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Journal articles on the topic 'Arabic preprocessing'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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1

Hegazi, Mohamed Osman, Yasser Al-Dossari, Abdullah Al-Yahy, Abdulaziz Al-Sumari, and Anwer Hilal. "Preprocessing Arabic text on social media." Heliyon 7, no. 2 (2021): e06191. http://dx.doi.org/10.1016/j.heliyon.2021.e06191.

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Ayedh, Abdullah, Guanzheng TAN, Khaled Alwesabi, and Hamdi Rajeh. "The Effect of Preprocessing on Arabic Document Categorization." Algorithms 9, no. 2 (2016): 27. http://dx.doi.org/10.3390/a9020027.

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Ahmed, Rawia I. O., and Mohamed E. M. Musa. "Preprocessing Phase for Offline Arabic Handwritten Character Recognition." International Journal of Computer Applications Technology and Research 5, no. 12 (2016): 760–63. http://dx.doi.org/10.7753/ijcatr0512.1005.

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Nassr, Z., N. Sael, and F. Benabbou. "PREPROCESSING ARABIC DIALECT FOR SENTIMENT MINING: STATE OF ART." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 323–30. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-323-2020.

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Abstract. Sentiment Analysis concerns the analysis of ideas, emotions, evaluations, values, attitudes and feelings about products, services, companies, individuals, tasks, events, titles and their characteristics. With the increase in applications on the Internet and social networks, Sentiment Analysis has become more crucial in the field of text mining research and has since been used to explore users’ opinions on various products or topics discussed on the Internet. Developments in the fields of Natural Language Processing and Computational Linguistics have contributed positively to Sentimen

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salim, marwa, sally Saad, and mostafa aref. "PREPROCESSING THE EGYPTIAN ARABIC DIALECT FOR PERSONALITY TRAITS PREDICTION." International Journal of Intelligent Computing and Information Sciences 19, no. 1 (2019): 1–12. http://dx.doi.org/10.21608/ijicis.2019.62603.

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Oussous, Ahmed, Fatima-Zahra Benjelloun, Ayoub Ait Lahcen, and Samir Belfkih. "ASA: A framework for Arabic sentiment analysis." Journal of Information Science 46, no. 4 (2019): 544–59. http://dx.doi.org/10.1177/0165551519849516.

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Sentiment analysis (SA), also known as opinion mining, is a growing important research area. Generally, it helps to automatically determine if a text expresses a positive, negative or neutral sentiment. It enables to mine the huge increasing resources of shared opinions such as social networks, review sites and blogs. In fact, SA is used by many fields and for various languages such as English and Arabic. However, since Arabic is a highly inflectional and derivational language, it raises many challenges. In fact, SA of Arabic text should handle such complex morphology. To better handle these c

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Mhamed, Mustafa, Richard Sutcliffe, Xia Sun, Jun Feng, Eiad Almekhlafi, and Ephrem Afele Retta. "Improving Arabic Sentiment Analysis Using CNN-Based Architectures and Text Preprocessing." Computational Intelligence and Neuroscience 2021 (September 6, 2021): 1–12. http://dx.doi.org/10.1155/2021/5538791.

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Sentiment analysis is an essential process which is important to many natural language applications. In this paper, we apply two models for Arabic sentiment analysis to the ASTD and ATDFS datasets, in both 2-class and multiclass forms. Model MC1 is a 2-layer CNN with global average pooling, followed by a dense layer. MC2 is a 2-layer CNN with max pooling, followed by a BiGRU and a dense layer. On the difficult ASTD 4-class task, we achieve 73.17%, compared to 65.58% reported by Attia et al., 2018. For the easier 2-class task, we achieve 90.06% with MC1 compared to 85.58% reported by Kwaik et a

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Aljuaid, Hanan, Dzulkifli Mohamad, and Muhammad Sarfraz. "Evaluation Approach of Arabic Character Recognition." International Journal of Computer Vision and Image Processing 1, no. 2 (2011): 58–77. http://dx.doi.org/10.4018/ijcvip.2011040105.

