Relevant bibliographies by topics / Arabic data mining

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Arabic data mining'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Arabic data mining"

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Nadiroh, Yahdinil Firdha. "Penerapan Data Mining Untuk Analisis Pengaruh Nilai Imla dan Nahwu Terhadap Nilai Bahasa Arab Di Pesantren." Jurnal Riset Informatika dan Teknologi Informasi 1, no. 3 (2024): 82–87. http://dx.doi.org/10.58776/jriti.v1i3.125.

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Students in Islamic boarding schools often use Arabic as their daily language. It is even mandatory to use it to communicate. This requires students to study harder and memorize more Arabic vocabulary. This study aims to apply data mining using multiple linear regression methods to analyze the effect of Imla and Nahwu values on Arabic language values in female students in Islamic boarding schools. This study used samples of female students' values in Imla, Nahwu, and Arabic. This method is applied to identify the relationship and influence of the three values and whether they can affect the le
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Haraty, Ramzi A., and Rouba Nasrallah. "Indexing Arabic texts using association rule data mining." Library Hi Tech 37, no. 1 (2019): 101–17. http://dx.doi.org/10.1108/lht-07-2017-0147.

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Purpose The purpose of this paper is to propose a new model to enhance auto-indexing Arabic texts. The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules. Design/methodology/approach The proposed model uses an association rule algorithm for extracting frequent sets containing related items – to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The associations of words extracted are illustrated as sets of words that appear frequently together
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Bashir, Eman, and Mohamed Bouguessa. "Data Mining for Cyberbullying and Harassment Detection in Arabic Texts." International Journal of Information Technology and Computer Science 13, no. 5 (2021): 41–50. http://dx.doi.org/10.5815/ijitcs.2021.05.04.

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Broadly cyberbullying is viewed as a severe social danger that influences many individuals around the globe, particularly young people and teenagers. The Arabic world has embraced technology and continues using it in different ways to communicate inside social media platforms. However, the Arabic text has drawbacks for its complexity, challenges, and scarcity of its resources. This paper investigates several questions related to the content of how to protect an Arabic text from cyberbullying/harassment through the information posted on Twitter. To answer this question, we collected the Arab co
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Hejazi, Hani D., and Ahmed A. Khamees. "Opinion mining for Arabic dialect in social media data fusion platforms: A systematic review." Fusion: Practice and Applications 9, no. 1 (2022): 08–28. http://dx.doi.org/10.54216/fpa.090101.

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The huge text generated on social media in Arabic, especially the Arabic dialect becomes more attractive for Natural Language Processing (NLP) to extract useful and structured information that benefits many domains. The more challenging point is that this content is mostly written in an Arabic dialect with a big data fusion challenge, and the problem with these dialects it has no written rules like Modern Standard Arabic (MSA) or traditional Arabic, and it is changing slowly but unexpectedly. One of the ways to benefit from this huge data fusion is opinion mining, so we introduce this systemat
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AL-Mashhadany, Abeer K., Dalal N. Hamood, Ahmed T. Sadiq Al-Obaidi, and Waleed K. Al-Mashhadany. "Extracting numerical data from unstructured Arabic texts(ENAT)." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (2021): 1759–70. https://doi.org/10.11591/ijeecs.v21.i3.pp1759-1770.

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Unstructured data becomes challenges because in recent years have observed the ability to gather a massive amount of data from annotated documents. This paper interested with Arabic unstructured text analysis. Manipulating unstructured text and converting it into a form understandable by computer is a high-level aim. An important step to achieve this aim is to understand numerical phrases. This paper aims to extract numerical data from Arabic unstructured text in general. This work attempts to recognize numerical characters phrases, analyze them and then convert them into integer values. The i
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Thabtah, Fadi, Omar Gharaibeh, and Rashid Al-Zubaidy. "Arabic Text Mining Using Rule Based Classification." Journal of Information & Knowledge Management 11, no. 01 (2012): 1250006. http://dx.doi.org/10.1142/s0219649212500062.

