ramayana, Upanishads, Vedas, and Mahabharata [73, 74]. There
are a number of challenges in translating Sanskrit texts into other languages
as discussed next. Firstly, Sanskrit is a language for research and
scholarship and study of Hinduism; however, it has a limited number of
speakers. Secondly, although the key Hindu mantras for major events are
written in Sanskrit [75], majority of Hindus do not understand Sanskrit. On
the bright side, Hindi is based on Sanskrit and there is significant
vocabulary in Hindi from Sanskrit [76]. Finally, Sanskrit is a major branch of
Indo-European [77] languages which have links to European languages such as
Latin, German and English [78, 79]. Indian Invasion Theory (IIT) proposed that
development of Sanskrit and Hindu (Vedic) texts in ancient India is from
invasion/migration of Indo-Aryan tribes into India pre-Vedic age [80]. IIT has
been the forefront of colonialism and has been used as a way for British
rulers to motivate their rule and supremacy in British India [81]. IIT aligned
well with Eurocentric education system which did not give importance to
ancient Indian and Hindu scholars such as Aryabhata who was instrumental in
developing the Hindu decimal number system [82, 83, 84], which was branded as
Arabic numerals [85]. IIT has been contested heavily [86, 87] with evidence
from Harrapan excavations [60] due to inconsistent timelines about
migration/invasion and lack of evidence given by IIT. Apart from this, the Rig
Veda which is the earliest Hindu text features hymns of Saraswati River [88]
when it was flowing more than 10,000 years ago [62]. There is an opposing Out
of India Theory (OIT) [89] that suggests that the evolution of Sanskrit and
related languages moved from India to Europe and other parts of Asia through
migrations.

Pānini who is known as the ’father of linguistics” [90] was a revered
philologist and grammarian in ancient India [91]. Pānini is known for his text
Astādhyāyī[92] which provides a treatise on Sanskrit grammar with rules on
linguistics, syntax and semantics covered in verse form. The Sanskrit language
has over the decade gained a lot of interest in computational linguistics and
computer science. There has been work done in understanding Sanskrit form view
of language models and natural language processing [93, 94, 94]. Briggs [93]
suggested that Sanskrit is the best language for modelling using artificial
intelligence methods.

There are more than 300 English translations of the Bhagavad Gita in English.
An earlier review provided a chronology of translations and commentaries
mostly from western scholars from 1785 to 1980 [95]. The Bhagavad Gita has
been translated in more than 80 languages.

##  III Methodology

###  III-A Datasets: Translations of the Bhagavad Gita

We select prominent translations of the Bhagavad Gita that span different
translation times-frames (decade) and focused on scholars from a Hindu
background [20]. Our framework for analysis of translations can be extended to
other translations of the Bhagavad Gita, we merely select these texts as a
proof-of-concept. We tried to use the older and the most popular translations,
ensuring that the translations feature verse by verse translations. The
translations typically contains an interpretation which have been discarded in
our analysis.

Texts | Translator | Year
---|---|---
The Gita according to Gandhi[96] | Mahatma Gandhi & Mahadev Desai | 1946
The Bhagavad Gita[97] | Eknath Easwaran | 1985
The Bhagavad Gita[98] | Shri Purohit Swami | 1935
TABLE I: Details of the texts used for sentiment and semantic analysis.

The Bhagavad Gita consist of 18 chapters which features a series of questions
and answers between Lord Krishna and Arjuna with a range of topics that
includes the philosophy of Karma. The Mahabharata war lasted for 18 days[99]
and hence the organisation of the Gita is symbolic. In our work, we consider
the three prominent translations of Bhagavad Gita as the source of data for
analysis. The translators’ name, text name, and the year of publication are
shown in the Table I.

The English translation of the Bhagavad Gita published in 1946 by Mahadev
Desai [96] which was translated from Gujarati version based on the commentary
known as ”Anasakti Yoga” by Mahatma Gandhi [20]. It is based on talks given by
Gandhi between February and November 1926 at the Satyagraha Ashram in
Ahmedabad, India. This was the time when Gandhi had withdrawn from mass
political activity and devoted much of his time and energy to translating Gita
from Sanskrit to his native language Gujarati. As a result, he met with his
followers daily, after morning prayer sessions, to discuss the Gita’s contents
and meaning as it unfolded before him. ”Anasakti Yoga” is the transcription of
those daily sessions. Mahatma Gandhi viewed the Bhagavad Gita not as a
historical work, but one that describes the perpetual conflict that rages on
in the hearts of the mankind under the guise of physical warfare. Hence,
Gandhi’s non-violent interpretation is in stark contrast with contemporary
Indian freedom fighters, such as Lala Lajpat Rai, Aurobindo Ghose, Bal
Gangadhar Tilak. These freedom fighters argued that it was the sacred duty of
Indians to fight against the British colonial rule, just as Krishna had urged
Arjuna to fulfill his sacred duty [100]. The translation by Mahatma Gandhi is
accompanied by short commentaries in every chapter which are discarded in our
analysis.

The Bhagavad Gita translation by Eknath Easwaran is a direct Sanskrit- English
translation published in 1985. Easwaran’s comprehensive introduction (55-page)
places the Bhagavad Gita in its historical setting, and brings out the
universality and timelessness of its teachings. It features chapter
introductions to clarify key concepts and provides a context, and notes and a
glossary to explain Sanskrit terms. Eknath Easwaran’s commentary shows the
Gita’s relevance to the modern society and highlights key topics in ethics,
humanism and spirituality. Note that the introduction and chapter commentaries
have been discarded in our study.

The Bhagavad Gita translation by Shri Purohit Swami [98] avoids the use of
Sanskrit concepts that may be unfamiliar to English-speakers, for example
translating the word ’yoga’ as ’spirituality’. This is also a direct Sanskrit-
English translation published much earlier than the previous ones (1935).
Unlike Eknath Easwaran, Shri Purohit Swami does not present a commentary to
the Bhagavad Gita, but offers direct translation of the text.

We also considered other prominent translations and commentaries, such as
those by A. C. Bhaktivedanta Swami Prabhupada [101], Paramhansa Yogananda
[102], Sarvepalli Radhakrishnan, and Swami Chinmayananda Saraswati [103];
however, due to limited scope of our study, we could not use them. These texts
can be considered in future studies.

###  III-B Text data extraction and processing

In order to process the respective files given in printable document format
(PDF), we converted them into text files. This conversion from PDF to text
file gave us a raw dataset consisting of all the texts shown in Table III.
Next, pre-processing was done on the entire dataset, which included the
following steps.

  1. 1.

Removing unicode characters generated in the text files due to noise in the
PDF files;

  2. 2.

Removing verse numbering in the Bhagavad Gita;

  3. 3.

Replacing the archaic words such as ”thy” and ”thou” with modern words and
retaining semantic information;

  4. 4.

Replacing different words used to refer to the respective protagonists,
Krishna and Arjuna (Table II);

  5. 5.

Converting verses to a single line.

  6. 6.

Removing commentary on the verses by the author.

  7. 7.

Removing repetitive and redundant sentences such as ”End of the Commentary”.

Table II shows the conversion of archaic words and aliases used for Arjuna and
Krishna. In Mahatma Gandhi’s translation, there are 136 occurrences of
different words used to refer to Arjuna, some of them being - ”Partha”,
”Kaunteya”, ”Bharata”, ”Mahabahu”, etc. Words like “Savyasachin”, which
literally means ambidextrous, have been replaced according to the context in
which they appeared (in this case, Arjuna, because he could shoot arrows with
both hands). Similar conversions have been made for different names of
Krishna. In Shri Purohit Swami’s translation, Krishna is more commonly
referred to as the ”lord” (101 occurrences). These word transformations were
performed to enable the language model to decipher the meaning and context
from the verses easily. In the case of the translation by Eknath Easwaran, no
archaic words were used and also different names were not used for Krishna and
Arjuna; hence, Table II does not provide any information.

Original Word | Transformed word | Mahatma Gandhi | Shri Purohit Swami | Eknath Easwaran
---|---|---|---|---
”thou” | ”you” | 134 | 118 | -
thy | your | 84 | 84 | -
thee | you | 60 | 58 | -
partha | arjuna | 38 | 0 | -
art | are | 26 | 20 | -
shalt | shall | 21 | 23 | -
kaunteya | arjuna | 21 | 0 | -
bharata | arjuna | 19 | 0 | -
thyself | yourself | 11 | 11 | -
Hrishikesha | Krishna | 7 | 0 | -
TABLE II: Text processing using word conversion with same meaning.

In natural language processing applications, there has been studies regarding
removal of stop words which can help in removing low level information so that
the model can focus on high level representation [104, 105, 106]. Although it
is typical for language models to remove stop words (eg. ”a,” ”and,” ”but,”
”how,” ”or,” and ”what”), in the case of sentiment analysis, it can eliminate
information regarding the context of the sentiment [107]. Hence, we did not
remove stop words for sentiment analysis. Table III presents examples of
selected verses from the processed text by Mahatma Gandhi and Shri Purohit
Swami after transforming words for preserving semantic information using
information given in Table II. Figure 1 presents the number of occurrences for
the word conversions during text processing for the Bhagavad Gita translation
by Mahatma Gandhi and Shri Purohit Swami.

