Publication Type:

Conference Paper


Mining Intelligence and Knowledge Exploration, Springer International Publishing, p.664–670 (2015)


India being a diverse country rich in spoken languages with around 23 official languages has always left open a wide arena for NLP researchers. The increase in the availability of voluminous data in Indian languages in the recent years has prompted researchers to explore the challenges in the Indian language domain. The proposed work explores Sentiment Analysis on Hindi tweets in a constrained environment and hence proposes a model for dealing with the challenges in extracting sentiment from Hindi tweets. The model has exhibited an average performance with cross validation accuracy for training data around 56 % and a test accuracy of 43 %.

Cite this Research Publication

M. Venugopalan and Deepa Gupta, “Sentiment Classification for Hindi Tweets in a Constrained Environment Augmented Using Tweet Specific Features”, in Mining Intelligence and Knowledge Exploration, 2015, pp. 664–670.