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This paper proposes and contributes towards designing a complete system for off-line Arabic character recognition. The proposed system is specifically meant for Arabic handwriting recognition, but it equally works for the typed character recognition. It has various phases including preprocessing and segmentation. It also includes thinning phase and finds vertical and horizontal projection profiles. The recognition phase is managed by genetic algorithm. The genetic algorithm stands on feature extraction algorithm that defines six features for each segment. The algorithm, for Arabic handwriting

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Luqman, Hamzah, Sabri A. Mahmoud, and Sameh Awaida. "Arabic and Farsi Font Recognition: Survey." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 01 (2015): 1553002. http://dx.doi.org/10.1142/s021800141553002x.

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Font Recognition (FR) is useful in improving optical text recognition accuracy and time. In addition, it can be used to restore the original document text fonts, styles and sizes. In this paper, we survey the literature of Arabic and Farsi FR research and used databases. The main phases of FR systems are surveyed (viz. preprocessing, classification techniques and used features). All published work of Arabic and Farsi FR, which the authors are aware of, are surveyed. To our knowledge, this is the first survey of Arabic/Farsi FR and used databases. In addition, the paper addresses the strengths

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Manal Nejjari and Abdelouafi Meziane. "SSAAR: An enhanced System for Sentiment Analysis of Arabic Reviews." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 20 (August 17, 2020): 81–95. http://dx.doi.org/10.24297/ijct.v20i.8827.

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Sentiment Analysis, or Opinion Mining, has recently captivated the interest of scientists worldwide. With the increasing use of the internet, the web is becoming overloaded by data that contains useful information, which can be used in different fields. In fact, many studies have shed light on Sentiment Analysis of online data in different languages. However, the amount of research dealing with the Arabic language is still limited. In this paper, an empirical study is led to Sentiment Analysis of online reviews written in Modern Standard Arabic. A new system called SSAAR (System for Sentiment

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M.Al-Shatnawi, Atallah. "A Preprocessing Model for Hand-Written Arabic Texts Based on Voronoi Diagrams." International Journal of Computer Science and Information Technology 7, no. 6 (2015): 1–18. http://dx.doi.org/10.5121/ijcsit.2015.7601.

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Ghallab, Abdullatif, Abdulqader Mohsen, and Yousef Ali. "Arabic Sentiment Analysis: A Systematic Literature Review." Applied Computational Intelligence and Soft Computing 2020 (January 29, 2020): 1–21. http://dx.doi.org/10.1155/2020/7403128.

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With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocess

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Husain, Fatemah, and Ozlem Uzuner. "A Survey of Offensive Language Detection for the Arabic Language." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 1 (2021): 1–44. http://dx.doi.org/10.1145/3421504.

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The use of offensive language in user-generated content is a serious problem that needs to be addressed with the latest technology. The field of Natural Language Processing (NLP) can support the automatic detection of offensive language. In this survey, we review previous NLP studies that cover Arabic offensive language detection. This survey investigates the state-of-the-art in offensive language detection for the Arabic language, providing a structured overview of previous approaches, including core techniques, tools, resources, methods, and main features used. This work also discusses the l

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Saeed, Thamir Rashed, Jabar Salman, and Alaa Hussein Ali. "Classification improvement of spoken arabic language based on radial basis function." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 402. http://dx.doi.org/10.11591/ijece.v9i1.pp402-408.

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The important task in the computer interaction is the languages recognition and classification. In the Arab world, there is a persistent need for the Arabic spoken language recognition To help those who have lost the upper parties in doing what they want through speech computer interaction. While, the Arabic automatic speech recognition (AASR) did not receive the desired attention from the researchers. In this paper, the Radial Basis Function(RBF) is used for the improvement of the Arabic spoken language letter. The recognition and classification process are based on three steps; these are; pr

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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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Ayadi, Rami, Mohsen Maraoui, and Mounir Zrigui. "Latent Topic Model for Indexing Arabic Documents." International Journal of Information Retrieval Research 4, no. 1 (2014): 29–45. http://dx.doi.org/10.4018/ijirr.2014010102.

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In this paper, the authors present latent topic model to index and represent the Arabic text documents reflecting more semantics. Text representation in a language with high inflectional morphology such as Arabic is not a trivial task and requires some special treatments. The authors describe our approach for analyzing and preprocessing Arabic text then we describe the stemming process. Finally, the latent model (LDA) is adapted to extract Arabic latent topics, the authors extracted significant topics of all texts, each theme is described by a particular distribution of descriptors then each t

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Ayadi, Rami, Mohsen Maraoui, and Mounir Zrigui. "Latent Topic Model for Indexing Arabic Documents." International Journal of Information Retrieval Research 4, no. 2 (2014): 57–72. http://dx.doi.org/10.4018/ijirr.2014040104.

