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Journal articles on the topic 'Arabic data mining'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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Hathlian, Nourah F. Bin, and Alaaeldin M. Hafez. "Subjective Text Mining for Arabic Social Media." International Journal on Semantic Web and Information Systems 13, no. 2 (2017): 1–13. http://dx.doi.org/10.4018/ijswis.2017040101.

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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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Al Rababaa, Mamoun Suleiman, and Essam Said Hanandeh. "The Automated VSMs to Categorize Arabic Text Data Sets." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 1 (2014): 4074–81. http://dx.doi.org/10.24297/ijct.v13i1.2925.

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Text Categorization is one of the most important tasks in information retrieval and data mining. This paper aims at investigating different variations of vector space models (VSMs) using KNN algorithm. we used 242 Arabic abstract documents that were used by (Hmeidi & Kanaan, 1997). The bases of our comparison are the most popular text evaluation measures; we use Recall measure, Precision measure, and F1 measure. The Experimental results against the Saudi data sets reveal that Cosine outperformed over of the Dice and Jaccard coefficients.

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Essam, Hanandeh. "Arabic Text Categorization Using Three Classifiers Methods: A Comparative Study." International Journal of Computer Science Issues 15, no. 6 (2018): 49–52. https://doi.org/10.5281/zenodo.2544629.

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Today, text categorization is usually used in various areas, such as: information retrieval, data mining and text mining. The present study aims to test the K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Support Vector Machine (SVM) algorithms on a relatively large dataset of Arabic documents. The latter dataset includes 1,000 arabic documents that are distributed across 10 classes. The latter test is based on recall and precision measures. It was found that Supporting Vector Machine algorithms classifier outperforms the other ones.

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Hamoud, H. Alshammari. "Bag-of-Phrases (BoPh) and sentiment analysis of Arabic text in Twitter." Indian Journal of Science and Technology 13, no. 40 (2020): 4202–15. https://doi.org/10.17485/IJST/v13i40.1202.

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Abstract <strong>Background/Objectives:</strong> Sentiment analysis plays main role in various text mining problems. Although, the Arabic text mining is important especially in the field of sentiment analysis, there is a paucity of research in it, especially, when it plays an important role in different issues in Arabic countries. Arabic language has many dialects that people use to express their feelings in social media. The objective of this study is to perform an experiment that follow the subjective opinion from the text. Subjective Analysis is one way that we can implement to improve

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Hussien, Omar Kamal Eldien, Amal Elsayed Aboutabl, and Riham Mohamed Younis Haggag. "Comparative Performance of Data Mining Techniques for Cyberbullying Detection of Arabic Social Media Text." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 392–400. http://dx.doi.org/10.17762/ijritcc.v11i11s.8167.

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Cyberbullying has spread like a virus on social media platforms and is getting out of control. According to psychological studies on the subject, the victims are increasingly suffering, sometimes to the point of committing suicide among the victims. The issue of cyberbullying on social media is spreading around the world. Social media use is growing, and it can have useful and negative implications when you take into account how social media platforms are abused through different forms of cyberbullying. Although there is a lot of cyberbullying detection in English, there are few studies in the

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El-Alami, Fatima-Zahra, Said Ouatik El Alaoui, and Noureddine En-Nahnahi. "Deep Neural Models and Retrofitting for Arabic Text Categorization." International Journal of Intelligent Information Technologies 16, no. 2 (2020): 74–86. http://dx.doi.org/10.4018/ijiit.2020040104.

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Arabic text categorization is an important task in text mining particularly with the fast-increasing quantity of the Arabic online data. Deep neural network models have shown promising performance and indicated great data modeling capacities in managing large and substantial datasets. This article investigates convolution neural networks (CNNs), long short-term memory (LSTM) and their combination for Arabic text categorization. This work additionally handles the morphological variety of Arabic words by exploring the word embeddings model using position weights and subword information. To guara

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Alzahrani, S. S. "Data Mining Regarding Cyberbullying in the Arabic Language on Instagram Using KNIME and Orange Tools." Engineering, Technology & Applied Science Research 12, no. 5 (2022): 9364–71. http://dx.doi.org/10.48084/etasr.5184.