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A well-known classification problem in the domain of text mining is text classification, which concerns about mapping textual documents into one or more predefined category based on its content. Text classification arena recently attracted many researchers because of the massive amounts of online documents and text archives which hold essential information for a decision-making process. In this field, most of such researches focus on classifying English documents while there are limited studies conducted on other languages like Arabic. In this respect, the paper proposes to investigate the pro
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Eldos, T. M. "Arabic Text Data Mining: a Root-Based Hierarchical Indexing Model." International Journal of Modelling and Simulation 23, no. 3 (2003): 158–66. http://dx.doi.org/10.1080/02286203.2003.11442267.

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Alothman, Manal Othman, Muhammad Badruddin Khan, and Mozaherul Hoque Abul Hasanat. "Review of Researches on Arabic Social Media Text Mining." Journal of Intelligent Systems and Computing 2, no. 1 (2021): 20–33. http://dx.doi.org/10.51682/jiscom.00201005.2021.

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Social media sites and applications have allowed people to share their comments, opinions, and point of views in different languages on mass scale. Arabic language is one of the languages that has seen huge surge in production of its digital textual content. The Arabic content and its metadata are a goldmine of useful information for a wide variety of applications. A large number of researchers are working on Arabic data in various domains of research such as natural language processing, sentiment analysis, event detection, named entity recognition, etc. This article presents a review of numbe
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.., Hani D., Ahmed A. Khamees, and Said A. Salloum. "Opinion mining for Arabic dialect in social media: A systematic review." International Journal of Advances in Applied Computational Intelligence 1, no. 2 (2022): 08–28. http://dx.doi.org/10.54216/ijaaci.010201.

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The huge text generated on social media in Arabic, especially the Arabic dialect becomes more attractive for Natural Language Processing (NLP) to extract useful and structured information that benefits many domains. The more challenging point is that this content is mostly written in an Arabic dialect, and the problem with these dialects it has no written rules like Modern Standard Arabic (MSA) or traditional Arabic, and it is changing slowly but unexpectedly. One of the ways to benefit from this huge data is opinion mining, so we introduce this systematic review for opinion mining from Arabic
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Burhanuddin, Alvian, Ahmad Latif Qosim, and Rizqi Amaliya. "Phrase Based and Neural Network Translation for Text Transliteration from Arabic to Indonesia." MATICS 14, no. 1 (2022): 13–17. http://dx.doi.org/10.18860/mat.v14i1.13853.

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Abstract- Transliteration is one solution to overcome the inability to read and write Arabic in Indonesia. However, this transliteration has many different versions in reality. The many differences in transliteration versions make it difficult for people to understand and pronounce the Arabic sentence. So there needs to be an approach to overcome the problem of these differences. The data mining approach can be used as an option to reduce these differences. In this study, the researcher found that automatic transliteration based on the data mining model had a reasonably good BLEU value.
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Dissertations / Theses on the topic "Arabic data mining"

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Alsaad, Amal. "Enhanced root extraction and document classification algorithm for Arabic text." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13510.

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Many text extraction and classification systems have been developed for English and other international languages; most of the languages are based on Roman letters. However, Arabic language is one of the difficult languages which have special rules and morphology. Not many systems have been developed for Arabic text categorization. Arabic language is one of the Semitic languages with morphology that is more complicated than English. Due to its complex morphology, there is a need for pre-processing routines to extract the roots of the words then classify them according to the group of acts or m
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Cicola, Ilaria. "Alchemy and computer : a computational analysis of the Jabirian corpus." Thesis, Paris, EPHE, 2016. http://www.theses.fr/2016EPHE5056/document.

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Cet élaborât veut donner un nouvel approche à l’étude du corpus jābirien, en prenant compte de la littérature et des études précédents et des problématiques inhérentes à ce particulier corpus (synonymie, polysémie, dispersion du savoir, citations d’autres auteurs, hypertextualité). Grace aux nouvelles technologies d’analyse computationnelle ce travail vise à la digitalisation de textes édités (Muḫtār Rasāʾil, Tabdīr al-iksīr al-aʿẓam, Kitāb al- ahjār), pour créer un corpus numérique annoté selon le Text Encoding Initiative (TEI), le système d’annotation le plus utilisé dans le Natural Language
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Alsharif, Abdulla H. "Intelligent M-Health-CBT combined technology for an enhanced smoking cessation management system using data mining techniques with a case study in Saudi Arabia." Thesis, Kingston University, 2016. http://eprints.kingston.ac.uk/37875/.

