The Heart of Charani Poetry: An AI Interpretation of Emotional Resonance

Year : 2025 | Volume : 12 | Issue : 01 | Page : 34 39
    By

    Bhavin Mehta,

  • Hirenkumar Thakor,

  1. Assistant Professor (Ph.D. Scholar), Faculty of Computer Application, Noble University, Junagadh, Gujarat, India
  2. Associate Professor, Faculty of Computer Application, Noble University, Junagadh, Gujarat, India

Abstract

Poetry has long been a means of expressing emotions and ideas, yet understanding the emotional depth within poems can be challenging using traditional computer-based tools. This study explores the emotions embedded in Charani poems, an important genre in Indian literature, through the lens of their distinctive poetic style. The primary objective is to develop a novel method of analyzing emotions in Charani poetry, contributing to the broader field of emotion analysis in literature. Drawing inspiration from the Indian concept of “Navarasa”, which categorizes nine core emotions, the study applies this framework to classify over 300 Charani poems. By grouping the poems according to the nine emotions, rasas, such as love, anger, sorrow, and joy, the study aims to provide a structured approach to understanding the emotional significance in these works. This classification not only offers deeper insights into the poetic expressions of Charani poems but also presents a more accessible method for studying emotional content across poetry genres. By connecting ancient cultural concepts with modern computational analysis, this research provides a fresh perspective on emotional content in literature.

Keywords: Charani poems, navarasa, emotion detection, artificial intelligence, natural language processing

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Bhavin Mehta, Hirenkumar Thakor. The Heart of Charani Poetry: An AI Interpretation of Emotional Resonance. Journal of Artificial Intelligence Research & Advances. 2024; 12(01):34-39.
How to cite this URL:
Bhavin Mehta, Hirenkumar Thakor. The Heart of Charani Poetry: An AI Interpretation of Emotional Resonance. Journal of Artificial Intelligence Research & Advances. 2024; 12(01):34-39. Available from: https://journals.stmjournals.com/joaira/article=2024/view=191583


References

  1. Mehta B, Rajyagor B. Gujarati poetry classification based on emotions using deep learning. International Journal of Engineering Applied Sciences and Technology (IJEAST). 2021 May; 6(1): 358–362.
  2. Saini JR, Kaur J. Kāvi: An annotated corpus of Punjabi poetry with emotion detection based on ‘navrasa’. Procedia Comput Sci. 2020 Jan 1; 167: 1220–9.
  3. Bafna PB, Saini JR. An Application of Zipf’s Law for Prose and Verse Corpora Neutrality for Hindi and Marathi Languages. Int J Adv Comput Sci Appl. 2020; 11(3): 261–265.
  4. Tiple B, Thomas PA. Analysis of features for mood detection in north indian classical music-a literature review. Int J Res Comput Commun Technol. 2017 Jun; 6(6): 181–5.
  5. Josan G, Bawa S. Automatic Mood Classification of Punjabi Poems Using Supervised Approach. CSI journal of computing. 2017 Apr; 4(1): 34–42.
  6. Ahmad S, Asghar MZ, Alotaibi FM, Khan S. Classification of poetry text into the emotional states using deep learning technique. IEEE Access. 2020 Apr 14; 8: 73865–78.
  7. Bafna P, Saini JR. Hindi poetry classification using eager supervised machine learning algorithms. In 2020 IEEE International Conference on Emerging Smart Computing and Informatics (ESCI). 2020 Mar 12; 175–178.
  8. Audichya MK, Saini JR. Towards natural language processing with figures of speech in Hindi poetry. Int J Adv Comput Sci Appl. 2021; 12(3): 128–133.
  9. Haider T, Eger S, Kim E, Klinger R, Menninghaus W. PO-EMO: Conceptualization, annotation, and modeling of aesthetic emotions in German and English poetry. arXiv preprint arXiv:2003.07723. 2020 Mar 17.
  10. Kaur J, Saini JR. Punjabi poetry classification: the test of 10 machine learning algorithms. In Proceedings of the 9th international conference on machine learning and computing. 2017 Feb 24; 1–5.
  11. Audichya MK, Saini JR. Stanza type identification using systematization of versification system of Hindi poetry. Int J Adv Comput Sci Appl. 2021; 12(1): 142–53.
  12. Ahmed MA, Hasan RA, Ali AH, Mohammed MA. The classification of the modern Arabic poetry using machine learning. TELKOMNIKA (Telecommunication Computing Electronics and Control). 2019 Oct 1; 17(5): 2667–74.

Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 20/12/2024
Accepted 27/12/2024
Published 30/12/2024


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