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This paper deals with data mining on verbal bullying by Instagram users. It tracks people who repeatedly have abusive behavior and may cause harm to other persons or groups. In this work, a dataset holding verbal bullying in the Arabic language was extracted from Instagram comments, and the entries were classified as regular verbal bullying and suspicious verbal bullying. KINIME and Orange open source data mining tools were utilized to discover comments that involved verbal bullying on Instagram and to delete previous comments while users sent their comments automatically and immediately. Clas

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Baker, Qanita, Farah Shatnawi, Saif Rawashdeh, Mohammad Al-Smadi, and Yaser Jararweh. "Detecting Epidemic Diseases Using Sentiment Analysis of Arabic Tweets." JUCS - Journal of Universal Computer Science 26, no. (1) (2020): 50–70. https://doi.org/10.3897/jucs.2020.004.

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Opinion mining is an important step towards facilitating information in health data. Several studies have demonstrated the possibility of tracking diseases using public tweets. However, most studies were applied to English language tweets. Influenza is currently one of the world's greatest infectious disease challenges. In this study, a new approach is proposed in order to detect Influenza using machine learning techniques from Arabic tweets in Arab countries. This paper is the first study of epidemic diseases based on Arabic language tweets. In this work, we have collected, labeled, filtered

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Elsaka, Tarek, Imad Afyouni, Ibrahim Hashem, and Zaher Al Aghbari. "Spatio-Temporal Sentiment Mining of COVID-19 Arabic Social Media." ISPRS International Journal of Geo-Information 11, no. 9 (2022): 476. http://dx.doi.org/10.3390/ijgi11090476.

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Since the recent outbreak of COVID-19, many scientists have started working on distinct challenges related to mining the available large datasets from social media as an effective asset to understand people’s responses to the pandemic. This study presents a comprehensive social data mining approach to provide in-depth insights related to the COVID-19 pandemic and applied to the Arabic language. We first developed a technique to infer geospatial information from non-geotagged Arabic tweets. Secondly, a sentiment analysis mechanism at various levels of spatial granularities and separate topic sc

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Al-Mashhadany, Abeer Khalid, Marwan B. Mohammed, and Mawlood Mukhlis Alrawi. "Inheritance issues' features extraction using Arabic text analyzer (IFAA)." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 611–24. https://doi.org/10.11591/ijeecs.v28.i1.pp611-624.

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Inheritance issue is part of our life. Daily, many persons may die. Person is gone, while his money stays for others. Islamic law took care of the issue of inheritance. Al-Quran has verses dedicated in inheritance issue. Al-Quran gives every person its rights, so; the share for each heir is determined. Islamic law jurists are asked frequently to solve inheritance issues. This work; inheritance issues’ features extraction using Arabic text analyzer (IFAA) hopes to analyze inheritance issue. It receives the issue as Arabic unstructured characterized text. It applies Arabic analyzer system

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Hizbullah, Nur, Zakiyah Arifa, Yoke Suryadarma, Ferry Hidayat, Luthfi Muhyiddin, and Eka Kurnia Firmansyah. "SOURCE-BASED ARABIC LANGUAGE LEARNING: A CORPUS LINGUISTIC APPROACH." Humanities & Social Sciences Reviews 8, no. 3 (2020): 940–54. http://dx.doi.org/10.18510/hssr.2020.8398.

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Purpose: The study explores the process of using Arabic websites for Arabic language learning, utilising the Arabic Corpus Linguistic approach. This approach enables data-mining out of websites, systematically compiling the mined data, as well as processing the data for the express purpose of Arabic language teaching including its clusters, such as Arabic pragmatics, Arabic linguistics, and Arabic translation teaching as well. MethodologyThe research is written descriptively and utilises qualitative methods used for analysing the process and step-by-step procedures to be executed to make

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Alhazmi, Ali, Rohana Mahmud, Norisma Idris, Mohamed Elhag Mohamed Abo, and Christopher Eke. "A systematic literature review of hate speech identification on Arabic Twitter data: research challenges and future directions." PeerJ Computer Science 10 (April 2, 2024): e1966. http://dx.doi.org/10.7717/peerj-cs.1966.

