Sentiment and Tone Analysis of the Holy Qur’an Using Natural Language Processing

This article investigates sentiment and tone analysis of the verses of the Holy Qur’an using advanced Natural Language Processing (NLP) techniques. By leveraging deep learning transformer models such as AraBERT and MARBERT, distinct models were designed and implemented for sentiment analysis (positive, negative, and neutral) and multi-label tone analysis. These models aim to identify emotional and tonal patterns within the sacred text of the Qur’an. Evaluation results demonstrate satisfactory accuracy and F۱ scores in detecting these patterns. Furthermore, an analysis of the Qur’an’s text reveals a balanced distribution of tones across its chapters (surahs). This research underscores the potential of NLP as a powerful tool for analyzing complex and multifaceted religious texts, paving the way for future studies in this domain.

کلیدواژه ها:

the Qur’an ، Sentiment analysis ، Tone analysis ، Natural Language Processing (NLP) ، Deep Learning ، Transformers