Skip to main content
SpringerLink
Log in

Arabic sentiment analysis: studies, resources, and tools

  • Review Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

To determine whether a document or a sentence expresses a positive or negative sentiment, three main approaches are commonly used: the lexicon-based approach, corpus-based approach, and a hybrid approach. The study of sentiment analysis in English has the highest number of sentiment analysis studies, while research is more limited for other languages, including Arabic and its dialects. Lexicon based approaches need annotated sentiment lexicons (containing the valence and intensity of its terms and expressions). Corpus-based sentiment analysis requires annotated sentences. One of the significant problems related to the treatment of Arabic and its dialects is the lack of these resources. We present in this survey the most recent resources and advances that have been done for Arabic sentiment analysis. This survey presents recent work (where the majority of these works are between 2015 and 2019). These works are classified by category (survey work or contribution work). For contribution work, we focus on the construction of sentiment lexicon and corpus. We also describe emergent trends related to Arabic sentiment analysis, principally associated with the use of deep learning techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. POS: Part Of Speech.

  2. The Quran is a scripture which, according to Muslims, is the verbatim words of Allah. It contains over 77,000 words revealed through Archangel Gabriel to Prophet Muhammad over 23 years beginning in 610 CE. It is divided into 114 chapters of varying sizes, where each section is divided into verses, adding up to a total of 6243 verses (Sharaf and Atwell 2012b).

  3. https://mawdoo3.com/.

  4. https://glosbe.com/en/arq/excellent.

  5. chat.mymaktoob.com.

  6. https://rapidminer.com/.

References

  • Abdul-Mageed M, Diab M (2012a) Toward building a large-scale Arabic sentiment lexicon. In: Proceedings of the 6th international global WordNet conference, pp 18–22

  • Abdul-Mageed M, Diab MT (2012b) Awatif: a multi-genre corpus for modern standard Arabic subjectivity and sentiment analysis. In: LREC. Citeseer, pp 3907–3914

  • Abdul-Mageed M, Diab M, Kübler S (2014) Samar: subjectivity and sentiment analysis for Arabic social media. Comput Speech Lang 28(1):20–37

    Article  Google Scholar 

  • Abdul-Mageed M, Diab MT (2016) Sana: a large scale multi-genre, multi-dialect lexicon for Arabic subjectivity and sentiment analysis. In: LREC, pp 1162–1169

  • Abdulla NA, Ahmed NA, Shehab MA, Al-Ayyoub M (2013) Arabic sentiment analysis: lexicon-based and corpus-based. In: 2013 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT). IEEE, pp. 1–6

  • Abdulla N, Mohammed S, Al-Ayyoub M, Al-Kabi M et al (2014a) Automatic lexicon construction for Arabic sentiment analysis. In: 2014 international conference on future internet of things and cloud (FiCloud). IEEE, pp 547–552

  • Abdulla NA, Ahmed NA, Shehab MA, Al-Ayyoub M, Al-Kabi MN, Al-rifai S (2014b) Towards improving the lexicon-based approach for Arabic sentiment analysis. Int J Inf Technol Web Eng (IJITWE) 9(3):55–71

    Article  Google Scholar 

  • Akaichi J (2013) Social networks’ facebook’ statutes updates mining for sentiment classification. In: 2013 international conference on social computing (SocialCom). IEEE, pp 886–891

  • Al-Ayyoub M, Khamaiseh AA, Jararweh Y, Al-Kabi MN (2018) A comprehensive survey of Arabic sentiment analysis. In: Information processing & management, pp 320–342

  • Al-Azani S, El-Alfy ESM (2017) Using word embedding and ensemble learning for highly imbalanced data sentiment analysis in short Arabic text. Procedia Comput Sci 109:359–366

    Article  Google Scholar 

  • Al-Sallab A, Baly R, Hajj H, Shaban KB, El-Hajj W, Badaro G (2017) Aroma: a recursive deep learning model for opinion mining in Arabic as a low resource language. ACM Trans Asian Low Resour Lang Inf Process (TALLIP) 16(4):25

    Google Scholar 

  • Al Shboul B, Al-Ayyoub M, Jararweh Y (2015) Multi-way sentiment classification of Arabic reviews. In: 2015 6th international conference on information and communication systems (ICICS). IEEE, pp 206–211