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Smoking has become one of the major global health concerns. Though there are various awareness activities being undertaken, the prevalence of smoking across the world is increasing at alarming levels. However, the extent of this increase varies among different countries. Even in culturally rich countries where smoking is considered as antagonistic behaviour both religiously and culturally, like Saudi Arabia, the prevalence of smoking is increasing at alarming levels. As smoking is mostly a behavioural aspect bundled with other factors, CBT (Cognitive Behavioural Therapy) integrated with m-heal
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Mikati, Ziad. "Du Data mining au Sense mining : modèle pour une analyse de la langue arabe, et ses représentations formelles en vue d'une application à des données demandant une haute sécurité." Besançon, 2010. http://www.theses.fr/2010BESA1017.

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Vannini, Alessandro. "Human Reliability Analysis for Dynamic Risk Assessment: a case of ammonia production plant." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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I fattori umani e organizzativi svolgono un ruolo chiave nella prevenzione e mitigazione di incidenti rilevanti. La Quantitative Risk Analysis (QRA), considera come cause di incidente solamente i fattori tecnici. Dunque è possibile integrare questa analisi con tecniche per Human Reliability Analysis (HRA), ma la loro applicazione è ancora limitata al settore nucleare. Inoltre, la staticità della QRA ha costruito le basi per la valutazione dinamica del rischio. Nel presente lavoro, è stato considerato un generico impianto di produzione di ammoniaca, per il quale è stato creato un database di i
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Book chapters on the topic "Arabic data mining"

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El BazzI, Mohamed Salim, Driss Mammass, Taher Zaki, and Abdelatif Ennaji. "A Graph-Based Ranking Model for Automatic Keyphrases Extraction from Arabic Documents." In Advances in Data Mining. Applications and Theoretical Aspects. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62701-4_25.

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Jiang, Zhiwei, Xiaoqing Ding, Liangrui Peng, and Changsong Liu. "Modified Bootstrap Approach with State Number Optimization for Hidden Markov Model Estimation in Small-Size Printed Arabic Text Line Recognition." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08979-9_33.

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Hamed, Mohamad, Tariq Mahmoud, Jorge Marx Gómez, and Georges Kfouri. "Using Data Mining and Business Intelligence to Develop Decision Support Systems in Arabic Higher Education Institutions." In Modernizing Academic Teaching and Research in Business and Economics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54419-9_4.

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Ferguson, K. P. "Operational remote sensing in Saudi Arabia: Landsat data and logistical support for the earth science community." In Remote sensing: an operational technology for the mining and petroleum industries. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-010-9744-4_29.

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Hathlian, Nourah F. Bin, and Alaaeldin M. Hafez. "Subjective Text Mining for Arabic Social Media." In Cognitive Analytics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch075.

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The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the fra
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Jolly, Simran Kaur, and Rashmi Agrawal. "Anatomizing Lexicon With Natural Language Tokenizer Toolkit 3." In Advances in Data Mining and Database Management. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6117-0.ch011.

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NLTK toolkit is an API platform built with Python language to interact with humans through natural language. The very first version of NLTK was released in 2005 (1.4.3), which was compatible with Python 2.4. The latest version was in September 2017 NLTK (3.2.5), which incorporated features like Arabic stemmers, NIST evaluation, MOSES tokenizer, Stanford segmenter, treebank detokenizer, verbnet, and vader, etc. NLTK was created in 2001 as a part of Computational Linguistic Department at the University of Pennsylvania. Since then it has been tested and developed. The important packages of this s
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Alharbi, Amirah. "Improving Mass Gathering Management Using Process Mining Techniques." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240003.