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The automatic speech identification in Arabic tweets has generated substantial attention among academics in the fields of text mining and natural language processing (NLP). The quantity of studies done on this subject has experienced significant growth. This study aims to provide an overview of this field by conducting a systematic review of literature that focuses on automatic hate speech identification, particularly in the Arabic language. The goal is to examine the research trends in Arabic hate speech identification and offer guidance to researchers by highlighting the most significant stu

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Norma, Bataina. "Towards Detecting Arabic Opinions Polarity on Social Networks." International Journal of Computer Science Issues 16, no. 2 (2019): 11–16. https://doi.org/10.5281/zenodo.3234112.

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The way from just read, to read & write web leads social networks users to be excited about distribution their feeling and opinions by several social media that support textual, audio and videos content. Few years ago, these opinions include various purposes such as: education, economic, political, and sport. With the increasing importance of these opinions, it becomes necessary to understand the meaning of them through the automatic analysis of user opinions. Opinion mining or sentiment analysis aims to fetch the attributes i.e. words from the opinions to decide the polarity of the users

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Zubiaga, Arkaitz, and Paolo Rosso. "Special issue on analysis and mining of social media data." PeerJ Computer Science 10 (February 29, 2024): e1909. http://dx.doi.org/10.7717/peerj-cs.1909.

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This Editorial introduces the PeerJ Computer Science Special Issue on Analysis and Mining of Social Media Data. The special issue called for submissions with a primary focus on the use of social media data, for a variety of fields including natural language processing, computational social science, data mining, information retrieval and recommender systems. Of the 48 abstract submissions that were deemed within the scope of the special issue and were invited to submit a full article, 17 were ultimately accepted. These included a diverse set of articles covering, inter alia, sentiment analysis,

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Al-Saggaf, Yeslam, and Amanda Davies. "Understanding the expression of grievances in the Arabic Twitter-sphere using machine learning." Journal of Criminological Research, Policy and Practice 5, no. 2 (2019): 108–19. http://dx.doi.org/10.1108/jcrpp-02-2019-0009.

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Purpose The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context. Design/methodology/approach To understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining p

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Bahruddin, Uril, Fahrizal Winky Ghifari, and Najib Ahmad Shofiyullah. "Improving Arabic Language Skills by Integrating the Durusul Lugah Al-'Arabia and Silsilah Al-Azhar As-Syarif Textbooks for PPDU Malang Students." Abjadia : International Journal of Education 7, no. 2 (2023): 160–73. http://dx.doi.org/10.18860/abj.v7i2.17854.

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Arabic learning for foreign speakers is not the same as for native speakers. Therefore, innovation is needed, including integrating teaching materials. This study aims to uncover the integration model of Arabic textbooks by describing the forms, influencing factors, and implications that have arisen from the development of Arabic. This research approach is qualitative with the type of case study. Data mining is carried out using observation, interviews, and documentation. The data analysis method is discriminatory-qualitative. The results of this study show that (1) the form of textbook integr

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Ismail, Muhammad Marwan, Farah Nadia Harun, Wan Moharani Muhamad, Nurhasma Muhamad Saad, and Zulkipli Md Isa. "THE ARAB SPRING ONLINE NEWS COVERAGE: CORPUS-BASED ANALYSIS OF THE TUNISIAN AND EGYPTIAN REVOLUTION KEYWORDS." International Journal of Humanities, Philosophy and Language 4, no. 14 (2021): 52–70. http://dx.doi.org/10.35631/ijhpl.414004.

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In 2011, the Arab world had become the centre of attention once again after the emergence of the so-called ‘Arab Spring’ in December 2010. This historical event in the modern history of the Arab region has brought significant social and political reform to the Arab world. The wave of Arab uprising begins in Tunisia at the end of 2010, rapidly separated into other neighbouring countries such as Egypt, Libya, Morocco, Syria, Bahrain, and Sudan. Since the early stage of protest, which mainly participated by locals, mass media has comprehensively reported this historical event, which brought down

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Wisam, Hazım Gwad Gwad, Mahmood Ismael Ismael Imad, and Gültepe Yasemin. "Twitter Sentiment Analysis Classification in the Arabic Language using Long Short-Term Memory Neural Networks." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 235–39. https://doi.org/10.35940/ijeat.B4565.029320.

