Classification of Encouragement (Targhib) And Warning (Tarhib) Using Sentiment Analysis on Classical Arabic

Hatem AlHasani, Saidah Saad, Junaidah Kassim


The Holy Qur’an is the main religious text of Islam. The Qur’an has its own methods of Targhib (encouragement) and Tarhib (warning), which are important features of the Qur’an. Most of the Quranic verses would urge and encourage people to do right and good deeds, and also warn them from committing evil and bad deeds. The method of classifying a text into two opposing opinions has been applied previously in solving the problem of sentiment analysis. Currently, it is applied in identifying between Targhib (encouragement) and Tarhib (warning) verses in the Qur’an. Each verse of the Qur’an can be treated as either an encouragement, warning or neutral. The language of the Holy Qur’an is one of the most challenging natural languages in sentiment analysis.  The aim of this work is to classify the verses of encouragement and warning using sentiment analysis and NLP techniques. Several approaches are used in the Sentiment Analysis classification, such as the machine learning approach, the lexicon-based approach and the hybrid approach. In carrying out this aim, the applied machine learning approach was used, where the impact of the use of different techniques such as POS tagging, N-Gram and Feature selection with correlation based were evaluated and investigated. 95.6% accuracy was achieved using Naïve Bayes (NB) and 91.5% accuracy was achieved using the Support Vector Machines (SVM). This study is a significant study in extracting information and knowledge from the Holy Qur’an. It is significant for both researchers in the field of Islamic studies as well as non-specialized researchers.

Full Text:



A. Z. Syed, “Applying sentiment and emotion analysis on brand tweets for digital marketing,†in Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on, 2015, pp. 1–6.

A. Yadollahi, A. G. Shahraki, and O. R. Zaiane, “Current State of Text Sentiment Analysis from Opinion to Emotion Mining,†ACM Comput. Surv., vol. 50, no. 2, p. 25, 2017.

S. V Wawre and S. N. Deshmukh, “Sentiment classification using machine learning techniques,†Int. J. Sci. Res, vol. 5, no. 4, pp. 1–3, 2016.

X. Ding and B. Liu, “Resolving object and attribute coreference in opinion mining,†in Proceedings of the 23rd International Conference on Computational Linguistics, 2010, pp. 268–276.

N. Boudad, R. Faizi, R. O. H. Thami, and R. Chiheb, “Sentiment analysis in Arabic: A review of the literature,†Ain Shams Eng. J., 2017.

M. Ahmad, S. Aftab, S. S. Muhammad, and S. Ahmad, “Machine Learning Techniques for Sentiment Analysis: A Review,†Int. J. Multidiscip. Sci. Eng, vol. 8, no. 3, pp. 27–32, 2017.

A. Abbasi, H. Chen, and A. Salem, “Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums,†ACM Trans. Inf. Syst., vol. 26, no. 3, p. 12, 2008.

E. Refaee and V. Rieser, “Subjectivity and sentiment analysis of Arabic twitter feeds with limited resources,†in Workshop on Free/Open-Source Arabic Corpora and Corpora Processing Tools Workshop Programme, 2014, p. 16.

A.-S. Ghadeer, I. Aljarah, and H. Alsawalqah, “Enhancing the Arabic Sentiment Analysis Using Different Preprocessing Operators,†New Trends Inf. Technol., p. 113, 2017.

K. M. Alomari, H. M. ElSherif, and K. Shaalan, “Arabic Tweets Sentimental Analysis Using Machine Learning,†in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2017, pp. 602–610.

S. A. Morsy, “Recognizing contextual valence shifters in document-level sentiment classification,†2011.

H. D. Kim, K. Ganesan, P. Sondhi, and C. Zhai, “Comprehensive review of opinion summarization,†2011.

T. Al-Moslmi, N. Omar, S. Abdullah, and M. Albared, “Approaches to cross-domain sentiment analysis: a systematic literature review,†IEEE Access, vol. 5, pp. 16173–16192, 2017.

B. Saberi and S. Saad, “Sentiment analysis or opinion mining: A review,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 5, pp. 1660–1666, 2017.

M. M. Altawaier and S. Tiun, “Comparison of machine learning approaches on arabic twitter sentiment analysis,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 6, no. 6, pp. 1067–1073, 2016.

B. Pang and L. Lee, “Opinion mining and sentiment analysis,†Found. Trends® Inf. Retr., vol. 2, no. 1–2, pp. 1–135, 2008.

N. Omar, M. Albared, T. Al-Moslmi, and A. Al-Shabi, “A comparative study of feature selection and machine learning algorithms for arabic sentiment classification,†in Asia information retrieval symposium, 2014, pp. 429–443.

R. E. Salah and L. Qadri binti Zakaria, “A Comparative Review of Machine Learning for Arabic Named Entity Recognition,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 2, pp. 511–518.

R. M. Duwairi and I. Qarqaz, “Arabic sentiment analysis using supervised classification,†in Future Internet of Things and Cloud (FiCloud), 2014 International Conference on, 2014, pp. 579–583.

M. Abdul-Mageed and M. T. Diab, “Subjectivity and Sentiment Annotation of Modern Standard Arabic Newswire,†in Proceedings of the 5th Linguistic Annotation Workshop, 2011, pp. 110–118.

M. Elarnaoty, S. AbdelRahman, and A. Fahmy, “A machine learning approach for opinion holder extraction in Arabic language,†arXiv Prepr. arXiv1206.1011, 2012.

M. Abdul-Mageed, M. Diab, and S. Kübler, “SAMAR: Subjectivity and sentiment analysis for Arabic social media,†Comput. Speech Lang., vol. 28, no. 1, pp. 20–37, 2014.

A. Mourad and K. Darwish, “Subjectivity and Sentiment Analysis of Modern Standard Arabic and Arabic Microblogs.,†2013.

M. Salameh, S. Mohammad, and S. Kiritchenko, “Sentiment after Translation: A Case-Study on Arabic Social Media Posts.,†in HLT-NAACL, 2015, pp. 767–777.

B. Sabri and S. Saad, “Arabic Sentiment Analysis with Optimal Combination of Features Selection and Machine Learning Approaches,†Res. J. Appl. Sci. Eng. Technol., vol. 13, no. 5, pp. 386–393, Sep. 2016.

R. M. Sallam, H. M. Mousa, and M. Hussein, “Improving Arabic Text Categorization using Normalization and Stemming Techniques,†2016.

I. Zeroual and A. Lakhouaja, “Arabic information retrieval: Stemming or lemmatization?,†in 2017 Intelligent Systems and Computer Vision (ISCV), 2017, pp. 1–6.

A. Pasha et al., “MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic.,†in LREC, 2014, vol. 14, pp. 1094–1101.

G. Tripathi and S. Naganna, “Feature selection and classification approach for sentiment analysis,†Mach. Learn. Appl. An Int. J., vol. 2, no. 2, pp. 1–16, 2015.

S. Kübler, C. Liu, and Z. A. Sayyed, “To use or not to use: Feature selection for sentiment analysis of highly imbalanced data,†Nat. Lang. Eng., pp. 1–35, 2017.

Z. M. Hira and D. F. Gillies, “A review of feature selection and feature extraction methods applied on microarray data,†Adv. Bioinformatics, vol. 2015, 2015.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development