Author | Original sentence | Transformed sentence
---|---|---
Mahatma Gandhi |  | 1\. If, O Janardana, thou holdest that the attitude of
---
detachment is superior to action, then why,
O Keshava, dost thou urge me to dreadful action?
| If, O Krishna, you hold that the attitude of
---
detachment is superior to action, then why,
O Krishna, do you urge me to dreadful action?
Mahatma Gandhi |  | 27\. Whatever thou doest, whatever thou eatest, whatever
---
thou offerest as sacrifice or gift, whatever austerity
thou dost perform, O kaunteya, dedicate all to Me.
| Whatever you do, whatever you eat, whatever
---
you offer as sacrifice or gift, whatever austerity
you do perform, O Arjuna, dedicate all to Me.
Shri Purohit Swami |  | 7\. Yet since with mortal eyes thou canst not see Me, lo!
---
I give thee the Divine Sight. See now the glory of
My Sovereignty.”
| Yet since with mortal eyes you can not see
---
Me, lo! I give you the Divine Sight. See now
the glory of My Sovereignty.”
Shri Purohit Swami |  | 3\. Thou art the Primal God, the Ancient, the Supreme
---
Abode of this universe, the Knower, the
Knowledge and the Final Home. Thou fillest
everything. Thy form is infinite.
| You are the Primal God, the Ancient, the Supreme
---
Abode of this universe, the Knower, the Knowledge
and the Final Home. You fill everything. Your
form is infinite.
TABLE III: Processed text after transforming words for preserving semantic
information from Mahatma Gandhi and Shri Purohit Swami. Figure 1: Number of
archaic words transformed in translations by Mahatma Gandhi and Shri Purohit
Swami.

###  III-C Sentence Embedding Models

Sentence BERT (S-BERT)[108] is a sentence embedding model which improves BERT
model by reducing computational time to find similar pair of text using
Siamese and triplet network structures to derive semantically meaningful
sentence embeddings. Although BERT is one of the most successful pre-trained
models, it neglects dependency among predicted tokens. MPNet [109] (Masked and
Permuted Pre-training) is a novel pre-trained model that inherits the features
of BERT and and another improved model known as XLNet [110] (Generalized
Autoregressive Pre-training). MPNet-base encodes sentences into high-
dimensional embedding vectors that can be used for various natural language
processing tasks. The model takes a variable length English text as an input
and gives 768-dimensional output vector. The input sentences of length greater
than 384 tokens are truncated. MPNet uses masked and permuted language
modeling to model the dependency among predicted tokens and see the position
information of the full sentence. The model is also fine-tuned using a
contrastive learning objective on a dataset of 1 billion sentence
pairs111https://huggingface.co/sentence-transformers/all-mpnet-base-v2 for
generating the embeddings.Given a sentence from a pair of similar sentences,
the model needs to predict the sentence with which it was originally paired
from a set of randomly sampled sentences. The sentence embeddings are
typically compared using cosine similarity.

KeyBERT[111] is a keyword extraction technique that leverages BERT embeddings
to produce keywords that take into account the semantic aspects of the
document. In this method, the input document is embedded using a pre-trained
BERT model which embeds a chunk of text into a fixed-size vector. The keywords
and expressions (n-grams) are extracted from the same document using bag of
words techniques (for example, CountVectorizer). Each keyword is then embedded
into a fixed-size vector with the same model used to embed the document. As
the keywords and the document are represented in the same embedding space,
KeyBERT computes a cosine similarity between the keyword embeddings and the
document embedding. Then, the most similar keywords (with the highest cosine
similarity score) are extracted. Furthermore, KeyBERT includes maximum sum
similarity (MSS) and maximal marginal relevance (MMR) to introduce diversity
in the resulting keywords. MMR considers the similarity of keywords/key-
phrases with the document, along with the similarity of already selected
keywords and key-phrases. This results in a selection of keywords that
maximizes their within diversity with respect to the document. In our proposed
framework, we use KeyBERT along with MMR for keyword extraction for all the
chapters in different translations of the Bhagavad Gita.

###  III-D Framework

We present the framework that is used for sentiment and semantic analysis of
the Bhagavad Gita. Our framework analyzes sentiment and semantic aspects of
individual verses from the Bhagavad Gita and also derives insights about the
differences in the themes discussed in the texts. As shown in Figure 2, we
begin by converting the PDF files into text files and process them as
mentioned in Section III B. We provide sentiment analysis via $BERT_{base}$
model by predicting sentiments of verses across the selected translations
(texts) of the Bhagavad Gita. Although sentiment analysis can be done with
model prediction that gives a positive/negative polarity score, this would not
reveal much information about the nature of the sentiment as the
positive/negative score can be vague due to different expressions used in
translations. Hence, we use the hand-labelled SenWave dataset[112] which
features 11 different sentiments labelled by a group of 50 experts for 10,000
tweets worldwide during COVID-19 pandemic in 2020. The $BERT_{base}$ model is
trained using the SenWave dataset [112] for a multi-label sentiment
classification task.

Although sentiment analysis can provide an idea regarding the differences in
the translations, it does not provide information about the difference in the
semantics (meaning) across the translations. Hence, semantic analysis [113] is
vital and accompanies sentiment analysis. We use the MPNet-base [109] sentence
embedding model to encode verses as it produces high quality embeddings. We
then compare verse by verse cosine similarity scores across translations to
analyze how the semantics of the verses vary. Since the embedding is generally
in a higher dimension, we reduce the dimension of the encoded vectors to
explore the clustering of data in different translations and chapters. We use
Uniform Manifold Approximation and Projection (UMAP) [114] dimensionality
reduction algorithm to visualise the high-dimensional vectors. We also extract
keywords from the text using BERT-based language models to analyze central
themes. We note that related methods such as rapid automatic keyword
extraction (RAKE) [115], Yet Another Keyword Extractor! (YAKE!)[116], and TF-
IDF ( term frequency–inverse document frequency) [117] can be used to extract
keywords and key-phrases. However, they are based mostly on the statistical
properties of a text rather than semantic similarity. Hence, we use KeyBERT as
it takes into account the semantic aspects of the text. We filter out the top
10 keywords and sort them in order of their relevance to a given chapter.

Figure 2: Framework for semantic similarity and sentiment analysis for
selected translations of the Bhagavad Gita.

###  III-E Model Training

We note that although the SenWave dataset is publicly available, permission is
required from the authors for usage. In order to ensure reproducibility of the
results, and given the constraints, we do not provide the processed dataset.
However, we provide trained models in our GitHub repository
222https://github.com/sydney-machine-learning/sentimentanalysis_bhagavadgita.
The Senwave dataset features 10,000 Tweets that were collected from March to
May 2020 labelled with 10 different sentiments plus 1 label which refers to
COVID-19 official report. We limit to 10 sentiments and remove the ”official
report” in data processing. A tweet can feature sentiments such as
”optimistic”, ”pessimistic”, ”anxious”, and ”thankful”. The multi-label
sentiment classification task refers to the tweet that can be classified as
”optimistic” and ”anxious” at once. We train the $BERT_{base}$ model on
SenWave dataset by pre-processing the tweets using our previous work [54].

The pre-trained BERT-base uncased model is further tuned on the SenWave
dataset [112] with training and test dataset created in a 90/10 % split on a
dataset of 10,000 tweets using a batch size of 1 using 4 training epochs. We
use the default hyper-parameters for the model and implement regularisation
using a dropout[118] layer with dropout probability of 0.3 to avoid over-
fitting. Finally, we add a linear activation layer with 10 outputs to predict
10 sentiments. Moreover, we use the adaptive moment estimation (Adam) gradient
[119] optimizer for training with a learning rate of 1e-05, and binary cross
entropy loss function for the multi-label classification task.

We encode verses using the MPNet-base model and compute the verse by verse
semantic similarity. We also use the MPNet-base model for extraction of
keywords (using KeyBERT) from all chapters. However, given the constraint in
the MPNet-base model that number of tokens should not exceed 384, it would not
be possible to encode large chapters directly. Hence, we propose a method to
overcome this limitation by breaking each chapter into paragraphs of 15
verses. We include 3 verses from the previous paragraph into the current
paragraph to retain some context and maintain continuity. For example, in the
first paragraph, verses 1-15 are included, and in the second paragraph verses
13-27, then 25-39, and so on. We keep the top 20 keywords because keywords
that have a lower similarity score in the original paragraph may be more
relevant when the entire chapter is considered.

Next, we extract the keywords for all paragraphs $i$ with 20 candidate
keywords of paragraph $j$ such that $i!=j$. For each keyword, we add up its
cosine similarity score for different paragraphs. Finally, we obtain the top
10 keywords having the highest cumulative scores. The key idea here is that if
a term is a keyword in a certain paragraph, it also needs to be a keyword in
other paragraphs for it to qualify as a keyword for the entire chapter. We use
MMR with a diversity value of 0.5 to prevent the selection of similar meaning
keywords.

##  IV Results

###  IV-A Data Analysis

(a) Eknath Easwaran

(b) Shri Purohit Swami

(c) Mahatma Gandhi

Figure 3: Top 10 Bi-grams and Tri-grams from processed verses of all
translations

Figures 3 presents the bigrams and trigrams obtained for the respective
translations of the Bhagavad Gita. Note that the stop words were removed for
obtaining bigrams and trigrams. Throughout the Bhagavad Gita, the concept of
three ’gunas’ or attributes has been widely discussed. Dominance of the
’sattvic’ attribute leads to liberation, that of ’rajasa’ attribute leads to
rebirth as humans and ’tamas’ means moving to a lower form of life. Hence, the
bigram ’three gunas’ has been aptly extracted in Gandhi’s version (Figure
3(c)). Also, the three major human attributes (gunas) i.e. sattva, rajas,
tamas in Easwaran’s translation roughly translate to the attributes purity,
passion and ignorance. described in Shri Purohit Swami’s translation (Figure
3(b)).

