Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (9): 626-636.doi: 10.23940/ijpe.22.09.p3.626636

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Naive Bayes and Neural Network Techniques for Marathi Poem Classification into Nine Rasa using Feature Selection

Rushali A. Deshmukh*   

  1. Department of Computer Engineering, JSPM's Rajarshi Shahu College of Engineering, Tathwade, Pune, 411033, India
  • Submitted on ; Revised on ; Accepted on
  • Contact: *E-mail address:

Abstract: In this approach, we considered classifying poems written in Marathi, one of the popular Indian languages, into nine categories. Using this classification, a person who is unaware of the Marathi Language can come to know what kind of emotion the poem shows. Here we considered tf-idf to represent the features of the poem. We have used Univariate feature selection(Chi2), Tree-based models, L1-based feature selection, and Recursive Feature Elimination to select top-ranked features. For nine categories of poems - Fear, Joy, Love(Prem), Sadness, Vir(Courage), Wonder, Anger, Depression, and Peace - the Naive Bayes classifier achieves maximum accuracy of 85% with Chi2 feature selection. Then we considered six categories of poems for classification. Experimentation is done using Naive Bayes and Neural Network machine learning algorithms. Among all feature selection methods, with Chi2 feature selection, the highest accuracy achieved is 97%.

Key words: natural language processing, stopword, feature selection, dimensionality reduction, naive bayes classifier, neural network, measures