Exploring the Cross-Lingual Similarity of Valmiki Ramayana Using Semantic and Sentiment Analysis

The Sanskrit language holds significant importance in Indian culture because it has been extensively used in religious literature, primarily in Hinduism. Numerous ancient Hindu texts originally composed in Sanskrit have since been translated into various Indian and non-Indian languages by Indian and foreign authors. These translations offer a renewed cultural perspective and broaden the reach of Indian literature to a global audience. However, the manual translations of these religious texts often lack thorough validation. Recent advancements in semantic and sentiment analysis, powered by deep learning, have provided enhanced tools for understanding language and text. In this paper, we present a framework that uses semantic and sentiment analysis to validate the English translation of the Ramayana against its original Sanskrit version. The "Ramayana" which narrates the journey of the Rama, the king of Ayodhya, is an ancient Hindu epic written by the sage Valmiki. It is known for its contribution to human values for centuries and has universal relevance. Given the importance of Sanskrit in Indian culture and its influence on literature, understanding the translations of key texts like the Ramayana is essential. Multilingual Bidirectional Encoder Representations from Transformers (mBERT) model is utilized to analyze the selected chapters of the English and the Sanskrit versions of Ramayana. Our analysis reveals that sentiment and semantic alignment between the original Sanskrit and English translations remain consistent despite stylistic and vocabulary differences. The study also compares the findings of Bidirectional Encoder Representations from Transformers (BERT) with its other variants to examine which BERT variant is more suitable for validating Sanskrit text. The paper demonstrates the potential of deep learning techniques for cross-lingual validation of ancient texts.