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In this paper, the authors present latent topic model to index and represent the Arabic text documents reflecting more semantics. Text representation in a language with high inflectional morphology such as Arabic is not a trivial task and requires some special treatments. The authors describe their approach for analyzing and preprocessing Arabic text then they describe the stemming process. Finally, the latent model (LDA) is adapted to extract Arabic latent topics, the authors extracted significant topics of all texts, each theme is described by a particular distribution of descriptors then ea

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AbuZeina, Dia, and Taqieddin Mostafa Abdalbaset. "Exploring the Performance of Tagging for the Classical and the Modern Standard Arabic." Advances in Fuzzy Systems 2019 (January 23, 2019): 1–10. http://dx.doi.org/10.1155/2019/6254649.

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The part of speech (PoS) tagging is a core component in many natural language processing (NLP) applications. In fact, the PoS taggers contribute as a preprocessing step in various NLP tasks, such as syntactic parsing, information extraction, machine translation, and speech synthesis. In this paper, we examine the performance of a modern standard Arabic (MSA) based tagger for the classical (i.e., traditional or historical) Arabic. In this work, we employed the Stanford Arabic model tagger to evaluate the imperative verbs in the Holy Quran. In fact, the Stanford tagger contains 29 tags; however,

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Wahbeh, Abdullah, Mohammed Al-Kabi, Qasem Al-Radaideh, Emad Al-Shawakfa, and Izzat Alsmadi. "The Effect of Stemming on Arabic Text Classification." International Journal of Information Retrieval Research 1, no. 3 (2011): 54–70. http://dx.doi.org/10.4018/ijirr.2011070104.

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The information world is rich of documents in different formats or applications, such as databases, digital libraries, and the Web. Text classification is used for aiding search functionality offered by search engines and information retrieval systems to deal with the large number of documents on the web. Many research papers, conducted within the field of text classification, were applied to English, Dutch, Chinese, and other languages, whereas fewer were applied to Arabic language. This paper addresses the issue of automatic classification or classification of Arabic text documents. It appli

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Duwairi, Rehab, and Mahmoud El-Orfali. "A study of the effects of preprocessing strategies on sentiment analysis for Arabic text." Journal of Information Science 40, no. 4 (2014): 501–13. http://dx.doi.org/10.1177/0165551514534143.

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AL-SADOUN, HUMOUD B., and ADNAN AMIN. "A NEW STRUCTURAL TECHNIQUE FOR RECOGNIZING PRINTED ARABIC TEXT." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 01 (1995): 101–25. http://dx.doi.org/10.1142/s0218001495000067.

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This paper proposes a new structural technique for Arabic text recognition. The technique can be divided into five major steps: (1) preprocessing and binarization; (2) thinning; (3) binary tree construction; (4) segmentation; and (5) recognition. The advantage of this technique is that its execution does not depend on either the font or size of character. Thus, this same technique might be utilized for the recognition of machine or hand printed text. The relevant algorithm is implemented on a microcomputer. Experiments were conducted to verify the accuracy and the speed of this algorithm using

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Hamed Abd, Dhafar, Ahmed T. Sadiq, and Ayad R. Abbas. "PAAD: POLITICAL ARABIC ARTICLES DATASET FOR AUTOMATIC TEXT CATEGORIZATION." Iraqi Journal for Computers and Informatics 46, no. 1 (2020): 1–10. http://dx.doi.org/10.25195/ijci.v46i1.246.

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Now day’s text Classification and Sentiment analysis is considered as one of the popular Natural Language Processing (NLP) tasks. This kind of technique plays significant role in human activities and has impact on the daily behaviours. Each article in different fields such as politics and business represent different opinions according to the writer tendency. A huge amount of data will be acquired through that differentiation. The capability to manage the political orientation of an online article automatically. Therefore, there is no corpus for political categorization was directed towards th

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Hewahi, Nabil M., and Kathrein Abu Kwaik. "Automatic Arabic Text Summarization System (AATSS) Based on Semantic Features Extraction." International Journal of Technology Diffusion 3, no. 2 (2012): 12–27. http://dx.doi.org/10.4018/jtd.2012040102.

