Science and Technology Opportunities in Hadith Research

The purpose of this research is to discuss the opportunities of science and technology in hadith research. This research method uses qualitative research through literature study with a content analysis approach. The results and discussion of this research include the hadith science methodology, the development of the hadith validity method and the hadith text criticism method with a multidisciplinary and transdisciplinary approach, and scientific and technological opportunities in hadith research for the need for integration of science. The conclusion of this study is that research on hadith with a transdisciplinary approach opens wide opportunities for science and technology that are currently developing for the realization of the integration of science. This study recommends the importance of applying a transdisciplinary approach to science and technology opportunities in the development of hadith research in Islamic higher educatio

The Impact of Arabic Part of Speech Tagging on Sentiment Analysis: A New Corpus and Deep Learning Approach

Sentiment Analysis is achieved by using Natural Language Processing (NLP) techniques and finds wide applications in analyzing social media content to determine people’s opinions, attitudes, and emotions toward entities, individuals, issues, events, or topics. The accuracy of sentiment analysis depends on automatic Part-of-Speech (PoS) tagging which is required to label words according to grammatical categories. The challenge of analyzing the Arabic language has found considerable research interest, but now the challenge is amplified with the addition of social media dialects. While numerous morphological analyzers and PoS taggers were proposed for Modern Standard Arabic (MSA), we are now witnessing an increased interest in applying those techniques to the Arabic dialect that is prominent in social media. Indeed, social media texts (e.g. posts, comments, and replies) differ significantly from MSA texts in terms of vocabulary and grammatical structure. Such differences call for reviewing the PoS tagging methods to adapt social media texts. Furthermore, the lack of sufficiently large and diverse social media text corpora constitutes one of the reasons that automatic PoS tagging of social media content has been rarely studied. In this paper, we address those limitations by proposing a novel Arabic social media text corpus that is enriched with complete PoS information, including tags, lemmas, and synonyms. The proposed corpus constitutes the largest manually annotated Arabic corpus to date, with more than 5 million tokens, 238,600 MSA texts, and words from Arabic social media dialect, collected from 65,000 online users’ accounts. Furthermore, our proposed corpus was used to train a custom Long Short-Term Memory deep learning model and showed excellent performance in terms of sentiment classification accuracy and F1-score. The obtained results demonstrate that the use of a diverse corpus that is enriched with PoS information significantly enhances the performance of social media analysis techniques and opens the door for advanced features such as opinion mining and emotion intelligence

Artificial intelligence for understanding the Hadith

My research aims to utilize Artificial Intelligence to model the meanings of Classical Arabic Hadith, which are the reports of the life and teachings of the Prophet Muhammad. The goal is to find similarities and relatedness between Hadith and other religious texts, specifically the Quran. These findings can facilitate downstream tasks, such as Islamic question- answering systems, and enhance understanding of these texts to shed light on new interpretations. To achieve this goal, a well-structured Hadith corpus should be created, with the Matn (Hadith teaching) and Isnad (chain of narrators) segmented. Hence, a preliminary task is conducted to build a segmentation tool using machine learning models that automatically deconstruct the Hadith into Isnad and Matn with 92.5% accuracy. This tool is then used to create a well-structured corpus of the canonical Hadith books. After building the Hadith corpus, Matns are extracted to investigate different methods of representing their meanings. Two main methods are tested: a knowledge-based approach and a deep-learning-based approach. To apply the former, existing Islamic ontologies are enumerated, most of which are intended for the Quran. Since the Quran and the Hadith are in the same domain, the extent to which these ontologies cover the Hadith is examined using a corpus-based evaluation. Results show that the most comprehensive Quran ontology covers only 26.8% of Hadith concepts, and extending it is expensive. Therefore, the second approach is investigated by building and evaluating various deep-learning models for a binary classification task of detecting relatedness between the Hadith and the Quran. Results show that the likelihood of the current models reaching a human- level understanding of such texts remains somewhat elusive