{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T22:31:04Z","timestamp":1781217064534,"version":"3.54.1"},"reference-count":73,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2019,5,21]],"date-time":"2019-05-21T00:00:00Z","timestamp":1558396800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2020,8]]},"abstract":"<jats:p>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 challenges, we decided to provide the research community and Arabic users with a new efficient framework for Arabic Sentiment Analysis (ASA). Our primary goal is to improve the performance of ASA by exploiting deep learning while varying the preprocessing techniques. For that, we implement and evaluate two deep learning models namely convolutional neural network (CNN) and long short-term memory (LSTM) models. The framework offers various preprocessing techniques for ASA (including stemming, normalisation, tokenization and stop words). As a result of this work, we first provide a new rich and publicly available Arabic corpus called Moroccan Sentiment Analysis Corpus (MSAC). Second, the proposed framework demonstrates improvement in ASA. In fact, the experimental results prove that deep learning models have a better performance for ASA than classical approaches (support vector machines, naive Bayes classifiers and maximum entropy). They also show the key role of morphological features in Arabic Natural Language Processing (NLP).<\/jats:p>","DOI":"10.1177\/0165551519849516","type":"journal-article","created":{"date-parts":[[2019,5,21]],"date-time":"2019-05-21T09:22:10Z","timestamp":1558430530000},"page":"544-559","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":83,"title":["ASA: A framework for Arabic sentiment analysis"],"prefix":"10.1177","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5780-2691","authenticated-orcid":false,"given":"Ahmed","family":"Oussous","sequence":"first","affiliation":[{"name":"National School of Applied Sciences (ENSA), Ibn Tofail University, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatima-Zahra","family":"Benjelloun","sequence":"additional","affiliation":[{"name":"National School of Applied Sciences (ENSA), Ibn Tofail University, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayoub Ait","family":"Lahcen","sequence":"additional","affiliation":[{"name":"National School of Applied Sciences (ENSA), Ibn Tofail University, Morocco; LRIT, Unit\u00e9 associ\u00e9e au CNRST (URAC 29), Mohammed V-Agdal University, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samir","family":"Belfkih","sequence":"additional","affiliation":[{"name":"National School of Applied Sciences (ENSA), Ibn Tofail University, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,5,21]]},"reference":[{"key":"bibr1-0165551519849516","first-page":"431","volume":"30","author":"Oussous A","year":"2018","journal-title":"J King Saud Univ-Comp Inform Sci"},{"key":"bibr2-0165551519849516","doi-asserted-by":"publisher","DOI":"10.3233\/WEB-160345"},{"key":"bibr3-0165551519849516","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-151574"},{"key":"bibr4-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2017.04.007"},{"key":"bibr5-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-96292-4_8"},{"key":"bibr6-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1177\/0165551516683908"},{"key":"bibr7-0165551519849516","first-page":"27","volume-title":"Proceedings of the first ACM conference on Online social networks","author":"Gon\u00e7alves P"},{"issue":"7","key":"bibr8-0165551519849516","first-page":"26","volume":"126","author":"Godsay M.","year":"2015","journal-title":"Int J Comp Appl"},{"issue":"3","key":"bibr9-0165551519849516","first-page":"29","volume":"158","author":"Mostafa AM.","year":"2017","journal-title":"Int J Comp Appl"},{"key":"bibr10-0165551519849516","first-page":"579","volume-title":"International conference on future internet of things and cloud (FiCloud)","author":"Duwairi RM"},{"key":"bibr11-0165551519849516","first-page":"19","volume-title":"Proceedings of the 3rd workshop on computational approaches to subjectivity and sentiment analysis (WASSA)","author":"Abdul-Mageed M"},{"key":"bibr12-0165551519849516","first-page":"166","volume-title":"6th international conference on information and communication systems (ICICS)","author":"Duwairi RM"},{"key":"bibr13-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-32467-8_13"},{"key":"bibr14-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1177\/0165551514534143"},{"key":"bibr15-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-60042-0_66"},{"key":"bibr16-0165551519849516","first-page":"139","volume-title":"Proceedings of the Computational Methods in Systems and Software Applied Computational (CoMeSySo 2017) Intelligence and Mathematical Methods","author":"Rahab H","year":"2017"},{"key":"bibr17-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1002\/asi.21598"},{"key":"bibr18-0165551519849516","volume-title":"Stemming Arabic text","author":"Khoja S","year":"1999"},{"key":"bibr19-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-017-2770-1"},{"key":"bibr20-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1002\/asi.21301"},{"key":"bibr21-0165551519849516","volume-title":"Proceedings of the 5th international business information management association conference","author":"Ghawanmeh S"},{"key":"bibr22-0165551519849516","doi-asserted-by":"publisher","DOI":"10.3923\/itj.2005.38.43"},{"key":"bibr23-0165551519849516","first-page":"1","volume-title":"3rd International Conference on information and communication technologies: from theory to applications (ICTTA)","author":"Sonbol R"},{"key":"bibr24-0165551519849516","first-page":"152","volume-title":"International conference on information technology: coding and computing (ITCC\u201905)","author":"Taghva K"},{"key":"bibr25-0165551519849516","volume-title":"International Arab conference on information technology","author":"Al-Shalabi R"},{"key":"bibr26-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-45111-9_44"},{"key":"bibr27-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1177\/0165551515625030"},{"key":"bibr28-0165551519849516","first-page":"3111","volume-title":"Proceedings of the 26th International Conference on Neural Information Processing Systems","volume":"2","author":"Mikolov T"},{"key":"bibr29-0165551519849516","doi-asserted-by":"publisher","DOI":"10.1111\/lnc3.12228"},{"key":"bibr30-0165551519849516","first-page":"151","volume-title":"Proceedings of the conference on empirical methods in natural language processing","author":"Socher R"},{"key":"bibr31-0165551519849516","first-page":"1631","volume-title":"Proceedings of the conference on empirical methods in natural language processing","author":"Socher R"},{"key":"bibr32-0165551519849516","doi-asserted-by":"crossref","unstructured":"Tai KS, Socher R, Manning CD. 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