We observe that the bigram of pleasure and pain appears in all translations. A
person who is fit for liberation is unaffected by worldly influences and
hence, is at complete peace with himself. He is unaffected by either pleasure
or pain. Since the bigrams and trigrams merely count the occurrence of a set
of words, the central themes that are emphasised will become evident
afterwords. While all three texts discuss the idea of ’supreme spirit’ or the
Atman; however, the path taken to realize it differs. Eknath Easwaran stresses
the importance of selfless service devoid of selfish attachments and desires
(Figure 3). Shri Purohit Swami advocates the path of right (righteous) action
by observing self control. Interestingly, Chapter 3 of Eknath Easwaran’s
translation is titled ’Selfless Service’ while Shri Swami Purohit has titled
it as ’Karma Yoga - The Path of Action’. Mahatma Gandhi observes that the mind
would have to be completely self restrained to be able to renounce the
attachment to fruits of action. The essence of Mahatma Gandhi’s interpretation
is beautifully captured in verse 47 of Chapter 2 - ”Action alone is thy
province, never the fruits thereof; let not thy motive be the fruit of action,
nor shouldst thou desire to avoid action.”

Furthermore, we notice that the leading bigram and trigram in Figure 3 across
the three different translations are entirely different. It seems that the
different translators have used different words to refer to similar concepts,
for examples Eknath Easwaran’s translation features every, creature and
supreme, goal as top bigrams whereas Shri Purohit Swami’s translation gives
supreme, spirit and right, action. Moreover, Mahatama Gandhi’s translation
features fruit, action and pleasure, pain. Hence, a word by word comparison
using bigrams and trigrams show a major difference of the respective
translations.

Table IV lists all the chapter titles of the three translations. In Mahatma
Gandhi’s translation, chapters are referred to as Discourses. For example,
chapter 1 is named Discourse 1. We note the difference in the way the
different translators interpreted the title of the respective chapters. Eknath
Easwaran, for example, translated Chapter 2 heading as ”Self-Realization”
while Shri Purohit Swami translated it as ” The Philosophy of Discrimination”.

Chapter No. | Eknath Easwaran | Shri Swami Purohit | Mahatma Gandhi
---|---|---|---
1 | The War Within | The Despondency of Arjuna | -
2 | Self-Realization | The Philosophy of Discrimination | -
3 | Selfless Service | Karma-Yoga - The Path of Action | -
4 | Wisdom in Action | Dnyana-Yoga - The Path of Wisdom | -
5 | Renounce and Rejoice | The Renunciation of Action | -
6 | The Practice of Meditation | Self-Control | -
7 | Wisdom from Realization | Knowledge and Experience | -
8 | The Eternal Godhead | Life Everlasting | -
9 | The Royal Path | The Science of Sciences and the Mystery of Mysteries | -
10 | Divine Splendor | The Divine Manifestations | -
11 | The Cosmic Vision | The Cosmic Vision | -
12 | The Way of Love | Bhakti-Yoga - The Path of Love | -
13 | The Field and the Knower | Spirit and Matter | -
14 | The Forces of Evolution | The Three Qualities | -
15 | The Supreme Self | The Lord-God | -
16 | Two Paths | Divine and Demonic Civilization | -
17 | The Power of Faith | The Threefold Faith | -
18 | Freedom and Renunciation | The Spirit of Renunciation | -
TABLE IV: Names of Chapters in Bhagavad Gita in different translations.

###  IV-B Sentiment Analysis

Next, we use the BERT model for verse-by-verse sentiment analysis of the
respective Bhagavad Gita translations. We first present the bigrams and the
trigrams of the top ten optimistic and pessimistic sentiments for the entire
text for the respective translations.

The sentiment analysis shows that [pleasure, pain] is an optimistic bigram in
all three translations (Figure 5, Figure 6, Figure 7). However, this has been
used in the context that one should have complete control over one’s mind,
which is one of the central themes in the Bhagavad Gita. According to the
Bhagavad Gita, the mind should not be clouded by either pleasure or pain, by
heat or cold, or by other worldly influences (Chapter 2, verse 15). Only such
a person is fit for immortality (moksha) to escape the cycle of birth and
death (Chapter 2, verse 15). Lord Krishna uses the contradicting concepts of
pleasure and pain to demonstrate theme about the human condition [120] in
order to convince Arjuna to pick his bow up and rise above worldly emotions.

Moreover, in terms of leading optimistic sentiments predicted by the model,
according to the bigrams - we find [supreme, goal] and [every, creature] and
the leading optimistic sentiment bigrams for the Eknath Easwaran’s translation
(Figure 5) whereas Shri Purohit Swami’s translation gives supreme, spirit and
self, control (Figure 7). Mahatma Gandhi’s translation gives [pleasure, pain]
and [without, attachment] (Figure 6). We notice that [pleasure, pain] is
listed in the top five optimistic bigrams across the respective translation
which shows that although the wording used in the translations are different,
the sentiments they expressed are similar. Interestingly, we notice that
[every, creature] (Figure 5) and [fruit, action] (Figure 7) is listed in both
pessimistic and optimistic sentiments in the respective translations. A major
reason could be due to the context in which these terms appear in the
respective verses in the text.

We next present the chapter-wise sentiments for the respective translations as
shown in Figures 8 and 9. Figure 4 presents the sentiments for the entire text
where we notice that sentiments such as ”denial”, ”anxious”, ”thankful” and
”empathetic” are the least expressed sentiments while ”annoyed”, ”optimistic”
and ”surprised” are the most expressed across the three texts. In terms of
chapter-wise analysis, we find that there is a major difference in the
predicted sentiments ”optimistic”, ”pessimistic” and ”surprise” in most cases
shown in Figures 8 and 9. While Eknath Easwaran’s translation leads in all
positive sentiments - ”optimistic”, ”thankful” and ”empathetic”, Mahatma
Gandhi’s translation has the highest number of ”pessimistic”, ”annoyed”,
”anxious” and ”surprise” sentiments. It is worthwhile to look into the text
and review the exact verses and the predicted sentiments. We select Chapter 6
(Figure 8 \- Panel f) and show the verses and their predicted sentiments which
have a stark difference among different translations in Table V. The words
used, grammar and language style, and the meaning associated with the verse
greatly differs across the different translators as shown in Table V along
with the underlying sentiment. If we consider verse 2, we find that the given
sentiments are entirely different for the different translations. This is due
to the way the verse is presented to our BERT-based language model. In the
case of translation by Mahatama Gandhi, the verse could be perceived as
joking. Eknath Easwaran presents that same verse in a tone that is showing
annoyed and surprised sentiments while Shri Purohit Swami presents it in a
tone that is annoyed. We need to highlight that similar to the sentiment
analysis based on tweets, the proposed model does not take the context of the
the overall chapter and hence this could be a limitation of the model for
providing accurate predictions. We provide rest of the sentiment predictions
in our GitHub repository. 333https://git.io/J9GcU

Figure 10 shows the heatmap with number of occurrence of a given sentiment in
relation to the rest of the sentiments of all the verses in the respective
translations. We notice that ”annoyed”, ”optimistic” and ”surprised” are the
key sentiments across all the respective translations. In relation to rest of
the sentiments, we find that the combination of ”optimistic and annoyed”,
”optimistic and surprise”, and ”optimistic and joking” are the leading
combinations.

We need to measure the similarity and diversity of the sentiments expressed
with verse-by-verse comparison across the different translations. Table VI
shows the Jaccard similarity score computed on the predicted sentiments for
three pairs of texts for selected chapters. The score is highest for Mahatma
Gandhi and Shri Purohit Swami’s versions which indicates they had the highest
overlap of the predicted sentiments.

Figure 4: Text-wise sentiment analysis. Figure 5: Top 10 optimistic and
pessimistic Bi-grams and Tri-grams from processed verses for Eknath Easwaran’s
translation Figure 6: Top 10 optimistic and pessimistic Bi-grams and Tri-grams
from processed verses for Mahatma Gandhi’s translation Figure 7: Top 10
optimistic and pessimistic Bi-grams and Tri-grams from processed verses for
Shri Swami Purohit’s translation

(a) Chapter 1: The War Within

(b) Chapter 2: Self-Realization

(c) Chapter 3: Selfless Service

(d) Chapter 4: Wisdom in Action

(e) Chapter 5: Renounce and Rejoice

(f) Chapter 6: The Practice of Meditation

(g) Chapter 7: Wisdom and Realization

(h) Chapter 8: The Eternal Godhead

(i) Chapter 9: The Royal Path

Figure 8: Chapter-wise sentiment analysis (Chapters 1 - 9)

(a) Chapter 10: Divine Splendor

(b) Chapter 11: The Cosmic Vision

(c) Chapter 12: The Way of Love (o)

(d) Chapter 13: The Field and the Knower

(e) Chapter 14: The Forces of Evolution

(f) Chapter 15: The Supreme Self

(g) Chapter 16: Two Paths

(h) Chapter 17: The Power of Faith

(i) Chapter 18: Freedom and Renunciation

Figure 9: Chapter-wise sentiment analysis Chapters 10 - 18)

Eknath Easwaran Mahatma Gandhi Shri Purohit Swami Verse 2 Therefore, Arjuna,
you should understand that renunciation and the performance of selfless
service are the same. Those who cannot renounce attachment to the results of
their work are far from the path. What is called sannyasa, know you to be
yoga, O Arjuna; for none can become a yogin who has not renounced selfish
purpose. O Arjuna! Renunciation is in fact what is called Right Action. No one
can become spiritual who has not renounced all desire. Predicted Sentiment
Annoyed, Surprise Annoyed, Joking Annoyed Verse 5 Reshape yourself through the
power of your will; never let yourself be degraded by self-will. The will is
the only friend of the Self, and the will is the only enemy of the Self. By
one’s Self should one raise oneself, and not allow oneself to fall; for soul
(Self) alone is the friend of self, and Self alone is self’s foe. Let him seek
liberation by the help of his Highest Self, and let him never disgrace his own
Self. For that Self is his only friend; yet it may also be his enemy.
Predicted Sentiment Optimistic, Annoyed Pessimistic, Surprise Optimistic,
Annoyed Verse 20 In the still mind, in the depths of meditation, the Self
reveals itself. Beholding the Self by means of the Self, an aspirant knows the
joy and peace of complete fulfillment. Where thought curbed by the practice of
yoga completely ceases, where a man sits content within himself, soul having
seen soul; There, where the whole nature is seen in the light of the Self,
where the man abides within his Self and is satisfied there, its functions
restrained by its union with the Divine, the mind finds rest. Predicted
Sentiments Optimistic Pessimistic, Sad, Surprise Optimistic, Surprise Verse 32
ARJUNA: O Krishna, the stillness of divine union which you describe is beyond
my comprehension. How can the mind, which is so restless, attain lasting
peace? I do not see, O Krishna, how this yoga, based on the equal-mindedness
that You have expounded to me, can steadily endure, because of fickleness (of
the mind). Arjuna said: I do not see how I can attain this state of equanimity
which You has revealed, owing to the restlessness of my mind. Predicted
Sentiments Surprise Pessimistic, Annoyed Surprise

