IIT Roorkee Researchers Develop A Sentiments Analysis Method For Sanskrit Text

IIT Roorkee Researchers Develop A Sentiments Analysis Method For Sanskrit Text

The Indian Institute of Technology (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. This research of IIT Roorkee proposed a method that comprises models for machine translation, translation evaluation, and sentiment analysis. The team involved in this research are Professor Balasubramanian Raman, Department of Computer Science and Engineering and his PhD student Mr Puneet Kumar, and MSc student Mr 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.

Elaborating on the sentiment analysis model, Professor Balasubramanian Raman, Department of Computer Science, IIT Roorkee, said, "We have trained our model to predict sentiment scores in the range of positive, neutral, or negative. And the model uses statistics, natural language processing, and machine learning to determine the sentiment with over 90 per cent accuracy."

The dataset to perform this research was taken from the Valmiki Ramayana website developed and maintained by the IIT Kanpur researchers. The future plans of the researchers are to exploit the morphological properties of Sanskrit for better classification using only root words with their respective suffixes and prefix. It is also planned to evaluate whether the morphological richness of Sanskrit is retained while translating to English. Moreover, the researchers also plan to obtain a model that discerns the context of words in multiple languages and provides word embeddings of lesser dimensions.