Arabic Sentiment Analysis Resources: A Survey

  • Areeb alOwisheqEmail author
  • Sarah alHumoud
  • Nora alTwairesh
  • Tarfa alBuhairi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9742)


Research interest in Arabic sentiment analysis (ASA) is rapidly increasing, therefore it is important to compile, document and analyze efforts in this area to facilitate further development. These ASA efforts aim to create tools that can sift through and gain meaningful knowledge from the unending data explosion. ASA approaches have continued to evolve despite lack in Arabic linguistic resources. In this paper we conduct a comprehensive and up-to-date review of recent resources for ASA.


Social networks Sentiment analysis Arabic Lexicon Corpus 


  1. 1.
    Al-Twairesh, N., Al-Khalifa, H., Al-Salman, A.-M.: Subjectivity and sentiment analysis of Arabic: trends and challenges. In: 11th International Conference on Computer Systems and Applications (AICCSA), IEEE/ACS, 2014, pp. 148–155 (2014)Google Scholar
  2. 2.
    Alhazmi, S., Black, W., McNaught, J.: Arabic SentiWordNet in relation to SentiWordNet 3.0. 2180 1266(4), 1 (2013)Google Scholar
  3. 3.
    Badaro, G., Baly, R., Hajj, H., Habash, N., El-Hajj, W.: A large scale Arabic sentiment lexicon for Arabic opinion mining. In: ANLP 2014, p. 165 (2014)Google Scholar
  4. 4.
    Abdul-Mageed, M., Diab, M.: Toward building a large-scale Arabic sentiment lexicon. In: Proceedings of the 6th International Global WordNet Conference, pp. 18–22 (2012)Google Scholar
  5. 5.
    Eskander, R., Rambow, O.: SLSA: A sentiment lexicon for standard Arabic presented at the empirical methods in natural language processing, Lisbon, Portugal (2015)Google Scholar
  6. 6.
    Al-Rowaily, K., Abulaish, M., Haldar, N.A.-H., Al-Rubaian, M.: BiSAL–a bilingual sentiment analysis lexicon to analyze dark web forums for cyber security. Digit. Investig. 14, 53–62 (2015)CrossRefGoogle Scholar
  7. 7.
    Mahyoub, F.H., Siddiqui, M.A., Dahab, M.Y.: Building an Arabic sentiment lexicon using semi-supervised learning. J. King Saud Univ.-Comput. Inf. Sci. 26(4), 417–424 (2014)Google Scholar
  8. 8.
    Valitutti, A., Strapparava, C., Stock, O.: Developing affective lexical resources. PsychNology J. 2(1), 61–83 (2004)Google Scholar
  9. 9.
    Ibrahim, H.S., Abdou, S.M., Gheith, M.: Idioms-proverbs lexicon for modern standard Arabic and colloquial sentiment analysis. Int. J. Comput. Appl. 118(11), 26–31 (2015)Google Scholar
  10. 10.
    Attia, M., Rashwan, M., Ragheb, A., Al-Badrashiny, M., Al-Basoumy, H., Abdou, S.: A compact Arabic lexical semantics language resource based on the theory of semantic fields. In: Nordström, B., Ranta, A. (eds.) GoTAL 2008. LNCS (LNAI), vol. 5221, pp. 65–76. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Mobarz, H., Rashown, M., Farag, I.: Using automated lexical resources in Arabic sentence subjectivity. Int. J. Artif. Intell. Appl. 5(6), 1 (2014)Google Scholar
  12. 12.
    Duwairi, R.M.: Sentiment analysis for dialectical Arabic. In: 2015 6th International Conference on Information and Communication Systems (ICICS), pp. 166–170 (2015)Google Scholar
  13. 13.
    Bayoudhi, A., Belguith, L.H., Ghorbel, H.: Sentiment classification of Arabic documents: experiments with multi-type features and ensemble algorithms (2015)Google Scholar
  14. 14.
    Al-Ayyoub, M., Essa, S.B., Alsmadi, I.: Lexicon-based sentiment analysis of Arabic tweets. Int. J. Soc. Netw. Min. 2(2), 101–114 (2015)CrossRefGoogle Scholar
  15. 15.
    Abuaiadh, D.: Dataset for Arabic Document Classification (2011).
  16. 16.
    Abdulla, N., Majdalawi, R., Mohammed, S., Al-Ayyoub, M., Al-Kabi, M.: Automatic lexicon construction for Arabic sentiment analysis. In: 2014 International Conference on Future Internet of Things and Cloud (FiCloud), pp. 547–552 (2014)Google Scholar
  17. 17.
    Abdulla, N.A., Ahmed, N.A., Shehab, M.A., Al-Ayyoub, M., Al-Kabi, M.N., Al-rifai, S.: Towards improving the lexicon-based approach for Arabic sentiment analysis. Int. J. Inf. Technol. Web Eng. IJITWE 9(3), 55–71 (2014)CrossRefGoogle Scholar
  18. 18.
    ElSahar, H., El-Beltagy, S.R.: Building large Arabic multi-domain resources for sentiment analysis. In: Gelbukh, A. (ed.) Computational Linguistics and Intelligent Text Processing. LNCS, vol. 9042, pp. 23–34. Springer, Heidelberg (2015)Google Scholar
  19. 19.
    Ibrahim, H.S., Abdou, S.M., Gheith, M.: Sentiment analysis for modern standard Arabic and colloquial. Int. J. Nat. Lang. Comput. 4(2) (2015)Google Scholar
  20. 20.
    El-Makky, N., Nagi, K., El-Ebshihy, A., Apady, E., Hafez, O., Mostafa, S., Ibrahim, S.: Sentiment Analysis of Colloquial Arabic Tweets (2015)Google Scholar
  21. 21.