TABLE V: Some of the least similar verses in Chapter 6 according to predicted
sentiments

(a) Eknath Easwaran

(b) Mahatma Gandhi

(c) Shri Purohit Swami

Figure 10: Heatmap showing number of occurrence of a given sentiment in relation to the rest of the sentiments of all the verses in the respective translations. Figure 11: Arjuna’s sentiments over time (chapters) in the translation by Eknath Easwaran. Figure 12: Lord Krishna’s sentiments over time (chapters) in the translation by Eknath Easwaran. | Easwaran-Gandhi | Purohit-Easwaran | Gandhi-Purohit
---|---|---|---
Chapter 3 | 0.604 | 0.506 | 0.684
Chapter 5 | 0.568 | 0.520 | 0.614
Chapter 7 | 0.559 | 0.486 | 0.574
Chapter 8 | 0.547 | 0.503 | 0.568
Chapter 9 | 0.501 | 0.486 | 0.565
Chapter 10 | 0.523 | 0.507 | 0.562
Chapter 11 | 0.507 | 0.488 | 0.527
Chapter 12 | 0.500 | 0.483 | 0.518
Chapter 15 | 0.494 | 0.490 | 0.516
Chapter 16 | 0.500 | 0.495 | 0.527
Chapter 17 | 0.510 | 0.501 | 0.536
Average | 0.526 | 0.497 | 0.563
TABLE VI: Sentiment analysis of selected pairs of translations with Jaccard
Similarity Score for predicted sentiments for select chapters

###  IV-C Semantic Analysis

Next, we provide analysis of the translation using semantic analysis of verse-
by-verse comparison of the text. The verses are encoded using MPNet-base model
and the cosine similarity is used as the similarity metric. The metric is
averaged for every chapter and the standard deviation is mentioned in
brackets. Table VII shows the verse by verse cosine similarity score for the
translations grouped by the chapter. We provide the mean and standard
deviation of the score for the respective verses in each chapter. We observe
that chapters 5 and 17 are semantically least similar and chapter 10 is most
similar according to the BERT-based language models. Further, in the pair-wise
comparison, Mahatma Gandhi and Shri Purohit Swami’s traslation are most simlar
to each other. It is important to note that these two translations also have
the highest Jaccard similarity score for predicted sentiments. We present some
of the semantically least similar verses in Table VIII. In Chapter 5, verse 5,
Eknath Easwaran interprets the goals of knowledge and service as being the
same, Mahatma Gandhi sees both sankhya and yoga (Sanskrit terms) as one and
Shri Purohit Swami interprets wisdom and right action as one. In this case,
Mahatma Gandhi’s verse is least similar to the other two (Score1 and Score2
both lower than Score3) primarily because it uses the Sanskrit terms of
sankhya and yoga. Whenever, a particular translation uses a Sanskrit term, it
obtains a lower cosine similarity score with respect to the other two as BERT-
based models have been primarily trained on a huge corpus of English texts. In
Chapter 16, verse 4, we observe that there is a difference in the devilish
qualities being talked about. Some of the qualities unique to each text are:
cruelty (Eknath Easwaran), pretentiousness, coarseness (Mahatma Gandhi) and
pride, insolence (Shri Purohit Swami). The difference in choice of words may
be due to the different eras in which these translations were created leading
to the use of prevalent words at that point of time. Next we present
semantically most similar verses in Table IX. In Chapter 3, verse 4, we
observe that though the choice of words is different, the meaning conveyed is
similar and the BERT-based language model assigns a high similarity score to
all three pairs. In Chapter 9, verse 18, the choice of words used by Mahatma
Gandhi and Shri Purohit Swami is almost identical and a high similarity score
(Score2) is obtained.

Furthermore, we review the semantic score by presenting the actual verses from
the translated versions of a selected chapter. We choose Chapter 12 since this
chapter has one of the least verses, it is easier for us to include it in the
paper. The scores for the rest of the chapters are published in our GitHub
repository 444https://git.io/JSyPB. In Table X, we present all the verses
taken from Chapter 12 with the cosine similarity (score). We also present the
mean and the standard deviation of the respective scores to provide an
indication about the overall semantic similarity of the verses for the chapter
for the combination of selected translation.

We visualise the encoded verses in two dimensions using UMAP in Figure 13. We
present the visualizations for one in every three chapters. We do not intend
to find separate clusters for each translation as it is evident from Table VII
that the verses from each translation are semantically very similar to each
other. However, we do find some interesting clusters which represent a
sequence of events or consecutive verses in the text. For example, in Figure
13(a), all the distinct clusters represent separate events. In Chapter 1,
these include verses describing Sanjaya’s description of battlefield to
Dhritarashtra, Arjuna moving his chariot to the center of the battlefield,
Arjuna expressing his grief to Krishna. In Chapter 4 (Figure 13(b)), we
observe 3 clusters. The left-most cluster includes verses describing different
forms of sacrifice, the bottom-most cluster comprises verses where Lord
Krishna emphasises renunciation of oneself from fruits of action, and the
third cluster includes the conversation between Krishna and Arjuna from the
first ten verses of the chapter. In Chapter 10 (Figure 13(d)), the lower
cluster comprises the verses in which Lord Krishna describes his divine
attributes to Arjuna, and the upper cluster features the rest of the
conversation between Krishna and Arjuna in the chapter (primarily first 20
verses).

Furthermore, the keywords in the text/chapter play a vital role in semantic
similarity. It is important to note that most keywords aptly signify the
themes of the chapters. This helps us to quickly assess how different authors
differ in their interpretation of key concepts. Table XI shows the keywords
obtained for all the chapters for the respective translation. In order to get
a better perspective, we need to review the keywords from a selected chapter.
Table XII shows the keywords from Chapter 12 and their associated scores
obtained using KeyBert.

Table XII shows the keywords extracted from Chapter 12. This chapter starts
with Arjuna asking Lord Krishna who are the better practitioners of yoga:
those who love him dearly or those who seek the eternal reality (or Atman).
”Yoga” is a keyword for Easwaran and Gandhi’s translation. Lord Krishna
responds that worshipping the eternal reality (Brahman) [121, 9] is extremely
difficult for the humans. So, he suggests Arjuna to meditate and be devoted to
him while engaging in selfless action (karma yoga). We note again that Lord
Krishna is the avatar of Lord Vishnu - supreme consciousness). A steady and
stable state of mind can only be achieved by humans by the practice of
meditation. It is pertinent to note the KeyBERT has extracted ”meditate” as
one of the top 10 keywords in Shri Purohit Swami’s translation. Lord Krishna
further says that the devotees who focus their mind with unflinching devotion
are very dear to him (Chapter 12 verse 20). ”Devotion” and ”devotee” marks
another central theme that has been correctly extracted and is the top keyword
in all three translations. Furthermore, words such as ”eternal”, ”unchanging”,
”omnipresent” used to refer to the Atman (self/qualia) are present. Arjuna and
Krishna, though not themes, appear extensively in the chapter and so, they
have been also extracted (Table XII). We report the cumulative score where we
have added the cosine similarity scores of the keywords in batches of 15
verses each.

In order to provide further visualisation of the keywords and how they are
related to each other, we use dimension reduction and plot the first two
components of the latent vectors. We use Uniform manifold approximation and
projection (UMAP) [114] for dimension reduction. We obtained the UMAP
embeddings by encoding top 10 keywords from all chapters using the MPNet
Sentence Embedding model. The keywords which appeared in more than one chapter
were removed. In these visualisations, we are primarily interested in
analyzing clusters of data as UMAP algorithm is good at preserving the local
structure by grouping neighboring data points together. Figure 14 presents the
visualisation (scatter plot) of UMAP embedding. The cluster of keywords that
provide important semantic information, with respect to their use in the
Bhagavad Gita, are shaded in yellow color. In Eknath Easwaran’s translation,
(Figure 14(a)) the words associated to the impending battle such ”archers”,
”chariots”, ”war”, ”armies”, ”battle” form a cluster. Also, ”roared”, ”death”
and ”killed” are close to this cluster in the embedding space. The keywords in
the philosophy of giving away (the central theme in Chapter 5 - Renounce and
Rejoice) also form a cluster. These include ”sacrifice” (and ”sacrifices”),
”renounce”, ”renouncing”, ”renunciation”, ”abstaining”, ”surrendering”, all of
which have a similar underlying meaning. One of the central themes in
Easwaran’s translation - selfless service is captured by words such as ”self-
realization”, ”selflessly” (”selfless”), ”realize” which cluster together.
However, ”selfish”, ”selfishly” also are a part of this cluster though they
convey the opposite meaning. The terms ”eternal”, ”immutable”, ”changeless”
and ”immortality” which are closely associated with qualia (Atman) appear as a
cluster. The keywords representing the protagonists and associates in the
Bhagavad Gita (”Krishna”, ”Arjuna”, ”duryodhana”, ”dhritarashtra”) and their
qualities such as ”sattva”, ”dharma”, ”tyaga”, ”rajas” and ”tamas” form a
cluster in the upper portion of the embedding space.

