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Sentiment Analysis in Dialectal Arabic: A Systematic Review

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Advanced Machine Learning Technologies and Applications (AMLTA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1339))

Abstract

Recently, Sentiment Analysis (SA) in Arabic has gained considerable interest in the research community. Several surveys were conducted concerning Arabic sentiment analysis at one hand and the Arabic dialects on the other hand. However, analyzing the Arabic dialect sentiment analysis in social media context is still questioned and requires further examination. This study aims to systematically review and synthesizes the Arabic sentiment analysis studies related to Arabic dialects aiming to provide a full analysis of 60 research articles from 2012 to 2020. The results pointed out that SVM and NB are the most frequent research ML algorithms used to classify Arabic dialect sentiments. Besides, tweets are required by most articles to carry out their experiments besides getting results. The results also revealed that the most studied dialects are Saudi, Egyptian, Jordanian, and Algerian. To that end, this systematic review paper's outcomes offer an insight into the current trend of Arabic sentiment analysis research involving Arabic dialect studies and form an essential reference for scholars in the Arabic Natural Language Processing context.

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References

  1. Salloum, S.A., Al-Hamad, A.Q., Al-Emran, M., Shaalan, K.: A survey of Arabic text mining. In: Studies in Computational Intelligence, vol. 740, pp. 417–431. Springer, Cham (2018)

    Google Scholar 

  2. Salloum, S.A., Mhamdi, C., Al-Emran, M., Shaalan, K.: Analysis and classification of Arabic newspapers’ facebook pages using text mining techniques. Int. J. Inf. Technol. Lang. Stud. 1(2), 8–17 (2017)

    Google Scholar 

  3. Salloum, S.A., Al-Emran, M., Monem, A.A., Shaalan, K.: Using text mining techniques for extracting information from research articles. In: Studies in Computational Intelligence, vol. 740, Springer, Heidelberg (2018)

    Google Scholar 

  4. Guellil, I., Azouaou, F.: ASDA: Analyseur Syntaxique du Dialecte Alg{\’e} rien dans un but d’analyse s{\’e} mantique. arXiv Prepr, arXiv1707.08998 (2017)

  5. Mustafa, H.H., Mohamed, A., Elzanfaly, D.S.: An enhanced approach for arabic sentiment analysis. Int. J. Artif. Intell. Appl. 8(5), 1–14 (2017)

    Google Scholar 

  6. Assiri, A., Emam, A., Al-Dossari, H.: Saudi twitter corpus for sentiment analysis. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 10(2), 272–275 (2016)

    Google Scholar 

  7. Baly, R., Khaddaj, A., Hajj, H., El-Hajj, W., Shaban, K.B.: ArSentD-LEV: a multi-topic corpus for target-based sentiment analysis in Arabic levantine tweets. arXiv Prepr, arXiv1906.01830 (2019)

  8. Elnagar, A., Einea, O., Lulu, L.: Comparative study of sentiment classification for automated translated Latin reviews into Arabic. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pp. 443–448 (2017)

    Google Scholar 

  9. Alawami, A.: Aspect terms extraction of Arabic dialects for opinion mining using conditional random fields. In: International Conference on Intelligent Text Processing and Computational Linguistics, pp. 211–220 (2016)

    Google Scholar 

  10. Oussous, A., Benjelloun, F.-Z., Lahcen, A.A., Belfkih, S.: ASA: a framework for Arabic sentiment analysis. J. Inf. Sci. 46, 544–559 (2019)

    Article  Google Scholar 

  11. Harrag, F.: Estimating the sentiment of arabic social media contents: a survey. In: 5th International Conference on Arabic Language Processing (2014)

    Google Scholar 

  12. Korayem, M., Crandall, D., Abdul-Mageed, M.: Subjectivity and sentiment analysis of arabic: a survey. In: International Conference on Advanced Machine Learning Technologies and Applications, pp. 128–139 (2012)

    Google Scholar 

  13. Biltawi, M., Etaiwi, W., Tedmori, S., Hudaib, A., Awajan, A.: Sentiment classification techniques for Arabic language: a survey. In: 2016 7th International Conference on Information and Communication Systems (ICICS), pp. 339–346 (2016)

