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.