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The increasing use of social media and the idea of extracting meaningful expressions from renewable and usable data which is one of the basic principles of data mining has increased the popularity of Sentiment Analysis which is an important working area recently and has expanded its usage areas. Compiled messages shared from social media can be meaningfully labeled with sentiment analysis technique. Sentiment analysis objectively indicates whether the expression in a text is positive, neutral, or negative. Detecting Arabic tweets will help for politicians in estimating universal incident-based

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Abdelkrim OUHAB, Mimoun MALKI, Djamel BERRABAH, and Faouzi BOUFARES. "An Unsupervised Entity Resolution Framework for English and Arabic Datasets." International Journal of Strategic Information Technology and Applications 8, no. 4 (2017): 16–29. http://dx.doi.org/10.4018/ijsita.2017100102.

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Entity resolution (ER) is an important step in data integration and in many data mining projects; its goal is to identify records that refer to the same real-world entity. Most existing ER frameworks have focused on datasets in Latin-based languages and do not support Arabic language. In this article, the authors present an unsupervised ER framework that supports English and Arabic datasets. Rather than using matching rules developed by an expert or manually labeled training examples, the proposed framework automatically generates its own training set. The generated training set is then used t

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Hadeel, N. Alshaer, A. Otair Mohammed, and Abualigah Laith. "Improved ICHI square feature selection method for Arabic classifiers." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 3 (2020): 157–70. https://doi.org/10.11591/ijict.v9i3.pp157-170.

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Feature selection problem is one of the main important problems in the text and data mining domain. This paper presents a comparative study of feature selection methods for Arabic text classification. Five of the feature selection methods were selected: ICHI square, CHI square, Information Gain, Mutual Information and Wrapper. It was tested with five classification algorithms: Bayes Net, Naive Bayes, Random Forest, Decision Tree and Artificial Neural Networks. In addition, Data Collection was used in Arabic consisting of 9055 documents, which were compared by four criteria: Precision, Recall,

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Almuqren, Latifah, Fatma S. Alrayes, and Alexandra I. Cristea. "An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach." Future Internet 13, no. 7 (2021): 175. http://dx.doi.org/10.3390/fi13070175.

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With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing customer churn by increasing customer satisfaction. Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. The related research reveals that many studies have focused on developing churner

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Abdelkoui, Feriel, and Mohamed-Khireddine Kholladi. "Extracting Criminal-Related Events from Arabic Tweets." Journal of Information Technology Research 10, no. 3 (2017): 34–47. http://dx.doi.org/10.4018/jitr.2017070103.

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Recently, Twitter as one of social networks has been considered as a rich source of spatio-temporal information and significant revenue for mining data. Event detection from tweets can help to predict more serious real-world events. Such as: criminal events, natural hazards, and the spread of epidemics. Etc. This paper deals with event-based extraction for criminal incidents from Arabic tweets. It presents a framework that supports automated extraction of spatial and temporal information from tweets. The proposed approach is based on combining various indicators, including the names of places

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Alshammari, Hamoud H. "Bag-of-Phrases (BoPh) and sentiment analysis of Arabic text in Twitter." Indian Journal of Science and Technology 13, no. 40 (2020): 4202–15. http://dx.doi.org/10.17485/ijst/v13i40.1202.

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Background/Objectives: Sentiment analysis plays main role in various text mining problems. Although, the Arabic text mining is important especially in the field of sentiment analysis, there is a paucity of research in it, especially, when it plays an important role in different issues in Arabic countries. Arabic language has many dialects that people use to express their feelings in social media. The objective of this study is to perform an experiment that follow the subjective opinion from the text. Subjective Analysis is one way that we can implement to improve the accuracy of the sentiment

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Hilmi, Danial, Nur Toifah, and Halimatus Sa’diyah. "Progress of Learning Arabic Outcome for State Islamic University Students." Proceeding of International Conference on Islamic Education (ICIED) 9, no. 1 (2024): 415. https://doi.org/10.18860/icied.v9i1.3171.