  • Al-Twairesh N, Al-Khalifa H, Al-Salman A, Al-Ohali Y (2017) Arasenti-tweet: a corpus for Arabic sentiment analysis of Saudi tweets. Procedia Comput Sci 117:63–72

    Article  Google Scholar 

  • Alahmary RM, Al-Dossari HZ, Emam AZ (2019) Sentiment analysis of Saudi dialect using deep learning techniques. In: 2019 international conference on electronics, information, and communication (ICEIC). IEEE, pp 1–6

  • Alayba AM, Palade V, England M, Iqbal R (2018a) A combined CNN and LSTM model for Arabic sentiment analysis. In: International cross-domain conference for machine learning and knowledge extraction. Springer, pp 179–191

  • Alayba AM, Palade V, England M, Iqbal R (2018b) Improving sentiment analysis in Arabic using word representation. In: 2018 IEEE 2nd international workshop on Arabic and derived script analysis and recognition (ASAR). IEEE, pp 13–18

  • Alharbi FR, Khan MB (2019) Identifying comparative opinions in Arabic text in social media using machine learning techniques. SN Appl Sci 1(3):213

    Article  Google Scholar 

  • Alhumoud SO, Altuwaijri MI, Albuhairi TM, Alohaideb WM (2015) Survey on Arabic sentiment analysis in Twitter. Int Sci Index 9(1):364–368

    Google Scholar 

  • Alowaidi S, Saleh M, Abulnaja O (2017) Semantic sentiment analysis of Arabic texts. Int J Adv Comput Sci Appl 8(2):256–262

    Google Scholar 

  • Altowayan AA, Tao L (2016) Word embeddings for Arabic sentiment analysis. In: 2016 IEEE international conference on big data (big data). IEEE, pp 3820–3825

  • Altrabsheh N, El-Masri M, Mansour H (2017) Combining sentiment lexicons of Arabic terms. In: Association for information and systems (AMICS)

  • Aly M, Atiya A (2013) Labr: a large scale Arabic book reviews dataset. In: Proceedings of the 51st annual meeting of the association for computational linguistics (volume 2: short papers), vol 2, pp 494–498

  • Antai R (2014) Sentiment classification using summaries: a comparative investigation of lexical and statistical approaches. In: 2014 6th computer science and electronic engineering conference (CEEC). IEEE, pp 154–159

  • Assiri A, Emam A, Aldossari H (2015) Arabic sentiment analysis: a survey. Int J Adv Comput Sci Appl 6(12):75–85

    Google Scholar 

  • Atia S, Shaalan K (2015) Increasing the accuracy of opinion mining in Arabic. In: 2015 first international conference on Arabic computational linguistics (ACLing). IEEE, pp 106–113

  • Attia M, Samih Y, El-Kahky A, Kallmeyer L (2018) Multilingual multi-class sentiment classification using convolutional neural networks. In: Proceedings of the eleventh international conference on language resouces and evaluation (LREC), pp 635–640

  • Badaro G, Baly R, Hajj H, Habash N, El-Hajj W (2014) A large scale Arabic sentiment lexicon for Arabic opinion mining. In: Proceedings of the EMNLP 2014 workshop on Arabic natural language processing (ANLP), pp 165–173

  • Badaro G, Baly R, Akel R, Fayad L, Khairallah J, Hajj H, Shaban K, El-Hajj W (2015) A light lexicon-based mobile application for sentiment mining of Arabic tweets. In: Proceedings of the second workshop on Arabic natural language processing, pp 18–25

  • Bahrainian SA, Dengel A (2013) Sentiment analysis using sentiment features. In: 2013 IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT), vol 3. IEEE, pp 26–29

  • Barhoumi A, Aloulou YEC, Belguith LH (2017) Document embeddings for Arabic sentiment analysis. In: Conference on language processing and knowledge management (LPKM)

  • Bhuta S, Doshi A, Doshi U, Narvekar M (2014) A review of techniques for sentiment analysis of Twitter data. In: 2014 international conference on issues and challenges in intelligent computing techniques (ICICT). IEEE, pp 583–591

  • Bies A, Song Z, Maamouri M, Grimes S, Lee H, Wright J, Strassel S, Habash N, Eskander R, Rambow O (2014) Transliteration of Arabizi into Arabic orthography: developing a parallel annotated Arabizi–Arabic script sms/chat corpus. In: Proceedings of the EMNLP 2014 workshop on Arabic natural language processing (ANLP), pp 93–103