    Google Scholar 

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

    Article  Google Scholar 

  15. Kaseb, G.S., Ahmed, M.F.: Arabic sentiment analysis approaches: an analytical survey. Int. J. Sci. Eng. Res. 7(10), 1871–1874 (2016)

    Google Scholar 

  16. Alhumoud, S.O., Altuwaijri, M.I., Albuhairi, T.M., Alohaideb, W.M.: Survey on arabic sentiment analysis in twitter. Int. Sci. Index 9(1), 364–368 (2015)

    Google Scholar 

  17. Salloum, S.A., Al-Emran, M., Monem, A.A., Shaalan, K.: A survey of text mining in social media: facebook and twitter perspectives. Adv. Sci. Technol. Eng. Syst. J. 2(1), 127–133 (2017)

    Article  Google Scholar 

  18. Salloum, S.A., Khan, R., Shaalan, K.: A survey of semantic analysis approaches. In: Joint European-US Workshop on Applications of Invariance in Computer Vision, pp. 61–70 (2020)

    Google Scholar 

  19. Salloum, S.A., Alshurideh, M., Elnagar, A., Shaalan, K.: Mining in educational data: review and future directions. In: Joint European-US Workshop on Applications of Invariance in Computer Vision, pp. 92–102 (2020)

    Google Scholar 

  20. Wahdan, K.S.A., Hantoobi, S., Salloum, S.A., Shaalan, K.: A systematic review of text classification research based ondeep learning models in Arabic language. Int. J. Electr. Comput. Eng 10(6), 6629–6643 (2020)

    Google Scholar 

  21. Alnawas, A., Arıcı, N.: The corpus based approach to sentiment analysis in modern standard Arabic and Arabic dialects: a literature review. Politek. Derg. 21(2), 461–470 (2018)

    Google Scholar 

  22. Ibrahim, M.A., Salim, N.: Opinion analysis for twitter and arabic tweets: a systematic literature review. J. Theor. Appl. Inf. Technol. 56(3), 338–348 (2013)

    Google Scholar 

  23. Elnagar, A., Khalifa, Y.S., Einea, A.: Hotel Arabic-reviews dataset construction for sentiment analysis applications. In: Intelligent Natural Language Processing: Trends and Applications, pp. 35–52. Springer, Heidelberg (2018)

    Google Scholar 

  24. Elnagar, A., Einea, O.: Brad 1.0: book reviews in arabic dataset. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), pp. 1–8 (2016)

    Google Scholar 

  25. Elnagar, A., Einea, O., Al-debsi, R.: Automatic text tagging of Arabic news articles using ensemble deep learning models (2019)

    Google Scholar 

  26. Sharma, A., Dey, S.: A comparative study of feature selection and machine learning techniques for sentiment analysis (2012)

    Google Scholar 

  27. Altowayan, A.A., Elnagar, A.: Improving Arabic sentiment analysis with sentiment-specific embeddings. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 4314–4320 (2017)

    Google Scholar 

  28. Elnagar, A., Al-Debsi, R., Einea, O.: Arabic text classification using deep learning models. Inf. Process. Manag. 57(1), 102121 (2020)

    Article  Google Scholar 

  29. Elnagar, A.: Investigation on sentiment analysis for arabic reviews. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), pp. 1–7 (2016)

    Google Scholar 

  30. Lulu, L., Elnagar, A.: Automatic Arabic dialect classification using deep learning models. Procedia Comput. Sci. 142, 262–269 (2018)

    Article  Google Scholar 

  31. Badaro, G., Baly, R., Hajj, H., Habash, N., El-Hajj, W.: 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 (2014)

    Google Scholar 

  32. Al Omari, M., Al-Hajj, M., Sabra, A., Hammami, N.: Hybrid CNNs-LSTM deep analyzer for arabic opinion mining. In: 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 364–368 (2019)

    Google Scholar 

  33. Atoum, J.O., Nouman, M.: Sentiment analysis of Arabic jordanian dialect tweets. Int. J. Adv. Comput. Sci. Appl. 10(2), 256–262 (2019)

    Google Scholar 

  34. Gamal, D., Alfonse, M., El-Horbaty, E.-S.M., Salem, A.-B.M.: Twitter benchmark dataset for arabic sentiment analysis. Int. J. Mod. Educ. Comput. Sci. 11(1), 33 (2019)