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The Arabic language learning process does not always have an impact on mastery of language skills. This is evident in the progress of Arabic language learning outcomes which absolutely have a good impact. In relation to this, this study aims to describe and compare the progress of Arabic language learning outcomes of students at PTKIN. The method used is through a descriptive quantitative approach with a population of all language center students and samples taken randomly from 264 students of UIN Maulana Malik Ibrahim Malang and 125 students of UIN Antasari Banjarmasin. Data collection techni

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THABTAH, FADI, WAEL HADI, NEDA ABDELHAMID, and AYMAN ISSA. "PREDICTION PHASE IN ASSOCIATIVE CLASSIFICATION MINING." International Journal of Software Engineering and Knowledge Engineering 21, no. 06 (2011): 855–76. http://dx.doi.org/10.1142/s0218194011005463.

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Associative classification (AC) is an important data mining approach which effectively integrates association rule mining and classification. Prediction of test data is a fundamental step in classification that impacts the outputted system accuracy. In this paper, we present three new prediction methods (Dominant Class Label, Highest Average Confidence per Class, Full Match Rule) and one rule pruning procedure (Partial Matching) in AC. Furthermore, we review current prediction methods in AC. Experimental results on large English and Arabic text categorisation data collections (Reuters, SPA) us

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Alshaer, Hadeel N., Mohammed A. Otair, and Laith Abualigah. "Improved ICHI square feature selection method for Arabic classifiers." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 3 (2020): 157. http://dx.doi.org/10.11591/ijict.v9i3.pp157-170.

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<span>Feature selection problem is one of the main important problems in the text and data mining domain. </span><span>This paper presents a comparative study of feature selection methods for Arabic text classification. Five of the feature selection methods were selected: ICHI square, CHI square, Information Gain, Mutual Information and Wrapper. It was tested with five classification algorithms: Bayes Net, Naive Bayes, Random Forest, Decision Tree and Artificial Neural Networks. In addition, Data Collection was used in Arabic consisting of 9055 documents, which were compared

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Ali Taher, Hawraa. "Dual-Language Sentiment Analysis: A Comprehensive Evaluating SVM, Logistic Regression, XGBoost, and Decision Tree Using TF-IDF On Arabic and English Dataset." Wasit Journal for Pure sciences 3, no. 4 (2024): 59–69. https://doi.org/10.31185/wjps.549.

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Sentiment analysis (SA) is a growing area of study that straddles a number of disciplines, including machine learning, data mining, and natural language processing. It is focused on the automatic extraction of viewpoints presented in a certain text. Many studies have been conducted in the area of sentiment analysis because to its broad uses, particularly on texts in English, whereas other languages like Arabic have gotten less attention. The Arabic language presents several difficulties, such as its rich morphology and the difficulty of tracing words back to their original roots. Arabic commen

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Almarimi, Abdulwahed, and Asmaa Salem. "Anomaly Detection in Arabic Texts using Ngrams and Self Organizing Maps." International Journal of Computer Science, Engineering and Applications 11, no. 04 (2021): 17–27. http://dx.doi.org/10.5121/ijcsea.2021.11402.

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Every written text in any language has one author or more authors (authors have their individual sublanguage). An analysis of text if authors are not known could be done using methods of data analysis, data mining, and structural analysis. In this paper, two methods are described for anomaly detections: ngrams method and a system of Self-Organizing Maps working on sequences built from a text. there are analyzed and compared results of usable methods for discrepancies detection based on character n-gram profiles (the set of character n-gram normalized frequencies of a text) for Arabic texts. Ar

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Almonayyes, Ahmad. "Multiple Explanations Driven Naïve Bayes Classifier." JUCS - Journal of Universal Computer Science 12, no. (2) (2006): 127–39. https://doi.org/10.3217/jucs-012-02-0127.

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Exploratory data analysis over foreign language text presents virtually untapped opportunity. This work incorporates Naïve Bayes classifier with Case-Based Reasoning in order to classify and analyze Arabic texts related to fanaticism. The Arabic vocabularies are converted to equivalent English words using conceptual hierarchy structure. The understanding process operates at two phases. At the first phase, a discrimination network of multiple questions is used to retrieve explanatory knowledge structures each of which gives an interpretation of a text according to a particular aspect of fanatic

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Elfaik, Hanane, and El Habib Nfaoui. "Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic Text." Journal of Intelligent Systems 30, no. 1 (2020): 395–412. http://dx.doi.org/10.1515/jisys-2020-0021.