  • Biltawi M, Etaiwi W, Tedmori S, Hudaib A, Awajan A (2016) Sentiment classification techniques for Arabic language: a survey. In: 2016 7th international conference on information and communication systems (ICICS). IEEE, pp 339–346

  • Boudad N, Faizi R, Thami ROH, Chiheb R (2017) Sentiment analysis in Arabic: a review of the literature. Ain Shams Eng J 9(4):2479–2490

    Article  Google Scholar 

  • Buckwalter T (2004) Buckwalter Arabic morphological analyzer version 2.0. linguistic data consortium, University of Pennsylvania, 2002. ldc cat alog no.: Ldc2004l02. Tech. rep. ISBN 1-58563-324-0

  • Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

  • Colace F, De Santo M, Greco L (2013) A probabilistic approach to tweets’ sentiment classification. In: 2013 Humaine association conference on affective computing and intelligent interaction (ACII). IEEE, pp 37–42

  • Cotterell R, Renduchintala A, Saphra N, Callison-Burch C (2014) An Algerian Arabic–French code-switched corpus. In: Workshop on free/open-source Arabic corpora and corpora processing tools workshop programme, p 34

  • Dahou A, Xiong S, Zhou J, Haddoud MH, Duan P (2016) Word embeddings and convolutional neural network for Arabic sentiment classification. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, pp 2418–2427

  • Darwish K (2013) Arabizi detection and conversion to Arabic. arXiv preprint arXiv:1306.6755

  • Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 international conference on web search and data mining. ACM, pp 231–240

  • Duwairi RM, Alfaqeh M, Wardat M, Alrabadi A (2016) Sentiment analysis for Arabizi text. In: 2016 7th international conference on information and communication systems (ICICS). IEEE, pp 127–132

  • El-Beltagy SR (2016a) Niletmrg at semeval-2016 task 7: deriving prior polarities for Arabic sentiment terms. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 486–490

  • El-Beltagy SR (2016b) Nileulex: a phrase and word level sentiment lexicon for Egyptian and modern standard Arabic. In: Proceedings of tenth international conference on language resouces and evaluation (LREC), pp 2900–2905

  • El-Beltagy SR (2017) Weightednileulex: a scored Arabic sentiment lexicon for improved sentiment analysis. language processing, pattern recognition and intelligent systems. Special issue on computational linguistics, speech & image processing for Arabic language. World Scientific Publishing Co, Singapore

    Google Scholar 

  • El-Beltagy SR, Ali A (2013) Open issues in the sentiment analysis of Arabic social media: a case study. In: 2013 9th international conference on innovations in information technology (IIT). IEEE, pp 215–220

  • El-Kilany A, Azzam A, El-Beltagy SR (2018) Using deep neural networks for extracting sentiment targets in Arabic tweets. In: Intelligent natural language processing: trends and applications. Springer, pp 3–15

  • El Mahdaouy A, Gaussier E, El Alaoui SO (2016) Arabic text classification based on word and document embeddings. In: International conference on advanced intelligent systems and informatics. Springer, pp 32–41

  • El-Masri M, Altrabsheh N, Mansour H (2017) Successes and challenges of Arabic sentiment analysis research: a literature review. Soc Netw Anal Min 7(1):54

    Article  Google Scholar 

  • ElSahar H, El-Beltagy SR (2014) A fully automated approach for Arabic slang lexicon extraction from microblogs. In: International conference on intelligent text processing and computational linguistics. Springer, pp 79–91

  • ElSahar H, El-Beltagy SR (2015) Building large Arabic multi-domain resources for sentiment analysis. In: International conference on intelligent text processing and computational linguistics. Springer, pp 23–34

  • Elshakankery K, Ahmed MF (2019) Hilatsa: a hybrid incremental learning approach for Arabic tweets sentiment analysis. Egypt Inform J. https://doi.org/10.1016/j.eij.2019.03.002

  • Eskander R, Rambow O (2015) Slsa: a sentiment lexicon for standard Arabic. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 2545–2550

  • Esuli A, Sebastiani F (2007) Sentiwordnet: a high-coverage lexical resource for opinion mining. Evaluation 17:1–26

