Marathi poem classification using machine learning

12Citations
Citations of this article
9Readers
Mendeley users who have this article in their library.
Get full text

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%.

References Powered by Scopus

Punjabi poetry classification: The test of 10 machine learning algorithms

33Citations
N/AReaders
Get full text

Authorship identification for Tamil classical poem (Mukkoodar Pallu) using C4.5 algorithm

12Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Design and Development of Computational Tools for Analyzing Elements of Hindi Poetry

9Citations
N/AReaders
Get full text

A learning approach towards metre-based classification of similar Hindi poems using proposed two-level data transformation

3Citations
N/AReaders
Get full text

Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

3Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Deshmukh, R. A., Kore, S., Chavan, N., Gole, S., & Adarsh, K. (2019). Marathi poem classification using machine learning. International Journal of Recent Technology and Engineering, 8(2), 2723–2727. https://doi.org/10.35940/ijrte.B1761.078219

Readers over time

‘20‘21‘23‘2401234

Readers' Seniority

Tooltip

Professor / Associate Prof. 2

40%

Lecturer / Post doc 1

20%

PhD / Post grad / Masters / Doc 1

20%

Researcher 1

20%

Readers' Discipline

Tooltip

Computer Science 4

80%

Engineering 1

20%

Save time finding and organizing research with Mendeley

Sign up for free
0