    Article  Google Scholar 

  35. Touati, I., Ellouze, M., Graja, M., Belguith, L.H.: Appraisal of two Arabic opinion summarization methods: statistical versus machine learning. Comput. J. (2020)

    Google Scholar 

  36. Bin Hathlian, N.F., Hafez, A.M.: Subjective text mining for Arabic social media. Int. J. Semant. Web Inf. Syst. 13(2), 13 (2017)

    Google Scholar 

  37. Itani, M., Roast, C., Al-Khayatt, S.: Corpora for sentiment analysis of Arabic text in social media. In: 2017 8th International Conference on Information and Communication Systems (ICICS), pp. 64–69 (2017)

    Google Scholar 

  38. Al-Rubaiee, H., Qiu, R., Li, D.: Identifying Mubasher software products through sentiment analysis of Arabic tweets. In: 2016 International Conference on Industrial Informatics and Computer Systems (CIICS), pp. 1–6 (2016)

    Google Scholar 

  39. Bin Hathlian, N.F., Hafezs, A.M.: Sentiment-subjective analysis framework for arabic social media posts. In: 2016 4th Saudi International Conference on Information Technology (Big Data Analysis)(KACSTIT), pp. 1–6 (2016)

    Google Scholar 

  40. Sghaier, M.A., Zrigui, M.: Sentiment analysis for Arabic e-commerce websites. In: 2016 International Conference on Engineering & MIS (ICEMIS), pp. 1–7 (2016)

    Google Scholar 

  41. Shoukry, A., Rafea, A.: A hybrid approach for sentiment classification of Egyptian Dialect Tweets. In: 2015 First International Conference on Arabic Computational Linguistics (ACLing), pp. 78–85 (2015)

    Google Scholar 

  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), pp. 353–358 (2015)

    Google Scholar 

  43. Shoukry, A., Rafea, A.: Sentence-level Arabic sentiment analysis. In: 2012 International Conference on Collaboration Technologies and Systems (CTS), pp. 546–550 (2012)

    Google Scholar 

  44. Kwaik, K.A., Chatzikyriakidis, S., Dobnik, S., Saad, M., Johansson, R.: An Arabic tweets sentiment analysis dataset (ATSAD) using distant supervision and self training. In: Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, pp. 1–8 (2020)

    Google Scholar 

  45. Fourati, C., Messaoudi, A., Haddad, H.: TUNIZI: a Tunisian Arabizi sentiment analysis Dataset. arXiv Prepr, arXiv2004.14303 (2020)

  46. Moudjari, L., Akli-Astouati, K., Benamara, F.: An Algerian corpus and an annotation platform for opinion and emotion analysis. In: Proceedings of The 12th Language Resources and Evaluation Conference, pp. 1202–1210 (2020)

    Google Scholar 

  47. Abdulla, N.A., Ahmed, N.A., Shehab, M.A., Al-Ayyoub, M.: Arabic sentiment analysis: lexicon-based and corpus-based. In: 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1–6 (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Sharjah, Sharjah, UAE

    Ashraf Elnagar

  2. Department of Foreign Language, University of Sharjah, Sharjah, UAE

    Sane Yagi

  3. Computer Engineering Department, University of Sharjah, Sharjah, UAE

    Ali Bou Nassif

  4. Electrical Engineering Department, University of Sharjah, Sharjah, UAE

    Ismail Shahin

  5. Machine Learning and NLP Research Group, Department of Computer Science, University of Sharjah, Sharjah, UAE

    Ashraf Elnagar, Sane Yagi, Ali Bou Nassif, Ismail Shahin & Said A. Salloum

Authors
  1. Ashraf Elnagar
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  2. Sane Yagi
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  3. Ali Bou Nassif
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  4. Ismail Shahin
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  5. Said A. Salloum
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Corresponding author

Correspondence to Said A. Salloum .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Computer and Information Faculty, Giza, Egypt

    Aboul-Ella Hassanien

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Kuo-Chi Chang

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Tang Mincong

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Elnagar, A., Yagi, S., Nassif, A.B., Shahin, I., Salloum, S.A. (2021). Sentiment Analysis in Dialectal Arabic: A Systematic Review. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_39

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