    Google Scholar 

  • Farghaly A, Shaalan K (2009) Arabic natural language processing: challenges and solutions. ACM Trans Asian Lang Inf Process (TALIP) 8(4):14

    Google Scholar 

  • Farra N, McKeown K (2017) Smarties: Sentiment models for Arabic target entities. arXiv preprint arXiv:1701.03434

  • Fellbaum C, Alkhalifa M, Black W, Elkateb S, Pease A, Rodriguez H, Vossen P (2006) Introducing the Arabic wordnet project

  • Frank E, Hall M, Holmes G, Kirkby R, Pfahringer B, Witten IH, Trigg L (2009) Weka-a machine learning workbench for data mining. In: Data mining and knowledge discovery handbook. Springer, Boston, pp 1269–1277

  • Gamal D, Alfonse M, El-Horbaty ESM, Salem ABM (2019) Twitter benchmark dataset for Arabic sentiment analysis. Int J Mod Educ Comput Sci 11(1):33

    Article  Google Scholar 

  • Gilbert B, Hussein J, Hazem H, Wassim EH, Nizar H (2018) Arsel: a large scale Arabic sentiment and emotion lexicon. In: Proceedings of the eleventh international conference on language resouces and evaluation (LREC)

  • Graff D, Buckwalter T, Jin H, Maamouri M (2006) Lexicon development for varieties of spoken colloquial Arabic. In: Proceedings of the 5th edition of the international conference on language resouces and evaluation (LREC), pp 999–1004

  • Graves A, Fernández S, Gomez F, Schmidhuber J (2006) Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd international conference on machine learning. ACM, pp 369–376

  • Guellil I, Boukhalfa K (2015) Social big data mining: a survey focused on opinion mining and sentiments analysis. In: 2015 12th international symposium on programming and systems (ISPS). IEEE, pp 1–10

  • Guellil I, Azouaou F, Abbas M (2017a) Comparison between neural and statistical translation after transliteration of Algerian Arabic dialect. In: WiNLP: women & underrepresented minorities in natural language processing (co-located withACL 2017)

  • Guellil I, Azouaou F, Abbas M, Fatiha S (2017b) Arabizi transliteration of Algerian Arabic dialect into modern standard Arabic. In: Social MT 2017: first workshop on social media and user generated content machine translation (co-located with EAMT 2017)

  • Guellil I, Azouaou F, Saâdane H, Semmar N (2017c) Une approche fondée sur les lexiques d’analyse de sentiments du dialecte algérien

  • Guellil I, Adeel A, Azouaou F, Benali F, Hachani AE, Hussain A (2018a) Arabizi sentiment analysis based on transliteration and automatic corpus annotation. In: Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media Analysis, pp 335–341

  • Guellil I, Adeel A, Azouaou F, Hussain A (2018b) Sentialg: Automated corpus annotation for Algerian sentiment analysis. arXiv preprint arXiv:1808.05079

  • Guellil I, Azouaou F, Benali F, Hachani AE, Saadane H (2018c) Approche hybride pour la translitération de l’arabizi algérien : une étude préliminaire. In: Conference: 25e conférence sur le Traitement Automatique des Langues Naturelles (TALN), Rennes, France, 14–18 May 2018, pp 509–517. https://www.researchgate.net/publication/326354578_Approche_Hybride_pour_la_transliteration_de_l%27arabizi_algerien_une_etude_preliminaire

  • Guellil I, Saâdane H, Azouaou F, Gueni B, Nouvel D (2019) Arabic natural language processing: an overview. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.02.006

  • Habash NY (2010) Introduction to Arabic natural language processing. Synth Lect Hum Lang Technol 3(1):1–187

    Article  Google Scholar 

  • Harrag F (2014) Estimating the sentiment of Arabic social media contents: a survey. In: 5th international conference on Arabic language processing

  • Harrat S, Meftouh K, Smaïli K (2017) Machine translation for Arabic dialects (survey). Inf Process Manag 56(2):262–273

    Article  Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  • Htait A, Fournier S, Bellot P (2017) Lsis at semeval-2017 task 4: using adapted sentiment similarity seed words for English and Arabic tweet polarity classification. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017), pp 718–722

  • Joulin A, Grave E, Bojanowski P, Mikolov T (2016) Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759