In Mahatma Gandhi’s translation (Figure 14(b)), ”rajas, tamas” (both
undesirable qualities) are very close to each other. Furthermore, most of the
prominent personalities representing the righteous side of the war -
”Krishna”, ”Arjuna”, ”Bhima” (Arjuna’s brother), ”Pandu” (Arjuna’s father),
”Subhadra” (Arjuna’s wife) and some of their qualities and attributes such as
”tyagi”, ”yogi”, ”sattva”, ”rajas”, ”tamas” form a cluster. Moreover,
”ashvattha” [122] which is a sacred fig tree for Hindus is very close to the
keyword ”tree”. The words related to qualia - ”omnipresent”, ”transcending”,
”unchanging”, ”imperishable”, ”everlasting” ”immovable” form a cluster near
the bottom of the plot. Entities involved in or pertaining to the impending
war of Kurukshetra - ”bowmen”, ”vestments”, ”trumpets”, ”multitude” (referring
to huge number of armed men on both sides), ”mortals”, ”sons” are also found
to cluster together. Finally, ”lord”, ”devotion”, ”devotee”, ”worship”,
”reverence” also form a cluster. In Shri Purohit Swami’s translation, Figure
14(c), the attributes of soul (as described in the Bhagavad Gita) form a
cluster. These attributes that include - ”divinity”, ”mysticism”, ”spirit”,
”power”, ”consciousness”, ”philosophy”, ”wisdom”, ”knowledge” form a cluster
with ”souls” Another key theme in Shri Purohit Swami’s translation is that of
sacrifice . In our visualization, ”sacrifice”, ”relinquishment”,
”renunciation”, ”vow” cluster together. The idea of the eternity of the soul
is captured by the words - ”omniscient”, ”omnipresent”, ”immovable”,
”changeless”, ”eternal”. While the eternity of soul is a widely discussed
theme in the Bhagavad Gita, so is the idea of rebirth. ”Progenitors”,
”reborn”, ”reincarnation”, ”births”, ”creation”, ”nature”, ”mankind” are
keywords closely related to this idea that cluster together. We have not shown
all keywords due to limited space. It is interesting to note that in the
respective UMAP embeddings, the points representing Arjuna and Lord Krishna
are always closest to each other. Please note that the bigger sizes of the
words ”krishna” , ”yoga” and ”sacrifice” in Figure 14(a), Figure 14(b), Figure
14(c) respectively do not imply any semantic importance in the figures.

| Easwaran vs Gandhi | Purohit vs Easwaran | Gandhi vs Purohit
---|---|---|---
Chapter 3 | 0.627 (0.133) | 0.608 (0.146) | 0.694 (0.114)
Chapter 5 | 0.629 (0.129) | 0.637 (0.105) | 0.628 (0.122)
Chapter 7 | 0.698 (0.144) | 0.668 (0.147) | 0.691 (0.148)
Chapter 8 | 0.662 (0.123) | 0.650 (0.081) | 0.673 (0.124)
Chapter 9 | 0.681 (0.126) | 0.686 (0.109) | 0.731 (0.094)
Chapter 10 | 0.759 (0.096) | 0.747 (0.113) | 0.733 (0.123)
Chapter 11 | 0.705 (0.109) | 0.731 (0.101) | 0.721 (0.110)
Chapter 12 | 0.609 (0.120) | 0.633 (0.097) | 0.724 (0.096)
Chapter 15 | 0.686 (0.116) | 0.675 (0.110) | 0.657 (0.067)
Chapter 16 | 0.662 (0.096) | 0.667 (0.100) | 0.691 (0.116)
Chapter 17 | 0.654 (0.111) | 0.591 (0.132) | 0.642 (0.132)
Mean | 0.670 (0.119) | 0.663 (0.113) | 0.689 (0.113)
TABLE VII: Semantic Analysis Cosine Similarity for select chapters from
selected pairs of the translations. Note that the mean is given with standard
deviation (in brackets) for all the verses in the respective chapters.

Chapter | Verse | Eknath Easwaran | Mahatma Gandhi | Shri Purohit Swami | Score1 | Score2 | Score3
---|---|---|---|---|---|---|---
5 | 5 |  | The goal of knowledge and
---
the goal of service are the same;
those who fail to see this
are blind.
| The goal that the sankhyas
---
attain is also reached by the yogins.
He sees truly who sees both sankhya
and yoga as one.
| The level which is reached by
---
wisdom is attained through right
action as well. He who perceives
that the two are one,
knows the truth.
0.295 | 0.338 | 0.517
9 | 22 |  | Those who worship me and
---
meditate on me constantly, without
any other thought I will
provide for all their needs.
| As for those who worship
---
Me, thinking on Me alone
and nothing else, ever attached
to Me, I bear the burden
of getting them what they need.
| But if a man will meditate on Me
---
and Me alone, and will worship
Me always and everywhere,
I will take upon Myself
the fulfillment of his aspiration,
and I will safeguard whatsoever
he shall attain.
0.571 | 0.527 | 0.528
15 | 11 |  | Those who strive resolutely on
---
the path of yoga see the
Self within. The thoughtless,
who strive imperfectly, do not.
| Yogins who strive see Him seated
---
in themselves; the witless ones who have
not cleansed themselves to see Him
not, even though they strive.
| The saints with great effort find
---
Him within themselves; but not the
unintelligent, who in spite of every
effort cannot control their minds.
0.450 | 0.629 | 0.367
16 | 4 |  | Other qualities, Arjuna, make a person
---
more and more inhuman: hypocrisy, arrogance,
conceit, anger, cruelty, ignorance.
| Pretentiousness, arrogance, self-conceit, wrath,
---
coarseness, ignorance—these are to be found
in one born with the devilish
heritage.
| Hypocrisy, pride, insolence, cruelty,
---
ignorance belong to him who is
born of the godless qualities.
0.447 | 0.586 | 0.520
17 | 18 |  | Disciplines practiced in order to
---
gain respect, honor, or admiration
are rajasic; they are undependable and
transitory in their effects.
| Austerity which is practiced
---
with an eye to gain praise, honour
and homage and for ostentation is said
to be rajasa; it is fleeting
and unstable.
| Austerity coupled with hypocrisy or
---
performed for the sake of
self-glorification, popularity or vanity, comes
from Passion, and its result is
always doubtful and temporary.
0.440 | 0.740 | 0.212

TABLE VIII: Semantically least similar verses. The cosine similarity (score)
is given with the following combinations. Score 1 -Eknath Easwaran vs Mahatma
Gandhi, Score 2 - Mahatma Gandhi vs Shri Purohit Swami, and Score 3 - Shri
Purohit Swami vs Eknath Easwaran

Chapter | Verse | Eknath Easwaran | Mahatma Gandhi | Shri Purohit Swami | Score1 | Score2 | Score3
---|---|---|---|---|---|---|---
3 | 4 |  | One who shirks action does not
---
attain freedom; no one can gain
perfection by abstaining from work.
| Never does man enjoy freedom
---
from action by not undertaking
action, nor does he attain that
freedom by mere renunciation of action.
| No man can attain freedom
---
from activity by refraining from
action; nor can he reach perfection
by merely refusing to act.
0.753 | 0.879 | 0.837
7 | 9 |  | I am the sweet fragrance in
---
the earth and the radiance of
fire; I am the life in
every creature and the striving
of the spiritual aspirant.
| I am the sweet fragrance
---
in earth; the brilliance in
fire; the life in all beings;
and the austerity in ascetics.
| I am the Fragrance of earth,
---
the Brilliance of fire. I am
the Life Force in all beings,
and I am the Austerity of the ascetics.
0.909 | 0.917 | 0.856
9 | 18 |  | I am the goal of life,
---
the Lord and support of all,
the inner witness, the abode of
all. I am the only refuge,
the one true friend; I am the
beginning, the staying, and the
end of creation; I am the
womb and the eternal seed.
| I am the Goal, the Sustainer,
---
the Lord, the Witness, the Abode,
the Refuge, the Friend; the Origin,
the End the Preservation, the Treasure
house, the Imperishable Seed.
| I am the Goal, the Sustainer,
---
the Lord, the Witness, the Home,
the Shelter, the Lover and
the Origin; I am Life and Death;
I am the Fountain and the Seed Imperishable.
0.839 | 0.907 | 0.871
12 | 18 |  | That devotee who looks upon
---
friend and foe with equal regard,
who is not buoyed up by praise
nor cast down by blame, alike
in heat and cold, pleasure and
pain, free from selfish attachments,
| Who is same to foe and friend,
---
who regards alike respect and disrespect,
cold and heat, pleasure and pain,
who is free from attachment;
| He to whom friend and foe
---
are alike, who welcomes equally honour
and dishonour, heat and cold, pleasure
and pain, who is enamoured of nothing,
0.827 | 0.885 | 0.820

TABLE IX: Semantically most similar verses. The cosine similarity (score) is
given with the following combinations. Score 1 -Eknath Easwaran vs Mahatma
Gandhi, Score 2 - Mahatma Gandhi vs Shri Purohit Swami, and Score 3 - Shri
Purohit Swami vs Eknath Easwaran