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Abstract Sentiment analysis aims to predict sentiment polarities (positive, negative or neutral) of a given piece of text. It lies at the intersection of many fields such as Natural Language Processing (NLP), Computational Linguistics, and Data Mining. Sentiments can be expressed explicitly or implicitly. Arabic Sentiment Analysis presents a challenge undertaking due to its complexity, ambiguity, various dialects, the scarcity of resources, the morphological richness of the language, the absence of contextual information, and the absence of explicit sentiment words in an implicit piece of text

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Ghareb, Abdullah Saeed, Azuraliza Abu Bakara, Qasem A. Al-Radaideh, and Abdul Razak Hamdan. "Enhanced Filter Feature Selection Methods for Arabic Text Categorization." International Journal of Information Retrieval Research 8, no. 2 (2018): 1–24. http://dx.doi.org/10.4018/ijirr.2018040101.

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The filtering of a large amount of data is an important process in data mining tasks, particularly for the categorization of unstructured high dimensional data. Therefore, a feature selection process is desired to reduce the space of high dimensional data into small relevant subset dimensions that represent the best features for text categorization. In this article, three enhanced filter feature selection methods, Category Relevant Feature Measure, Modified Category Discriminated Measure, and Odd Ratio2, are proposed. These methods combine the relevant information about features in both the in

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M. Ilmi, M. Ilmi, and Nadiyah Nadiyah. "PROBLEMATIKA GURU PADA PEMBELAJARAN BAHASA ARAB DI KELAS VI MADRASAH IBTIDAIYAH NEGERI 10 BANJAR." DARRIS: Jurnal Pendidikan Madrasah Ibtidaiyah 2, no. 2 (2019): 98–114. http://dx.doi.org/10.47732/darris.v2i2.129.

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This study discusses the problems of teachers in learning Arabic in class VI Madrasah Ibtidaiyah Negeri 10 Banjar. The formulation of the problem in this study is how the teacher's problematics in learning Arabic in class VI Madrasah Ibtidaiyah Negeri 10 Banjar and what factors influence it. Subjects in this study were Arabic language teachers and 58th grade VI students. While the object in this study is the problematic of teachers in learning Arabic in class VI Madrasah Ibtidaiyah Negeri 10 Banjar and what factors influence it. This type of research is descriptive field research, while the ap

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Baker, Qanita, Farah Shatnawi, Saif Rawashdeh, Mohammad Al-Smadi, and Yaser Jararweh. "Detecting Epidemic Diseases Using Sentiment Analysis of Arabic Tweets." JUCS - Journal of Universal Computer Science 26, no. 1 (2020): 50–70. http://dx.doi.org/10.3897/jucs.2020.004.

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Opinion mining is an important step towards facilitating information in health data. Several studies have demonstrated the possibility of tracking diseases using public tweets. However, most studies were applied to English language tweets. Influenza is currently one of the world's greatest infectious disease challenges. In this study, a new approach is proposed in order to detect Influenza using machine learning techniques from Arabic tweets in Arab countries. This paper is the first study of epidemic diseases based on Arabic language tweets. In this work, we have collected, labeled, filtered

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Setyanto, Arief, Arif Laksito, Fawaz Alarfaj, et al. "Arabic Language Opinion Mining Based on Long Short-Term Memory (LSTM)." Applied Sciences 12, no. 9 (2022): 4140. http://dx.doi.org/10.3390/app12094140.

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Arabic is one of the official languages recognized by the United Nations (UN) and is widely used in the middle east, and parts of Asia, Africa, and other countries. Social media activity currently dominates the textual communication on the Internet and potentially represents people’s views about specific issues. Opinion mining is an important task for understanding public opinion polarity towards an issue. Understanding public opinion leads to better decisions in many fields, such as public services and business. Language background plays a vital role in understanding opinion polarity. Variati

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Mohamed, Sally, Mahmoud Hussien, and Hamdy M. Mousa. "ADPBC: Arabic Dependency Parsing Based Corpora for Information Extraction." International Journal of Information Technology and Computer Science 13, no. 1 (2021): 54–61. http://dx.doi.org/10.5815/ijitcs.2021.01.04.

