Text Mining Approach to Emotion Analysis in Translation of Surah Yusuf With NRC Emotion Lexicon

Annisa Syafiqah; Syahiduz Zaman; Mochamad Imamudin

doi:10.25299/itjrd.2025.17765

Authors

Annisa Syafiqah Departement of Informatics Engineering, Maulana Malik Ibrahim Islamic State University
Syahiduz Zaman Departement of Informatics Engineering, Maulana Malik Ibrahim Islamic State University
Mochamad Imamudin Departement of Informatics Engineering, Maulana Malik Ibrahim Islamic State University

DOI:

https://doi.org/10.25299/itjrd.2025.17765

Keywords:

Alquran , Emotional Analysis , Sentiment Analysis , Surah Yusuf , Text Mining

Abstract

In the digital era, the accessibility of vast textual data, including the Quran, has facilitated broader comprehension of its teachings. This study analyzes the emotions in the English translation of Surah Yusuf using the NRC Emotion Lexicon. The findings show that trust is the most dominant emotion (22.89%), followed by joy (15.66%), anticipation (13.25%), sadness (12.05%), fear (10.84%), anger (9.64%), surprise (8.43%), and disgust (7.23%). These results confirm the text's diverse emotional expressions and the effectiveness of the lexicon-based method. The research aligns with the initial goals and highlights the potential of emotion analysis in understanding religious texts. Future research can expand the analysis to more verses and use machine learning for improved accuracy. This study aids scholars and students in exploring the Quran's emotional and spiritual dimensions and can be adapted to other texts for broader applications.

Downloads

Download data is not yet available.

References

[1] S. K. Hamed and M. J. A. Aziz, “Classification of Holy Quran Translation Using Neural Network Technique,” J. Eng. Appl. Sci., vol. 12, no. 1, pp. 4468–4475, 2018, doi: 10.3923/jeasci.2018.4468.4475.

[2] M. Awais and S. Durrani, “Sentiment and Emotion Analysis on Consumer Review using NRCLex,” in 2nd International Conference on Engineering , Natural and Social Sciences, 2023, pp. 166–171. [Online]. Available: https://as-proceeding.com/index.php/icensos/article/view/514

[3] A. Mathur, P. Kubde, and S. Vaidya, “Emotional analysis using twitter data during pandemic situation: Covid-19,” Proc. 5th Int. Conf. Commun. Electron. Syst. ICCES 2020, no. Icces, pp. 845–848, 2020, doi: 10.1109/ICCES48766.2020.09138079.

[4] M. Ilham, M. M. Amiruddin, and A. Arif, “Islamic Harmony Examplar: The Qur’an’s Frame on Social Interaction with Non-Muslims,” J. Kaji. Ilmu-ilmu Keislam., vol. 6, no. 2, pp. 191–206, 2020, doi: 10.24952/fitrah.v6i2.2777.

[5] M. Zuhanda, A. Syofra, D. Mathelinea, P. Gio, Y. Anisa, and N. Novita, “Analysis of Twitter User Sentiment on the Monkeypox Virus Issue Using the NRC Lexicon,” J. Mantik, vol. 6, no. 4, pp. 3854–3860, 2023, doi: https://doi.org/10.35335/mantik.v6i4.3502. pdf

[6] B. Waspodo, Nuryasin, A. K. N. Bany, R. H. Kusumaningtyas, and E. Rustamaji, “Indonesia COVID-19 Online Media News Sentiment Analysis with Lexicon-based Approach and Emotion Detection,” 10th Int. Conf. Cyber IT Serv. Manag., pp. 1–6, 2022, doi: 10.1109/CITSM56380.2022.9935884.

[7] Y. A. N. Jannah and R. B. Prasetyo, “Analisis Sentimen dan Emosi Publik pada Awal Pandemi COVID-19 Berdasarkan Data Twitter dengan Pendekatan Berbasis Leksikon,” in Seminar Nasional Official Statistics 2022, 2022, pp. 597–607. doi: https://doi.org/10.34123/semnasoffstat.v2022i1.1483.

[8] N. Z. A. Rahman, A. A. Aziz, E. E. M. Noor, N. H. Ali, and N. A. A.H, “A Review on Impact on Human Emotion While Listening and Reciting Quran,” Int. J. Work Organ. Emot., vol. 13, no. 4, p. 1, 2022, doi: 10.1504/ijwoe.2022.10052304.

[9] X. Wang, L. Kou, V. Sugumaran, X. Luo, and H. Zhang, “Emotion Correlation Mining through Deep Learning Models on Natural Language Text,” IEEE Trans. Cybern., vol. 51, no. 9, pp. 4400–4413, 2021, doi: 10.1109/TCYB.2020.2987064.

[10] J. Gowri, M. Suriya, and S. Ajay, “Text Mining Model: A Review,” J. Emerg. Technol. Innov. Res., vol. 7, no. 2, pp. 248–251, 2020, [Online]. Available: http://www.jetir.org/papers/JETIR2002442.

[11] A. N. Rohman, E. Utami, and S. Raharjo, “Deteksi Kondisi Emosi pada Media Sosial Menggunakan Pendekatan Leksikon dan Natural Language Processing,” Eksplora Inform., vol. 9, no. 1, pp. 70–76, 2019, doi: 10.30864/eksplora.v9i1.277.

[12] L. Hickman, S. Thapa, L. Tay, M. Cao, and P. Srinivasan, “Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations,” Organ. Res. Methods, vol. 25, no. 1, pp. 114–146, 2022, doi: 10.1177/1094428120971683.

[13] Ibn Kathir, Tafsir Ibn Kathir (Abridged), Darussalam Publishers, [Online]. Available: https://www.dar-us-salam.com

[14] Al-Tabari, Tafsir Al-Tabari, Dar al-Ma’arif, [Online]. Available: https://www.daralmaarif.com