علیرضا طالب پور

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. 

Proving the miraculous nature of the Qur’an as one of the methods to believe in the Qur’an as a divine text has always attracted the attention of the researchers of the Qur’an; nevertheless, the published efforts on this subject are facing numerous methodological challenges. This research attempts to introduce a new and original method exempt from the challenges of the present methods by utilizing some of the techniques in mathematical sciences, logic and statistics. This scientific case-based method rates the effective parameters in the recognition of scientific miracle for one case. Accordingly, it will calculate the certainty of each case and proceeds to combine the ratings of various cases of scientific miracle to calculate the degree of certainty of the divine nature of the book based on those results. In combining the ratings, a threshold will be determined based on human belief behavior. An advantage of this method is that it is extra-religious, data driven, quantitative, applicable to all texts, and generalizable to the entire content. 

Some religious modernists believe that correct and scientific comprehension of the Qur’an is merely provided through historical and cultural study of the context of revelation time. Others, on the other hand, advocate that knowing interior and connected evidence suffices for Qur’an comprehension. Further, they say that exterior and unconnected evidence such as cultural and historical studies can have little effect on Qur’an comprehension. These various attitudes made us research them and represent a model to assess the dependency of Qur’an comprehension on cultural and historical conditions of revelation time. The present study examined works of scholars in this field such as Nasr Hamid Abu Zayd, Khorramshahi, and Tabatabaei through a descriptive and analytical method. Then, a model was recommended to evaluate the dependence of understanding of the Qur’an on the cultural and historical conditions of revelation time. The model includes four components: cause of revelation; vocabulary; subject matter; expressions which encompass twelve rules based on the features of verses in each component. Input of the model is one verse and its output is a dependent, independent, or semi-dependent mode.