Hindi Poetry Classification using Eager Supervised Machine Learning Algorithms

Document management is an essential but critical task. Categorizing these documents into the groups benefits many applications in commercial, industrial and other domains. Manual efforts are reduced by placing documents into its corresponding class. And predicting the category of document. It also reduces the time which otherwise would have required to read the document. Hindi has gained significant value in different fields like information technology, since the last decade due to the multilinguistic talent supported by websites. Natural Language toolkits along with text mining generate speedy, economic and scalable results. In spite of gaining importance in the digital era, Hindi document classification is targeted by very few researchers. Two eager machine learning algorithms are applied on the corpus containing 450 Hindi poems. Poetry/poem gets classified based on terms present in it. The classifiers are evaluated using a misclassification error.