(a) Chapter 1

(b) Chapter 4

(c) Chapter 7

(d) Chapter 10

(e) Chapter 13

(f) Chapter 17

Figure 13: Chapter-wise UMAP embedding visualization

Verse | Eknath Easwaran | Mahatma Gandhi | Shri Purohit Swami | Score1 | Score2 | Score3
---|---|---|---|---|---|---
1 |  | ARJUNA: Of those steadfast
---
devotees who love you and
those who seek you as the
eternal formless Reality, who
are the more established in yoga?
| Of the devotees who thus worship
---
You, incessantly attached, and
those who worship the
Imperishable Unmanifest, which are
the better yogins? The Lord Said:
| “Arjuna asked: My Lord! Which are the better
---
devotees who worship You, those who try
to know You as a Personal God, or those
who worship You as Impersonal and Indestructible?
0.414 | 0.589 | 0.573
2 |  | KRISHNA: Those who set their hearts
---
on me and worship me with unfailing
devotion and faith are more
established in yoga.
| Those I regard as the best yogins
---
who, riveting their minds on Me, ever
attached, worship Me, with the
highest faith.
| Lord Shri Krishna replied: Those
---
who keep their minds fixed on
Me, who worship Me always with
unwavering faith and concentration; these
are the very best.
0.490 | 0.575 | 0.663
3 |  | As for those who seek the
---
transcendental Reality, without name,
without form, contemplating the Unmanifested,
beyond the reach of thought and
of feeling,
| But those who worship the
---
Imperishable, the indefinable, the
Unmanifest, the Omnipresent, the
Unthinkable, the Rock-seated, the Immovable,
the Unchanging,
| Those who worship Me as the
---
Indestructible, the Undefinable,
the Omnipresent, the Unthinkable,
the Primeval, the Immutable and
the Eternal;
0.579 | 0.826 | 0.488
4 |  | with their senses subdued and
---
mind serene and striving for
the good of all beings, they
too will verily come unto me.
| Keeping the whole host of senses
---
in complete control, looking on all
with an impartial eye, engrossed in
the welfare of all beings—these come
indeed to Me.
| Subduing their senses, viewing all
---
conditions of life with the same eye,
and working for the welfare of all
beings, assuredly they come to Me.
0.533 | 0.619 | 0.742
5 |  | Yet hazardous and slow is the
---
path to the Unrevealed, difficult
for physical creatures to tread.
| Greater is the travail of those
---
whose mind is fixed on the
Unmanifest; for it is hard for
embodied mortals to gain the Unmanifest—Goal.
| But they who thus fix their
---
attention on the Absolute and Impersonal
encounter greater hardships, for it is
difficult for those who possess a body
to realise Me as without one.
0.430 | 0.622 | 0.426
6 |  | But they for whom I am the
---
supreme goal, who do all work
renouncing self for me and meditate on
me with single-hearted devotion,
| But those who casting all their
---
actions on Me, making Me their all
in all, worship Me with the meditation
of undivided devotion,
| Verily, those who surrender their actions
---
to Me, who muse on Me, worship Me
and meditate on Me alone, with no
thought save of Me,
0.776 | 0.799 | 0.725
7 |  | these I will swiftly rescue from
---
the fragments cycle of birth and death,
for their consciousness has entered into me.
| Of such, whose thoughts are centered
---
on Me, O Arjuna, I become before
long the Deliverer from the ocean
of this world of death.
| O Arjuna! I rescue them from the ocean
---
of life and death, for their minds
are fixed on Me.
0.546 | 0.752 | 0.560
8 |  | Still your mind in me, still your
---
intellect in me, and without doubt
you will be united with me forever.
| On Me set your mind, on Me rest
---
your conviction; thus without doubt shall
you remain only in Me hereafter.
| Then let your mind cling only
---
to Me, let your intellect abide in
Me; and without doubt you shall live
hereafter in Me alone.
0.632 | 0.788 | 0.669
9 |  | If you cannot still your mind
---
in me, learn to do so through the
regular practice of meditation.
| If you can not set your mind steadily
---
on Me, then by the method of constant practice
seek to win Me, O Arjuna.
| But if you can not fix your mind
---
firmly on Me, then, My beloved friend,
try to do so by constant practice.
0.583 | 0.622 | 0.655
10 |  | If you lack the will for such
---
self-discipline, engage yourself in my work,
for selfless service can lead you at
last to complete fulfillment.
| If you are also unequal to this method
---
of constant practice, concentrate on service
for Me; even thus serving Me you
shall attain perfection.
| And if you are not strong enough
---
to practise concentration, then devote yourself
to My service, do all yours acts for
My sake, and you shall still
attain the goal.
0.637 | 0.746 | 0.664
11 |  | If you are unable to do even this,
---
surrender yourself to me, disciplining yourself
and renouncing the results of all your actions.
| If you are unable even to do this,
---
then dedicating all to Me, with mind
controlled, abandon the fruit of action.
| And if you are too weak even for
---
this, then seek refuge in union with Me,
and with perfect self-control renounce the fruit
of your action.
0.749 | 0.646 | 0.682
12 |  | Better indeed is knowledge than mechanical
---
practice. Better than knowledge is meditation.
But better still is surrender of attachment to results,
because there follows immediate peace.
| Better is knowledge than practice,
---
better than knowledge is concentration,
better than concentration is renunciation
of the fruit of all action,
from which directly issues peace.
| Knowledge is superior to blind action,
---
meditation to mere knowledge,
renunciation of the fruit of action
to meditation, and where there is renunciation
peace will follow.
0.673 | 0.854 | 0.681
13 |  | That one I love who is incapable of ill
---
will, who is friendly and compassionate.
Living beyond the reach of I and mine
and of pleasure and pain,
| Who has ill-will towards none, who is
---
friendly and compassionate, who has shed all
thought of ‘mine’ or ‘I’, who regards
pain and pleasure alike, who is long-suffering;
| He who is incapable of hatred towards any being,
---
who is kind and compassionate, free from
selfishness, without pride, equable
in pleasure and in pain, and forgiving,
0.558 | 0.811 | 0.507
14 |  | patient, contented, self-controlled, firm
---
in faith, with all their heart and
all their mind given to me with
such as these I am in love.
| Who is ever content, gifted with yoga,
---
self-restrained, of firm conviction, who has
dedicated his mind and reason to Me—that
devotee (bhakta) of Mine is dear to Me.
| Always contented, self-centred, self-controlled,
---
resolute, with mind and reason dedicated to Me,
such a devotee of Mine is My beloved.
0.495 | 0.646 | 0.768
15 |  | Not agitating the world or by it agitated,
---
they stand above the sway of elation,
competition, and fear: that one is my beloved.
| Who gives no trouble to the world,
---
to whom the world causes no trouble,
who is free from exultation, resentment, fear
and vexation,—that man is dear to Me.
| He who does not harm the world,
---
and whom the world cannot harm,
who is not carried away by any impulse of joy,
anger or fear, such a one is My beloved.
0.567 | 0.758 | 0.621
16 |  | They are detached, pure, efficient,
---
impartial, never anxious, selfless in all their
undertakings; they are my devotees, very dear
to me.
| Who expects nothing, who is pure, resourceful,
---
unconcerned, untroubled, who indulges in no undertakings,
—that devotee of Mine is dear to Me.
| He who expects nothing, who is pure,
---
watchful, indifferent, unruffled, and who renounces
all initiative, such a one is My beloved.
0.684 | 0.847 | 0.569
17 |  | That one is dear to me who runs not
---
after the pleasant or away from the painful,
grieves not, lusts not, but lets things come
and go as they happen.
| Who rejoices not, neither frets nor grieves,
---
who covets not, who abandons both good
and ill—that devotee of Mine is dear
to Me.
| He who is beyond joy and hate,
---
who neither laments nor desires,
to whom good and evil fortunes are the same,
such a one is My beloved.
0.628 | 0.678 | 0.597
18 |  | That devotee who looks upon friend and foe
---
with equal regard, who is not buoyed up
by praise nor cast down by blame, alike in
heat and cold, pleasure and pain, free
from selfish attachments,
| Who is same to foe and friend,
---
who regards alike respect and disrespect,
cold and heat, pleasure and pain,
who is free from attachment;
| He to whom friend and foe are alike,
---
who welcomes equally honour and dishonour,
heat and cold, pleasure and pain,
who is enamoured of nothing,
0.827 | 0.885 | 0.885
19 |  | the same in honor and dishonor,
---
quiet, ever full, in harmony everywhere,
firm in faith such a one is dear to me.
| Who weighs in equal scale blame and praise,
---
who is silent, content with whatever his lot,
who owns no home, who is of steady mind,—
that devotee of Mine is dear to Me.
| Who is indifferent to praise and censure,
---
who enjoys silence, who is contented
with every fate, who has no fixed abode,
who is steadfast in mind, and filled
with devotion, such a one is My beloved.
0.541 | 0.732 | 0.583
20 |  | Those who meditate upon this immortal dharma
---
as I have declared it, full of faith and seeking
me as lifes supreme goal, are truly my devotees,
and my love for them is very great.
| They who follow this essence of dharma,
---
as I have told it, with faith, keeping Me as
their goal,—those devotees are exceeding
dear to Me.
| Verily those who love the spiritual wisdom
---
as I have taught, whose faith never fails,
and who concentrate their whole nature on Me,
they indeed are My most beloved.”
0.840 | 0.684 | 0.664
|  |  |  Mean (Standard Deviation) | 0.609 (0.120) | 0.724 (0.096) | 0.633 (0.097)

TABLE X: Semantic similarity of verses from Chapter 12. The cosine similarity
(score) is given with the following combinations. Score 1 - Eknath Easwaran vs
Mahatma Gandhi, Score 2 - Mahatma Gandhi vs Shri Purohit Swami, and Score 3 -
Shri Purohit Swami vs Eknath Easwaran.