  • Karamibekr M, Ghorbani AA (2012) Sentiment analysis of social issues. In: 2012 international conference on social informatics (SocialInformatics). IEEE, pp 215–221

  • Kaseb GS, Ahmed MF (2016) Arabic sentiment analysis approaches: an analytical survey. Int J Sci Eng Res 7(10):712–723

    Google Scholar 

  • Khan AZ, Atique M, Thakare V (2015) Combining lexicon-based and learning-based methods for Twitter sentiment analysis. Int J Electron Commun Soft Comput Sci Eng (IJECSCSE) 89:89–91

    Google Scholar 

  • Khoja S, Garside R (1999) Stemming Arabic text. Lancaster, UK, Computing Department, Lancaster University

  • Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882

  • Korayem M, Crandall D, Abdul-Mageed M (2012) Subjectivity and sentiment analysis of Arabic: a survey. In: International conference on advanced machine learning technologies and applications. Springer, pp 128–139

  • Kumar A, Kohail S, Kumar A, Ekbal A, Biemann C (2016) Iit-tuda at semeval-2016 task 5: beyond sentiment lexicon: combining domain dependency and distributional semantics features for aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 1129–1135

  • Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188–1196

  • LeCun Y, Bengio Y et al (1995) Convolutional networks for images, speech, and time series. In: The handbook of brain theory and neural networks, vol 3361

  • Liang PW, Dai BR (2013) Opinion mining on social media data. In: 2013 IEEE 14th international conference on mobile data management (MDM), vol 2. IEEE, pp 91–96

  • Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167

    Article  Google Scholar 

  • Maamouri M, Bies A, Buckwalter T, Mekki W (2004) The Penn Arabic Treebank: building a large-scale annotated Arabic corpus. In: NEMLAR conference on Arabic language resources and tools, vol 27. Cairo, pp 466–467

  • Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies-volume 1. Association for Computational Linguistics, pp 142–150

  • Mahyoub FH, Siddiqui MA, Dahab MY (2014) Building an Arabic sentiment lexicon using semi-supervised learning. J King Saud Univ Comput Inf Sci 26(4):417–424

    Google Scholar 

  • Mataoui M, Zelmati O, Boumechache M (2016) A proposed lexicon-based sentiment analysis approach for the vernacular Algerian Arabic. Res Comput Sci 110:55–70

    Google Scholar 

  • Medhaffar S, Bougares F, Esteve Y, Hadrich-Belguith L (2017) Sentiment analysis of Tunisian dialects: Linguistic resources and experiments. In: Proceedings of the third Arabic natural language processing workshop, pp 55–61

  • Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113

    Article  Google Scholar 

  • Meftouh K, Harrat S, Jamoussi S, Abbas M, Smaili K (2015) Machine translation experiments on padic: a parallel Arabic dialect corpus. In: The 29th Pacific Asia conference on language, information and computation

  • Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

  • Mohammad SM, Kiritchenko S, Zhu X (2013) NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:1308.6242

  • Mohammad S, Salameh M, Kiritchenko S (2016a) Sentiment lexicons for Arabic social media. In: Proceedings of tenth international conference on language resouces and evaluation (LREC), pp 33–37

  • Mohammad SM, Salameh M, Kiritchenko S (2016b) How translation alters sentiment. J Artif Intell Res 55:95–130

    Article  MathSciNet  Google Scholar 

  • Mourad A, Darwish K (2013) Subjectivity and sentiment analysis of modern standard Arabic and Arabic microblogs. In: Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 55–64

  • Mulki H, Haddad H, Gridach M, Babaoglu I (2019) Empirical evaluation of leveraging named entities for Arabic sentiment analysis. arXiv preprint arXiv:1904.10195

  • Nabil M, Aly M, Atiya A (2015) Astd: Arabic sentiment tweets dataset. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 2515–2519

  • Pasha A, Al-Badrashiny M, Diab MT, El Kholy A, Eskander R, Habash N, Pooleery M, Rambow O, Roth R (2014) Madamira: a fast, comprehensive tool for morphological analysis and disambiguation of Arabic. LREC 14:1094–1101

    Google Scholar 

  • Rahab H, Zitouni A, Djoudi M (2017) Siaac: sentiment polarity identification on Arabic Algerian newspaper comments. In: Proceedings of the computational methods in systems and software. Springer, pp 139–149