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There is a massive amount of different information and data in the World Wide Web, and the number of Arabic users and contents is widely increasing. Information extraction is an essential issue to access and sort the data on the web. In this regard, information extraction becomes a challenge, especially for languages, which have a complex morphology like Arabic. Consequently, the trend today is to build a new corpus that makes the information extraction easier and more precise. This paper presents Arabic linguistically analyzed corpus, including dependency relation. The collected data includes

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Alhazmi, Huda. "Arabic Twitter Conversation Dataset about the COVID-19 Vaccine." Data 7, no. 11 (2022): 152. http://dx.doi.org/10.3390/data7110152.

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The development and rollout of COVID-19 vaccination around the world offers hope for controlling the pandemic. People turned to social media such as Twitter seeking information or to voice their opinion. Therefore, mining such conversation can provide a rich source of data for different applications related to the COVID-19 vaccine. In this data article, we developed an Arabic Twitter dataset of 1.1 M Arabic posts regarding the COVID-19 vaccine. The dataset was streamed over one year, covering the period from January to December 2021. We considered a set of crawling keywords in the Arabic langu

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Bisri, Achmad, Supardi Supardi, Yayu Heryatun, Hunainah Hunainah, and Annisa Navira. "Educational data mining model using support vector machine for student academic performance evaluation." Journal of Education and Learning (EduLearn) 19, no. 1 (2025): 478–86. http://dx.doi.org/10.11591/edulearn.v19i1.21609.

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In the educational landscape, educational data mining has emerged as an indispensable tool for institutions seeking to deliver exceptional and high-quality education. However, education data revealed suboptimal academic performance among a significant portion of the student population, which consequently resulted in delayed graduation. This experimental research generally aims to evaluate student graduation outcomes. Meanwhile, the specific aim is to predict student academic performance by applying the support vector machine (SVM) model based on sampling techniques. The proposed model is evalu

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Haryanto, Nur. "مشكلة ترجمة النصوص العربية للطلبة بالمدرسة المتوسطة الاسلامية الحكومية الثانية بنجكولو". Imtiyaz : Jurnal Pendidikan dan Bahasa Arab 2, № 1 (2018): 68. http://dx.doi.org/10.29300/im.v2i1.1259.

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Teaching materials in the teaching of Arabic in middle school One of them is the translation of some short and long sentence, as in the conversations and topics of choice that fit the curriculum. It is also taught to students of simple Arabic wholesale. However, from the information obtained by the researcher from the Arabic teacher he says that the students could not master this skill, this situation can be seen from their duties in translating the Arabic language into Indonesian language still suffer from limitations in the syntax of the sentences and the choice of words in the particular ex

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Hadjadj, Hassina, and Halim Sayoud. "Arabic Authorship Attribution Using Synthetic Minority Over-Sampling Technique and Principal Components Analysis for Imbalanced Documents." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–17. http://dx.doi.org/10.4018/ijcini.20211001.oa33.

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Nowadays, dealing with imbalanced data represents a great challenge in data mining as well as in machine learning task. In this investigation, we are interested in the problem of class imbalance in Authorship Attribution (AA) task, with specific application on Arabic text data. This article proposes a new hybrid approach based on Principal Components Analysis (PCA) and Synthetic Minority Over-sampling Technique (SMOTE), which considerably improve the performances of authorship attribution on imbalanced data. The used dataset contains 7 Arabic books written by 7 different scholars, which are se

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Khalafat, Monther, Ja'far S. Alqatawna, Rizik M. H. Al-Sayyed, Mohammad Eshtay, and Thaeer Kobbaey. "Violence Detection over Online Social Networks: An Arabic Sentiment Analysis Approach." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 14 (2021): 90. http://dx.doi.org/10.3991/ijim.v15i14.23029.

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<p class="0abstract">Today, the influence of the social media on different aspects of our lives is increasing, many scholars from various disciplines and majors looking at the social media networks as the ongoing revolution. In Social media networks, many bonds and connections can be established whether being direct or indirect ties. In fact, Social networks are used not only by people but also by companies. People usually create their own profiles and join communities to discuss different common issues that they have interest in. On the other hand, companies also can create their virtua

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