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Recently, the need has increased for an effective and powerful tool to automatically summarize text. For English and European languages an intensive works have been done with high performance and nowadays they look forward to multi-document and multi-language summarization. However, Arabic language still suffers from the little attentions and research done in this filed. In this paper, we propose a model to automatically summarize Arabic text using text extraction. Various steps are involved in the approach: preprocessing text, extract set of features, classify sentence based on scoring method

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AbuMansour, Hussein Y., and Fahad S. Almekhlafi. "An Intelligent Model for Text Forensics from Arabic Twitter Posts." Arab Journal of Forensic Sciences & Forensic Medicine 2, no. 2 (2020): 172–78. http://dx.doi.org/10.26735/zton7830.

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The number of twitter users in Arab countries at a rapid pace of growth. As for the social networking platforms, they are a suitable environment for luring innocent users and commit different forms of crimes against them by cyber predators through claiming something they are not such as fake ID, age or gender targeting certain segments of the society. This have resulted in a huge number of cybercrimes including Phishing, harassment, cyber terrorism and many other forms. In this context, a number of different research efforts in the literature are available for English but very rare for Arabic

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Dinges, Laslo, Ayoub Al-Hamadi, Moftah Elzobi, Sherif El-etriby, and Ahmed Ghoneim. "ASM Based Synthesis of Handwritten Arabic Text Pages." Scientific World Journal 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/323575.

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Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system th

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Butt, Hanan, Muhammad Raheel Raza, Muhammad Javed Ramzan, Muhammad Junaid Ali, and Muhammad Haris. "Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images." Forecasting 3, no. 3 (2021): 520–40. http://dx.doi.org/10.3390/forecast3030033.

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According to statistics, there are 422 million speakers of the Arabic language. Islam is the second-largest religion in the world, and its followers constitute approximately 25% of the world’s population. Since the Holy Quran is in Arabic, nearly all Muslims understand the Arabic language per some analytical information. Many countries have Arabic as their native and official language as well. In recent years, the number of internet users speaking the Arabic language has been increased, but there is very little work on it due to some complications. It is challenging to build a robust recogniti

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BOUSLAMA, FAOUZI. "NEURAL NETWORKS IN THE RECOGNITION OF MACHINE PRINTED ARABIC CHARACTERS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 03 (1999): 395–414. http://dx.doi.org/10.1142/s0218001499000239.

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The objective of this study is to analyze and compare three different recognition approaches to machine printed Arabic characters. The first approach is a template matching and a correlation technique where an input character is compared to a standard set of stored prototype images. The second and the third approaches are based on feature analysis and matching. The features in the second approach are extracted from the horizontal and vertical projections of the images of characters. The third approach is a structural approach where the features are extracted from the geometry of the segments t

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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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Al-Hagery, Mohammed Abdullah, Manar Abdullah Al-assaf, and Faiza Mohammad Al-kharboush. "Exploration of the best performance method of emotions classification for arabic tweets." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 2 (2020): 1010. http://dx.doi.org/10.11591/ijeecs.v19.i2.pp1010-1020.

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<p><span>Arab users of social media have significantly increased, thus increasing the opportunities for extracting knowledge from various areas of life such as trade, education, psychological health services, etc. The active Arab presence on Twitter motivates many researchers to classify and analysis Arabic tweets from numerous aspects. This study aimed to explore the best performance scenarios in the classification of emotions conveyed through Arabic tweets. Hence, various experiments were conducted to investigate the effects of feature extraction techniques and the N-gram model o

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Ali, Manal Mostafa. "Arabic sentiment analysis about online learning to mitigate covid-19." Journal of Intelligent Systems 30, no. 1 (2021): 524–40. http://dx.doi.org/10.1515/jisys-2020-0115.

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Abstract The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National Research Council Canada

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Sidi, Widya Dharma, and I. Gede Arta Wibawa. "Sum of Squared Difference (SSD) Template Matching Testing on Writing Learning Application." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 4 (2020): 453. http://dx.doi.org/10.24843/jlk.2020.v08.i04.p11.