Chapter | Eknath Easwaran | Mahatma Gandhi | Shree Purohit Swami
---|---|---|---
1 |  | arjuna, krishna, battle, dharma, brahmins
---
dhritarashtra, war, devadutta, bhishma, bhima
| arjuna, maharatha, battle, devadatta, armies,
---
sanjaya, bhima, kripa, yudhishthira, war
| arjuna, dharmaraja, maharaja, battle, dhritarashtra,
---
devadatta, war, duryodhana, sanjaya, kripa
2 |  | arjuna, sorrow, souls, immortality, krishna,
---
death, arise, reverence, despair, disciple
| arjuna, anguish, krishna, sorrowing, reverence,
---
arise, heartedness, compassion, endure, slain
| arjuna, equanimity, anguish, krishna, reverence,
---
compassion, endure, terror, disciple, battle
3 |  | arjuna, devotion, wisdom, selfless, selflessly,
---
krishna, knowledge, inaction, desires, brahman
| arjuna, krishna, sacrifice, inaction, detachment,
---
renunciation, desires, pervading, yoga, prakriti
| arjuna, wisdom, sacrifice, meditates, inaction,
---
krishna, meditate, worship, refraining, honour
4 |  | teachings, arjuna, dharma, krishna, yoga,
---
eternal, devotee, sages, changeless, surrendering
| arjuna, yoga, wrath, sages, devotee,
---
essence, changeless, succession, learnt, varnas
| arjuna, reincarnate, wisdom, krishna, worship,
---
materialism, devotee, changeless, ancestors, births
5 |  | selfless, wisdom, renunciation, arjuna, krishna,
---
judgment, desires, sannyasa, duality, selfishly
| renunciation, yoga, soul, sankhyas, virtue, actions,
---
peace, karmayoga, attain, ascetic
| meditating, wisdom, renunciation, krishna, arjuna,
---
praise, divine, sin, purification, perceives
6 |  | meditation, yoga, selfrealization, arjuna, selfless,
---
attains, energy, abiding, refrain, renounce
| yoga, yogi, arjuna, oneself, contentment, nirvana,
---
purification, spirit, motionless, sannyasin
| spirituality, meditation, krishna, arjuna, celibacy,
---
purification, renounces, virtuous, vow, sage
7 |  | arjuna, devotion, krishna, spiritual, prakriti,
---
eternal, universe, yoga,nature,realize
| arjuna,vedas,transcending,yoga, jiva,
---
essence,evil,universe,manliness,mystery
| arjuna,krishna,godless,spiritual, righteousness,
---
eternal,scriptures,beings,natures,unto
8 |  | brahman,arjuna,devotion, krishna, eternal,
---
souls, adhiyajna, death, existence, realize
| brahman, arjuna, vedas, adhyatma, devotion,
---
death,embodied,abode,yogi,departs
| arjuna,devotion,consciousness, eternity,souls,
---
divinity,krishna,meditate,scriptures,omniscient
9 |  | devotion, krishna, arjuna, eternal, souls,
---
divine, behold, vijnana, wisdom, existence
| arjuna,dharma, transcendent, prakriti, doctrine,
---
souls, homage, multitude, condemn, lord
| devotion, arjuna, krishna, godless,mysticism,souls,
---
multitude,immovable,beings,universe
10 |  | krishna, arjuna,brahman,devotion,sages,
---
creation,divine,manifestations,wisdom,equanimity
| arjuna,brahman,devotion,gods, beings,
---
discernment,yoga,everlasting,contentment, inward
| krishna,arjuna,devotion, godless,wisdom,
---
eternal,mankind,holiest,progenitors,contentment
11 |  | arjuna,krishna,creation,behold,infinite,
---
celestial,perceive,yoga,exalted,sages
| arjuna,krishna,gods,behold, universe,
---
multitudes,vasus,yoga,vestments,imperishable
| arjuna,krishna,omnipresent, universe,behold,
---
celestial,embraced,radiance,powers,petal
13 |  | devotion,brahman,krishna,buddhi,spiritual,
---
immortality, wisdom, arjuna, sages, arise
| brahman, consciousness, devotion, arjuna, uprightness,
---
body, aphoristic, aversion, immovable, pretentiousness
| devotion, wisdom, vitality, perception, krishna,
---
omniscient, nature, arjuna, matter, renunciation
14 |  | krishna, arjuna, tamas, indolence, transcends,
---
reborn, wisdom,death,rajas,wombs
| arjuna,tamas,sages,creation, prakriti,
---
attains,heedlessness,sattva,beings,restlessness
| purity,indolence,krishna, divinity,sinless,
---
infatuation,spirit,reborn,eternal,knowledge
15 |  | krishna,eternal,scriptures,wisdom, essence,
---
immutable,ashvattha,sages,tree,universe
| vedanta,ashvattha,eternal, souls,perceive,
---
tree,ramified,abode,inward,ancient
| eternal,creation,krishna, scriptures,wisdom,
---
perceive,celestial,regeneration,nourishment,tree
16 |  | krishna,arjuna,demonic,scriptures, purity,
---
god,renunciation,qualities,hypocrisy,shall
| arjuna,sacrifice,purity, souls,wrath,spiritedness,
---
devilish,aversion,pretentiousness,god
| godless,krishna,wrath, scriptures,harmlessness,
---
modesty,shall,destroy,delusion,hypocritical
17 |  | tamasic,krishna,sacrifices,worship,arjuna,
---
scriptures,disregard,rajas, hypocrisy,sattva
| worship,krishna, yakshas, arjuna,forsake,
---
shastra,sattva,rajas,pretentiousness,unholy
| sacrifice,purity,krishna, arjuna,scriptures,
---
nourishing,aryans,vigour,unclean,covet
18 |  | arjuna,discipline,sacrifice,renunciation, sannyasa,
---
discomfort,divine,tyaga,selfish,meaning
| arjuna,sacrifice,renunciation, doctrine,undertakes,
---
tyagi,abandon,embodied,actions,wise
| arjuna,benevolence,sacrifice, renunciation,philosophy,
---
relinquishment,desire,renounce,forgoing,act

TABLE XI: Keywords for All chapters Eknath Easwaran | Score | Mahatma Gandhi | Score | Shri Purohit Swami | Score
---|---|---|---|---|---
devotion | 1.83 | devotion | 2.17 | devotee | 1.54
arjuna | 1.61 | arjuna | 1.54 | worship | 1.51
krishna | 1.52 | omnipresent | 1.20 | krishna | 1.33
yoga | 1.31 | yoga | 1.16 | arjuna | 1.29
selfless | 1.29 | mortals | 1.00 | eternal | 1.10
eternal | 1.22 | embodied | 0.98 | meditate | 0.99
serene | 1.10 | lord | 0.93 | renunciation | 0.90
renouncing | 0.89 | unchanging | 0.92 | verily | 0.76
beings | 0.86 | attain | 0.87 | realise | 0.70
established | 0.66 | steadily | 0.73 | attention | 0.60
TABLE XII: Keywords extracted from Chapter 12 for the respective translations.

(a) Eknath Easwaran

(b) Mahatma Gandhi

(c) Shri Purohit Swami

Figure 14: Visualisation of UMAP embedding of keywords from the respective
translations.

##  V Further Analysis and Discussion

We note that the Bhagavad Gita is a conversation between Arjuna and Lord
Krishna which takes place in the middle of the Kurushetra battlefield before
the war begins. The conversation is merely a few hours long and over the
course of the Bhagavad Gita, Arjuna goes from being extremely distressed to a
more calm and lucid state aware of his responsibilities. We wish to explore if
the verses in which Arjuna has spoken can capture the variations of the
sentiments expressed. Therefore, we provide an analysis where the we calculate
a sentiment polarity score in the range based on grouping the optimistic and
pessimistic sentiments. In order to quantify the polarity of predicted
sentiments, we assign a sentiment polarity score based on set of verses in
each chapter by the respective speakers (Lord Krishna and Arjuna). We assign
the sentiments such as ”optimistic”, ”thankful” and ”empathetic” a score of
+1, and further assign ”pessimistic”, ”anxious”, ”sad”, ”annoyed”, and
”denial” a score of -1. Finally, we assign ”joking” and ”surprise” a score of
0 since they can have positive or negative connotation. It should be noted
that Arjuna does not speak in certain chapters (7, 9, 13, 15, 16) and hence,
the polarity score is zero. We show the chapter-wise polarity score for Eknath
Easwaran’s translation in Figure 11 for Arjuna where we observe that Chapter 1
has the maximum negative sentiment over time (chapters). We note that this is
due to the situation where in the begining of the Bhagavad Gita, Arjuna is
distraught and refuses to fight the war on seeing his family and friends on
the opposite side. Moreover, we notice the polarity increases towards positive
sentiments from Chapter 2 and then there is a bump in Chapter 11. This is
because in Chapter 11, Arjuna is overwhelmed by Lord Krishna’s universal form
(viraat roopa) and considers himself fortunate to have witnessed it, hence the
maximum positive sentiments come from this chapter. Similarly, we have shown
the variation of Lord Krishna’s sentiments in Figure 12 where he begins with a
neutral sentiment polarity and then becomes negative in Chapter 2, and
positive afterwards till Chapter 13. Moving on, his sentiments fluctuates and
also becomes close to neutral as the time progresses.

In Table V, verse 2 validates our earlier observation of the differences in
central themes (selfless service - Eknath Easwaran, right action - Shri
Purohit Swami). It is important to note that the BERT model has been trained
on a dataset of tweets which featured sarcastic comments and jokes. Hence, the
Senwave dataset contains the label - ”joking”. However, in the Bhagavad Gita,
this sentiment is not apt and so, some verses may have been classified as
”joking” sentiment inaccurately. Please note that we have shown the pre-
processed verses here as they have been used for sentiment prediction and not
the original verses.