  • Rahab H, Zitouni A, Djoudi M (2019) Sana: sentiment analysis on newspapers comments in Algeria. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.04.012

  • Rushdi-Saleh M, Martín-Valdivia MT, Ureña-López LA, Perea-Ortega JM (2011) Bilingual experiments with an Arabic–English corpus for opinion mining. In: Proceedings of the international conference recent advances in natural language processing 2011, pp 740–745

  • Rushdi-Saleh M, Martín-Valdivia MT, Ureña-López LA, Perea-Ortega JM (2011) Oca: opinion corpus for Arabic. J Assoc Inf Sci Technol 62(10):2045–2054

    Article  Google Scholar 

  • Salameh M, Mohammad S, Kiritchenko S (2015) Sentiment after translation: a case-study on Arabic social media posts. In: Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 767–777

  • Salloum SA, AlHamad AQ, Al-Emran M, Shaalan K (2018) A survey of Arabic text mining. In: Intelligent natural language processing: trends and applications. Springer, pp 417–431

  • Schmitt M, Steinheber S, Schreiber K, Roth B (2018) Joint aspect and polarity classification for aspect-based sentiment analysis with end-to-end neural networks. arXiv preprint arXiv:1808.09238

  • Sharaf ABM, Atwell E (2012a) Qurana: corpus of the Quran annotated with pronominal anaphora. In: Proceedings of eighth international conference on language resouces and evaluation (LREC), pp 130–137

  • Sharaf ABM, Atwell E (2012b) Qursim: A corpus for evaluation of relatedness in short texts. In: LREC, pp 2295–2302

  • Shoufan A, Alameri S (2015) Natural language processing for dialectical Arabic: a survey. In: Proceedings of the second workshop on Arabic natural language processing, pp 36–48

  • Singh V, Piryani R, Uddin A, Waila P (2013) Sentiment analysis of movie reviews: a new feature-based heuristic for aspect-level sentiment classification. In: 2013 international multi-conference on automation, computing, communication, control and compressed sensing (iMac4s). IEEE, pp 712–717

  • Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307

    Article  Google Scholar 

  • Tafreshi S, Diab M (2018) Emotion detection and classification in a multigenre corpus with joint multi-task deep learning. In: Proceedings of the 27th international conference on computational linguistics, pp 2905–2913

  • Tellez ES, Miranda-Jiménez S, Graff M, Moctezuma D, Suárez RR, Siordia OS (2017) A simple approach to multilingual polarity classification in Twitter. Pattern Recognit Lett 94:68–74

    Article  Google Scholar 

  • Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol 61(12):2544–2558

    Article  Google Scholar 

  • Touahri I, Mazroui A (2019) Studying the effect of characteristic vector alteration on Arabic sentiment classification. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.04.011

  • Vo DT, Zhang Y (2016) Don’t count, predict! an automatic approach to learning sentiment lexicons for short text. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 2: short papers), vol 2, pp 219–224

  • Wan X (2012) A comparative study of cross-lingual sentiment classification. In: Proceedings of the The 2012 IEEE/WIC/ACM international joint conferences on web intelligence and intelligent agent technology, vol 01. IEEE Computer Society, pp 24–31

  • Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: a survey. WIREs Data Min Knowl Discov 8(4):e1253. https://doi.org/10.1002/widm.1253

    Article  Google Scholar 

  • Zia SS, Fatima S, Mala I, Sadiq Ali Khan M, Naseem M, Das B (2018) A survey on sentiment analysis, classification and applications. Int J Pure Appl Math 119(10):1203–1211

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratoire des Méthodes de Conception des Systèmes, Ecole nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Alger, Algeria

    Imane Guellil & Faical Azouaou

  2. Department of Informatics, Universidad Técnica Federico Santa María, Av. Vicña Mackenna 3939, Santiago, Chile

    Marcelo Mendoza

Authors
  1. Imane Guellil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Faical Azouaou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcelo Mendoza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Imane Guellil.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guellil, I., Azouaou, F. & Mendoza, M. Arabic sentiment analysis: studies, resources, and tools. Soc. Netw. Anal. Min. 9, 56 (2019). https://doi.org/10.1007/s13278-019-0602-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-019-0602-x

Keywords

Search

Navigation