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Abstract This research was conducted to determine the accuracy of the Sum of Squared Difference (SSD) Template Matching method in the Application of Learning Numbers Writing Games. This game application is an application created to help early childhood in recognizing Arabic numbers, namely numbers from 0 to 9. In the SSD Template Matching method there are several processes including Preprocessing, thinning, feature extraction, and classification (SSD template matching). In testing the game application involves 10 respondents who were asked to write numbers correctly as requested by the a

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Abdulateef, Samer, Naseer Ahmed Khan, Bolin Chen, and Xuequn Shang. "Multidocument Arabic Text Summarization Based on Clustering and Word2Vec to Reduce Redundancy." Information 11, no. 2 (2020): 59. http://dx.doi.org/10.3390/info11020059.

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Arabic is one of the most semantically and syntactically complex languages in the world. A key challenging issue in text mining is text summarization, so we propose an unsupervised score-based method which combines the vector space model, continuous bag of words (CBOW), clustering, and a statistically-based method. The problems with multidocument text summarization are the noisy data, redundancy, diminished readability, and sentence incoherency. In this study, we adopt a preprocessing strategy to solve the noise problem and use the word2vec model for two purposes, first, to map the words to fi

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Suhandy, Diding, and Meinilwita Yulia. "The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee." International Journal of Food Science 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/6274178.

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Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world’s priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that m

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Aljameel, Sumayh S., Irfan Ullah Khan, Nida Aslam, Malak Aljabri, and Eman S. Alsulmi. "Machine Learning-Based Model to Predict the Disease Severity and Outcome in COVID-19 Patients." Scientific Programming 2021 (April 19, 2021): 1–10. http://dx.doi.org/10.1155/2021/5587188.

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The novel coronavirus (COVID-19) outbreak produced devastating effects on the global economy and the health of entire communities. Although the COVID-19 survival rate is high, the number of severe cases that result in death is increasing daily. A timely prediction of at-risk patients of COVID-19 with precautionary measures is expected to increase the survival rate of patients and reduce the fatality rate. This research provides a prediction method for the early identification of COVID-19 patient’s outcome based on patients’ characteristics monitored at home, while in quarantine. The study was

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Aliwy, Ahmed H. "Tokenization as Preprocessing for Arabic Tagging System." International Journal of Information and Education Technology, 2012, 348–53. http://dx.doi.org/10.7763/ijiet.2012.v2.149.

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"The effects of Pre-Processing Techniques on Arabic Text Classification." International Journal of Advanced Trends in Computer Science and Engineering 10, no. 1 (2021): 41–48. http://dx.doi.org/10.30534/ijatcse/2021/061012021.

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In the last two decades, the amount of available Arabic text data on the World Wide Web is dramatically growing, making it the fourth most used language on the web. Accordingly, the demand for efficient Arabic text classification is increasing, especially for web page content filtering, information retrieval, and e-mail spam detection. Several Machine Learning algorithms have been implemented to classify Arabic documents. However, the results achieved are not comparable with those obtained in other languages such as English, primarily when using preprocessing techniques that do not take into c

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"Using Classification Techniques to SMS Spam Filter." International Journal of Innovative Technology and Exploring Engineering 8, no. 12 (2019): 1734–39. http://dx.doi.org/10.35940/ijitee.l3206.1081219.

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SMS is service that uses mobile phone that allows the users to exchange textual content. Spamming can be defined as sending unwanted content to a group of people for various purposes such as fraud. SMS spam is one form of spamming in which unwanted messages are delivered to many clients by spammers. Therefore, it has become necessary to develop SMS spam detection system to keep up with the current development of message services. Where the aim of this work is developing spam filter for Arabic and English languages by using two filter to be able to detect spam sms efficiently. Content based met

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Srifi, Mehdi, Ahmed Oussous, Ayoub Ait Lahcen, and Salma Mouline. "Evaluation of recent advances in recommender systems on Arabic content." Journal of Big Data 8, no. 1 (2021). http://dx.doi.org/10.1186/s40537-021-00420-2.

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AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we fir

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"Improve the Recognition of Spoken Arabic Letter Based on Statistical Features." Iraqi Journal of Computer, Communication, Control and System Engineering, December 10, 2018, 26–32. http://dx.doi.org/10.33103/uot.ijccce.18.3.3.