    ALTEC, Arabic MPQA Subjective Lexicon and Arabic Opinion Holder Corpus, Arabic Langauge Technology Center (2011).
  22. 22.
    Abdul-Mageed, M., Diab, M.T.: Subjectivity and sentiment annotation of modern standard Arabic newswire. In: Proceedings of the 5th Linguistic Annotation Workshop, pp. 110–118 (2011)Google Scholar
  23. 23.
    El-Beltagy, S.R., Ali, A.: Open issues in the sentiment analysis of Arabic social media: a case study. In: 2013 9th International Conference on Innovations in Information Technology (IIT), pp. 215–220 (2013)Google Scholar
  24. 24.
    Alhumoud, S., Albuhairi, T., Alohaideb, W.: Hybrid sentiment analyser for Arabic tweets using R. In: Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3 K 2015), vol. 1, Lisbon, Purtogal (2015)Google Scholar
  25. 25.
    Alhumoud, S., Albuhairi, T., Altuwaijri, M.: Arabic sentiment analysis using WEKA a hybrid learning approach. In: Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015), vol. 1, Lisbon, Purtogal (2015)Google Scholar
  26. 26.
    Aldayel, H.K., Azmi, A.M.: Arabic tweets sentiment analysis–a hybrid scheme. J. Inf. Sci. (2015)Google Scholar
  27. 27.
    Duwairi, R., Ahmed, N.A., Al-Rifai, S.Y.: Detecting sentiment embedded in Arabic social media–a lexicon-based approach. J. Intell. Fuzzy Syst. 29(1), 107–117 (2015)CrossRefGoogle Scholar
  28. 28.
    Obaidat, I., Mohawesh, R., Al-Ayyoub, M., AL-Smadi, M., Jararweh, Y.: Enhancing the determination of aspect categories and their polarities in Arabic reviews using lexicon-based approaches. In: 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1–6 (2015)Google Scholar
  29. 29.
    Khasawneh, R.T., Wahsheh, H.A., Alsmadi, I.M., AI-Kabi, M.N.: Arabic sentiment polarity identification using a hybrid approach. In: 2015 6th International Conference on Information and Communication Systems (ICICS), pp. 148–153 (2015)Google Scholar
  30. 30.
    Al Sallab, A.A., Baly, R., Badaro, G., Hajj, H., El Hajj, W., Shaban, K.B.: Deep learning models for sentiment analysis in Arabic. In: ANLP Workshop 2015, p. 9 (2015)Google Scholar
  31. 31.
    Abdul-Mageed, M., Diab, M., Korayem, M.: Subjectivity and sentiment analysis of modern standard Arabic. In: 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, vol. 2 (2011)Google Scholar
  32. 32.
    Maamouri, M., Bies, A., Buckwalter, T., Mekki, W.: The Penn Arabic treebank: building a large-scale annotated Arabic corpus. In: NEMLAR Conference on Arabic Language Resources and Tools, vol. 27, pp. 466–467 (2004)Google Scholar
  33. 33.
    Rushdi-Saleh, M., Martín-Valdivia, M.T., Ureña-López, L.A., Perea-Ortega, J.M.: OCA: opinion corpus for Arabic. J. Am. Soc. Inf. Sci. Technol. 62(10), 2045–2054 (2011)CrossRefGoogle Scholar
  34. 34.
    Ahmed, W.A., El-Halees, A.: Arabic Opinion Mining Using Parallel Decision TreesGoogle Scholar
  35. 35.
    Al-Smadi, M., Qawasmeh, O., Talafha, B., Quwaider, M.: Human annotated Arabic dataset of book reviews for aspect based sentiment analysis. In: 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), pp. 726–730 (2015)Google Scholar
  36. 36.
    Aly, M.A., Atiya, A.F.: LABR: A large scale Arabic book reviews dataset. In: ACL, vol. 2, pp. 494–498 (2013)Google Scholar
  37. 37.
    Khalil, T., Halaby, A., Hammad, M., El-Beltagy, S.R.: Which configuration works best? An experimental study on supervised Arabic twitter sentiment analysis (2015)Google Scholar
  38. 38.
    Refaee, E., Rieser, V.: Subjectivity and sentiment analysis of Arabic twitter feeds with limited resources. In: Workshop on Free/Open-Source Arabic Corpora and Corpora Processing Tools Workshop Programme, p. 16 (2014)Google Scholar
  39. 39.
    Badaro, G., Baly, R., Akel, R., Fayad, L., Khairallah, J., Hajj, H., El-Hajj, W., Shaban, K.B.: A light lexicon-based mobile application for sentiment mining of Arabic tweets. In: ANLP Workshop 2015, p. 18 (2015)Google Scholar
  40. 40.
    Mourad, A., Darwish, K.: 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 (2013)Google Scholar
  41. 41.
    Nabil, M., Aly, M., Atiya, A.F.: ASTD: Arabic sentiment tweets dataset. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2515–2519 (2015)Google Scholar
  42. 42.
    Ibrahim, H.S., Abdou, S.M., Gheith, M.: MIKA: a tagged corpus for modern standard Arabic and colloquial sentiment analysis. In: 2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), vol. 2, pp. 353–358 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Areeb alOwisheq
    • 1
    Email author
  • Sarah alHumoud
    • 1
  • Nora alTwairesh
    • 2
  • Tarfa alBuhairi
    • 1
  1. 1.Computer Science DepartmentAl-Imam Muhammad ibn Saud Islamic UniversityRiyadhSaudi Arabia
  2. 2.Information Technology DepartmentKing Saud UniversityRiyadhSaudi Arabia

Personalised recommendations