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Crowd management is a significant topic especially for countries that support gathering events frequently. The Kingdom of Saudi Arabia hosts and manages one of the world class annual religious gatherings known as “pilgrimage”. Several challenges are raised for managing and controlling such mass gathering event. In this paper we propose a comprehensive framework for event processes modelling and management. The framework consists of four main stages starts with acquiring temporal data and ends by modelling different processes of the event. The main contribution of this work is to demonstrate ho
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Klouche, Badia, Sidi Mohamed Benslimane, and Sakina Rim Bennabi. "Ooredoo Rayek." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch065.

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Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article
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Nagy, Imre Gábor. "Le rôle des pȃturages communaux dans l’agriculture de Pécs à l’époque du dualisme." In Different Approaches to Economic and Social Changes: New Research Issues, Sources and Results. Working Group of Economic and Social History Regional Committee of the Hungarian Academy of Sciences in Pécs, 2022. http://dx.doi.org/10.15170/seshst-02-06.

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The purpose of the study. To show the role of pastoral animal husbandry in dualism in a large Hungarian city where the majority of the population lived from industry, trade, mining and transport. How did the city assembly approach the maintenance of the pasture that made up part of the land. Applied methods. We examined the archives of the Baranya County Archives of the Hungarian National Archives, the archives of the city assembly and the city council, the accounting office, the economic supervision, the city regulations and cadastral maps, documents and maps, and we reviewed the local press.
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Conference papers on the topic "Arabic data mining"

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Miaji, Mohammed, and Yaser Miaji. "Data Mining for Social Network Pattern Recognition: A case study of TikTok in Saudi Arabia." In 2024 19th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP). IEEE, 2024. https://doi.org/10.1109/smap63474.2024.00017.

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Elhawary, Mohamed, and Mohamed Elfeky. "Mining Arabic Business Reviews." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.24.

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M.Ahmed, Abdelmoty, Reda Abo Alez, Muhammad Taha, and Gamal Tharwat. "Automatic Translation of Arabic Sign to Arabic Text (ATASAT) System." In The Fourth International Conference on Database and Data Mining. Academy & Industry Research Collaboration Center (AIRCC), 2016. http://dx.doi.org/10.5121/csit.2016.60511.

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Gashaw, Ibrahim, and H. L. Shashirekha. "Amharic-arabic Neural Machine Translation." In 5th International Conference on Data Mining and Applications. Aircc publishing Corporation, 2019. http://dx.doi.org/10.5121/csit.2019.91606.

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Tashtoush, Yahya, Areen Magableh, Omar Darwish, Lujain Smadi, Omar Alomari, and Anood ALghazoo. "Detecting Arabic YouTube Spam Using Data Mining Techniques." In 2022 10th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2022. http://dx.doi.org/10.1109/isdfs55398.2022.9800840.

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Ghareb, Abdullah S., Abdul Razak Hamdan, and Azuraliza Abu Bakar. "Text associative classification approach for mining Arabic data set." In 2012 4th Conference on Data Mining and Optimization (DMO). IEEE, 2012. http://dx.doi.org/10.1109/dmo.2012.6329808.

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Farra, Noura, Elie Challita, Rawad Abou Assi, and Hazem Hajj. "Sentence-Level and Document-Level Sentiment Mining for Arabic Texts." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.95.

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Alghamdi, Hanan M., and Ali Selamat. "Topic detections in Arabic Dark websites using improved Vector Space Model." In 2012 4th Conference on Data Mining and Optimization (DMO). IEEE, 2012. http://dx.doi.org/10.1109/dmo.2012.6329790.

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AL-Wehaibi, Rehab Nasser, and Muhammad Badruddin Khan. "Predicting Arabic Tweet Popularity by Use of Data and Text Mining Techniques." In the 6th International Conference. ACM Press, 2014. http://dx.doi.org/10.1145/2668260.2668290.

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S. AlDahri, Sulaiman. "Detection of Automatic The VOT Value for Voiced Stop Sounds in Modern Standard Arabic (MSA)." In The Fourth International Conference on Database and Data Mining. Academy & Industry Research Collaboration Center (AIRCC), 2016. http://dx.doi.org/10.5121/csit.2016.60505.

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