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The recognition and classification of languages represent a vital factor in the computer interaction. This paper presents Arabic Sign Language recognition, which is represented as an appealing application. The work in this paper is based on three steps; preprocessing, feature extraction and classification (Recognition). The statistical features have been used than the physical features, while Multilayer feed-forward neural network as classification methods. The recognition percent is 96.33% has been gained over-perform the earlier works. The simulation has been made by using Matlab 2015b.

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Kanan, Tarek, Bilal Hawashin, Shadi Alzubi, et al. "Improving Arabic Text Classification Using P-Stemmer." Recent Advances in Computer Science and Communications 13 (September 4, 2020). http://dx.doi.org/10.2174/2666255813999200904114023.

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Introduction: Stemming is an important preprocessing step in text classification, and could contribute in increasing text classification accuracy. Although many works proposed stemmers for English language, few stemmers were proposed for Arabic text. Arabic language has gained increasing attention in the previous decades and the need is vital to further improve Arabic text classification. Method: This work combined the use of the recently proposed P-Stemmer with various classifiers to find the optimal classifier for the P-stemmer in term of Arabic text classification. As part of this work, a s

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Damayanti, Ariesta, and Pujiatus Syahara. "PENERAPAN METODE BACKRPOPAGATION UNTUK IDENTIFIKASI HURUF HIJAIYAH TULISAN TANGAN." JSI: Jurnal Sistem Informasi (E-Journal) 10, no. 1 (2018). http://dx.doi.org/10.36706/jsi.v10i1.5135.

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Hijaiyah letters are Arabic spelling letters that are the original language of the Qur'an. Just like other types of letters, the hijaiyah has certain shapes and characteristics that will form a certain pattern. By using the concept of artificial neural networks, can dibanguun a system that can recognize the pattern by doing the previous training. One of the most commonly used meotodes in artificial neural network paradigms is the crawling or backpropagation buffer. This hijaiyah letters identification system is built using the handwritten hijaiyah image data of 150 images. The feature or featu

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Al-Taani, Ahmad T., and Fadi A. ALkhazaaleh. "Part-of-Speech Tagging for Arabic Text using Particle Swarm Optimization and Genetic Algorithm." Recent Advances in Computer Science and Communications 14 (January 14, 2021). http://dx.doi.org/10.2174/2666255814666210114120558.

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Background: Part of Speech (POS) Tagging is a process of defining the suitable part of speech for each word in the given context such as defining if a word is a verb, a noun or a particle. POS tagging is an important preprocessing step in many Natural Language Processing (NLP) applications such as question answering, text summarization, and information retrieval. Objective: The performance of NLP applications depends on the accuracy of POS taggers since assigning right tags for the words in a sentence enables the application to work properly after tagging. Many approaches have been proposed fo

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Anwar, Agus Sofiyan, Zainal Abidin, and Ririen Kusumawati. "MESIN PENCARI AYAT AL QURAN MENGGUNAKAN INEXACT STRING MATCHING." MATICS, March 21, 2012. http://dx.doi.org/10.18860/mat.v0i0.1569.

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<p>Dengan adanya teknologi digital, al Quran yang dahulu berupa teks manual sekarang sudah dapat dijumpai versi digitalnya. Hal tersebut memicu pengembangan perangkat lunak yang membantu mendapatkan informasi dari teks al Quran, seperti: pencarian ayat berdasarkan kata, frase maupun tema, terjemahan al Quran, tafsir al Quran. Dalam kaitannya dengan pencarian ayat berdasarkan kata atau frase, pada umumnya perangkat lunak yang ada menggunakan teknik exact string matching, yaitu teknik pencarian ayat yang sesuai dengan kata inputan secara tepat. Teknik tersebut sangat sesuai jika pemakai pe

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Fati, Suliman Mohamed. "Detecting Cyberbullying across Social Media Platforms in Saudi Arabia Using Sentiment Analysis: A Case Study." Computer Journal, April 19, 2021. http://dx.doi.org/10.1093/comjnl/bxab019.

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Abstract Twitter has become an open space for the users’ interactions and discussions on diverse trending topics. One of the issues raised on social media platforms is the misunderstanding of ‘freedom of speech’, which in turn, leads us to a new social and behavioral attack: cyberbullying. Cyberbully affects both individuals and societies. Despite tough sanctions globally and locally, cyberbullying is still a serious issue, which needs further consideration. Thus, this research aims to address this issue by proposing a framework, based on sentiment analysis, to detect cyberbullying in the twee

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