زینب حاجی محمدی

With the expansion of digital technologies and the increasing use of intelligent platforms and applications, a new space has emerged for promoting Qur’anic concepts. In this context, Qur’anic digital businesses can simultaneously pursue cultural, social, and economic objectives. The present study aims to develop a taxonomy of digital business models focusing on the promotion of the Qur’an with a global and intergenerational perspective. To this end, successful local examples such as Habl al-Matin and Tanin Vahy , as well as international applications available on Google Play and the App Store, including Quran Companion, Muslim Pro, Ayat, and Learn Quran Tajwid , were analyzed. This analysis was conducted based on business model dimensions, including value proposition, target customer, sales channel, and value capture. Subsequently, a conceptual framework comprising several main categories of these models is proposed. This taxonomy can serve as a foundation for the design, evaluation, and targeted support of Qur’anic digital businesses (QDB) with a social orientation, benefiting researchers, cultural policymakers, and religious entrepreneurs.

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.