Researchers Develop Classification Method For Sanskrit Text

IIT Roorkee researchers develop a sentiments analysis method for Sanskrit text that has achieved 92.83 per cent accuracy for sentiment classification in Sanskrit text

Sanskrit is one of the world’s most ancient languages; however, natural language processing tasks such as machine translation and sentiment analysis have not been explored. Indian Institute of Technology Roorkee (IIT Roorkee) researchers have developed an efficient method for Sanskrit text sentiment analysis. The proposed technique has achieved 87.50 per cent accuracy for machine translation and 92.83 per cent accuracy for sentiment classification.

The research proposed a method that comprises models for machine translation, translation evaluation, and sentiment analysis. The team involved in this research are Prof. Balasubramanian Raman, Department of Computer Science and Engineering and his PhD student Puneet Kumar, and M.Sc. student Kshitij Pathania, Department of Mathematics.

The machine translations have been used as cross-lingual mapping of the source and the target language. The obtained English translations are sufficiently mature and natural as the original English sentences. The model has been published as a Research Paper in a reputed peer-reviewed journal Applied Intelligence

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