In terms of the limitations, we have two major types of limitations. The first
is in terms of limitations of the model and the training data - since the
model is based on pre-trained BERT model which is generic and cannot capture
the essence of Hindu philosophy that features Sanskrit terms that are
difficult to be translated. We note that there are a number of BERT specific
models, for different domains such as Med-BERT for medicine [123] and legal-
BERT for law [124]. In the case of philosophy, we do not have any pre-trained
model for masked language modelling. Our application is unique, and hence a
philosophy BERT based on western philosophy will not suffice. We would need a
pre-trained Hindu philosophy BERT model to capture the essence and specific
words such as Brahman, avatar, karma, bhagwan, Vishnu, maya, viraat roopa, and
dharma. Once the key terms are recognised, our pre-processing of the
respective texts will not be crude where we simply change the words to English
without considering the context. We need to note that the English language is
heavily influenced by the concept of God in Abrahamic religions (Christianity,
Judian and Islam); hence, the term God has a specific meaning which is in
contrast to equivalent concepts such as Brahman or Vishnu. The Abrahmic notion
of God refers to the creator who is not part of the universe [125], but can
control the universe and lives of the mortals with rewards and punishment. The
Hindu definition of God is entirely different [126], where the creator and the
creation are seen as the same. Hindu philosophy views the creation featuring
two major aspects; i.e consciousness (purusha) which is depicted by the male
Gods such as Brahma and nature and matter; nature (prakrati) which is depicted
by their consorts, the Goddesses such as Saraswati, Parvati and Laxmi.

We note that in Bhagavad Gita chapter 2, there are fifteen predicates used to
define the Atman which in some philosophical traditions such as the Advaita
Vedanta is seen same as the Paramatman (supreme Atman) and also Brahman
(ultimate reality). The descriptions of Atman are given by the terms avinasin
(non-perishable), avyaya (non-changeable), anasin (non-destructible), aprameya
(nonmeasurable), aja, (non-born) nitya (eternal), sasvata (constant),
sarvagata (omnipresent), sthanu (steadfast) acale (non-movable), sanatana
(everlasting) avyakta (non-manifest), acintya (nonconceivable), avikarya (non-
variable), and avadhya (non-violable). These definitions of the supreme
reality are also found in the Upanishads [127]. These include, tat tvam asi
(thou art that) - Chandogya Upanishad - 6.8.7 aham brahmāsmi (I am Brahman) -
Brihadaranyaka Upanishad 1.4.10 which build the foundation of Advaita Vedanta
[128].

Hinduism is often labelled as polytheist, but in principle it is pantheism
[129] which is a philosophical system based on the notion that reality is
identical with divinity, i.e the creator and creation are the same. Apart from
these, Hindu philosophy also caters for schools of atheism such as Carvaka
philosophy [130] and non-theism such as Buddhism and Jainism. These schools
and definitions make it difficult to translate Sanskrit words to English since
they have historical development of the terms with philosophical annotations.
For examples, Vishnu means ”preserver”, ”vastness” and Brahma ”refers to ”the
creator” and Shiva can refer to ”the auspicious one” and and depending on the
content, various different names are used. For instance, Natraja refers to the
the dancing Shiva - widely known as the cosmological dancer [131]. Rudra often
refers to the personality of Shiva in anger. Moreover, Sanskrit terms with
similar wordings can have entirely different meaning, e. Bhram (illusion),
Brahma (creator), Brahman (ultimate reality) and Brahmand (universe), Brahmin
(priest or academic caste/profession).

Apart from the context and the difference in the philosophical system, we note
that the challenge of translating the Bhagavad Gita is not only that it is
written in an ancient language which is not a conversational language
nowadays, but also that it is written as a poem (as a song). The term ”gita”
translates to song and Bhagavad Gita translates to song of the divine. The
Bhagavad Gita has been written as a song so that it can be remembered and sung
and this is how it has been transmitting through oral tradition. It is well
known that translating poetry and songs leads to losing rhyming patterns and
at times meaning, since certain metaphors and expressions are constrained to
specific languages, cultural traditions and time-frames [132, 133]. Moreover,
certain verses in the Gita can have multiple layers of meaning since they are
written in form of poetry and feature literary devices such as metaphors
[134]. Hence, translating such texts are very challenging, due to language and
style of the ancient Hindu writers also known as Rishi’s [135, 136]. Note
there are certain prominent translations, such as the one by Sir Edwin Arnold
that are giving a poetic interpretation [137] which does not distinguish the
different verses. A recent translation by Badhe [138] maintained the verses
with rhythm and rhyming patterns so that it does not lose the essence of the
song-verse style from the original Sanskrit version. Future work can be done
by analysis of sentiment and semantic similarity of translations that maintain
rhythm and rhyme.

##  VI Conclusion and Future work

We presented a framework for comparing translations of the Bhagavad Gita using
sentiment and semantic analysis. In terms of the comparison of the
translations with statistical measures (such as bigrams and trigrams), we
found major differences in the translations. Due to the nature of the Sanskrit
language and the fact that the Bhagavad Gita is a song with rhythm and rhyme,
it is not surprising that the different translator’s used different vocabulary
to describe the same concepts. Moreover, we note that the vast difference in
the dates of the translations (1935, 1946, and 1985); hence it is natural to
find a major change in vocabulary and expressions. Moreover, we also found
that two of the earlier translators (Mahatma Gandhi and Shri Purohit Swami)
used archaic words which were pre-processed to modern vocabulary so that it is
easier for modelling. In the translation by Eknath Easwaran, we did not find
any archaic words. Furthermore, the translators also used different names for
Arjuna and Lord Krishna, which we also pre-processed. Hence, the language and
vocabulary across the the different translations vastly differ.

In the sentiment analysis , we find that although the language and vocabulary
has been vastly different, the BERT-based language model has been able to
recognise the sentiments well which have been mostly similar given the
different chapters of the respective translations. The sentiments such as
optimistic, annoyed and surprised have been the most expressed while the model
also considered a significant number of verses as joking. In the heatmaps
(Figure 10), we observe that verses labelled as ’joking’ have a significant
overlap with ”optimistic”, ”annoyed” and ”surprise”. We could have removed the
joking label from the training data as we know that the Bhagavad Gita does not
contain jokes, but it is interesting to know that due to the translation
style, humour was detected in the way certain questions were asked and
answered. We present some verses in Table XIII that were classified as
”Joking”. We note that these were not jokes but verses that can be considered
humorous, which is based on the way experts have hand-labelled tweets during
COVID-19 outbreak in 2020. We can note this as a major limitation since we are
using hand labelled sentiments from social media as training data and applying
it to a entirely different area - philosophy and religion. It is also
interesting to find how the sentiment polarity changes for Arjuna and Lord
Krishna over time, as the conversation deepens where Arjuna is pessimistic
towards the beginning and then becomes optimistic with knowledge of Hindu
philosophy and Karma Yoga imparted by Lord Krishna. Apart from the knowledge
given by the philosophy of karma and dharma, the polarity of the sentiments
expressed which changed over time could be a reason why the Bhagavad Gita is
seen as a book of psychology, management and conflict resolution. The
sentiments expressed by Krishna shows that with philosophical knowledge of
dharma and mentor-ship, a troubled mind can get clarity for making the right
decisions in times of conflict.

The results from the semantic analysis also showed that the meaning conveyed
across the different translations were similar although different language and
vocabulary were used. Further, the keywords extracted from the different
translations also showed similar terms, although in different order of
importance.

Future work can focus in using Sanskrit expert translators to review the
sentiments presented by the framework for the different translators. Moreover,
we can also apply the framework to translations of the Bhagavad Gita that
retain rhythm and rhyme. The same framework can be used to analyse the
translations of other texts, these include the Upanishads and also
translations of texts in other domains from different languages.

## Data and Code

Python-based open source code and data can be found here
555https://github.com/sydney-machine-learning/sentimentanalysis_bhagavadgita.

## Author contributions statement

R. Chandra devised the project with the main conceptual ideas and experiments
and contributed to overall writing, literature review and discussion of
results. V. Kulkarni provided implementation and experimentation and further
contributed in results visualisation and analysis.

## Acknowledgement

We acknowledge contributions of Sweta Rathi who supported earlier part of the
project.

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Chapter |  | Eknath Easwaran | Mahatma Gandhi | Shri Purohit Swami
---|---|---|---|---
5 | Verse 23 |  | But those who overcome the
---
impulses of lust and anger which
arise in the body are made
whole and live in joy.
| The man who is able even here on
---
earth, before he is released from
the body, to hold out against the floodtide
of lust and wrath,—he is a yogi,
he is happy.
| He who, before he leaves
---
his body, learns to surmount
the promptings of desire and anger
is a saint and is happy.
| Predicted Sentiment |  | Joking, Optimistic
---
| Joking, Optimistic
---
| Joking, Optimistic
---
11 | Verse 14 |  | Filled with amazement, his hair
---
standing on end in ecstasy, he bowed
before the Lord with joined palms and
spoke these words.
| Then Arjuna, wonderstruck and thrilled
---
in every fibre of his being, bowed
low his head before the Lord, addressing
Him thus with folded hands. Arjuna Said:
| Thereupon Arjuna, dumb with awe,
---
his hair on end, his head bowed,
his hands clasped in salutation, addressed the
Lord thus:
| Predicted Sentiment |  | Joking
---
| Joking, Surprise
---
| Joking, Surprise
---
11 | Verse 27 |  | All are rushing into your
---
awful jaws; I see some of them
crushed by your teeth.
| Are hastening into the fearful
---
jaws of Your terrible mouths. Some
indeed, caught between Your teeth,
are seen, their heads being
crushed to atoms.
| I see them all rushing headlong
---
into Your mouths, with terrible tusks,
horrible to behold. Some are mangled between
your jaws, with their heads crushed
to atoms.
| Predicted Sentiments |  | Joking
---
| Joking, Anxious, Annoyed
---
| Joking
---

TABLE XIII: Some verses with ’Joking’ as the predicted sentiment.