Marathi Poem Classification using Machine Learning
R. A. Deshmukh1, Suraj Kore2, Namrata Chavan3, Sayali Gole4, Kumar Adarsh5
1Prof. Rushali A. Deshmukh, Department of Computer Engineering, JSPM’s Rajarshi Shahu College of Engineering, Affiliated to Savitribai Phule Pune University, Pune, India.
2Mr. Suraj Kore, Department of Computer Engineering, JSPM’s Rajarshi Shahu College of Engineering, Affiliated to Savitribai Phule Pune University, Pune, India.
3Ms. Namrata Chavan, Department of Computer Engineering, JSPM’s Rajarshi Shahu College of Engineering, Affiliated to Savitribai Phule Pune University, Pune, India.
4Ms. Sayali Gole, Department of Computer Engineering, JSPM’s Rajarshi Shahu College of Engineering, Affiliated to Savitribai Phule Pune University, Pune, India.
5Mr. Kumar Adarsh, Department of Computer Engineering, JSPM’s Rajarshi Shahu College of Engineering, Affiliated to Savitribai Phule Pune University, Pune, India.
Manuscript received on 11 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 2723-2727 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1761078219/19©BEIESP | DOI: 10.35940/ijrte.B1761.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Poem a piece of writing in which the expression of feelings and ideas is given intensity by particular attention to diction (sometimes involving rhyme), rhythm, and imagery. It is used for showing different views. Every poet writes a poem with a different intention and different views. In the proposed system we have classified the poem according to its sentiments by using words of different categories. Machine learning algorithm SVM classifier is used for differencing the class of the poem. This system also enables the user to search the poem based on the poet name and poet type. For 341 poems of five categories ‘Friend’, ‘Prem’, ‘Bhakti’, ‘Prerna’ and ‘Desh’ accuracy achieved is 93.54%.
Index Terms: Machine Learning, Support Vector Machine, Sentiments, Confusion Matrix, Accuracy, Error Rate.
Scope of the Article: Classification