درخت حوزه‌های تخصصی

Throughout history, numerous exegetes of the Qur’an have presented interpretations of the verses of the Qur’an in accordance with the customary understanding, while not methodically specifying the extent of the validity of the customary understanding, in the process of interpreting the Qur’an. Hence, it is essential to properly clarify the validity and position of customary understanding in the interpretation of diverse types of verses, such as doctrinal verses, ethical verses, verses concerning social and cultural issues, and scientific verses.<br />The results of this study indicate that the customary understanding is admissible as an indication and evidence in the interpretation of the Qur’an and can be used to understand all the concepts of the verses at the level of literal meaning, however, at the level of intended meaning it is only allowed for the understanding of specific verses referring to customary practices, not for metaphysical concepts or verses with specific Qur’anic terminology.

As data and context continue to expand, a vast amount of textual content, including books, blogs, and papers, is produced and distributed electronically. Analyzing such large amounts of content manually is a time-consuming task. Automatic detection of feelings and emotions in these texts is crucial, as it helps to identify the emotions conveyed by the author, understand the author's writing style, and determine the target audience for these texts. The Qur’an, regarded as the word of God and a divine miracle, serves as a comprehensive guide and a reflection of human life. Detecting emotions and feelings within the content of the Qur’an contributes to a deeper understanding of God's commandments. Recent advancements, particularly the application of transformer-based language models in natural language processing, have yielded state-of-the-art results that are challenging to surpass easily. In this paper, we propose a method to enhance the accuracy and generality of these models by incorporating syntactic features such as Parts Of Speech (POS) and Dependency Parsing tags. Our approach aims to elevate the performance of emotion detection models, making them more robust and applicable across diverse contexts. For model training and evaluation, we utilized the Isear dataset, a well-established and extensive dataset in this field. The results indicate that our proposed model achieves superior performance compared to existing models, achieving an accuracy of 77% on this dataset. Finally, we applied the newly proposed model to recognize the feelings and emotions conveyed in the Itani English translation of the Qur’an. The results revealed that joy has the most significant contribution to the emotional content of the Holy Qur’an.