Skip to main content
SpringerLink
Log in

A Systematic Review of Sentiment Analysis in Arabizi

  • Conference paper
  • First Online:
Intelligent Decision Technologies (KESIDT 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 352))

Included in the following conference series:

  • 172 Accesses

Abstract

Sentiment analysis (SA), also called opinion mining, is concerned with the automatic extraction of opinions conveyed in a certain text. Many studies have been conducted in the area of SA especially on English texts, while other languages such as Arabic received less attention. Recently, Arabic Sentiment Analysis (ASA) has received a great deal of interest in the research community. Several studies have been conducted on Arabic and especially in arabizi. This survey presents a systematic review of Arabic sentiment analysis research related to arabizi.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  Google Scholar 

  2. Abdulla, N.A., Al-Ayyoub, M., Al-Kabi, M.N.: An extended analytical study of Arabic sentiments. Int. J. Big Data Intell. 1(1–2), 103–113 (2014)

    Article  Google Scholar 

  3. 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 

  4. 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 

  5. 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)

    Google Scholar 

  6. Alawya, A.: Aspect terms extraction of Arabic dialects for opinion mining using conditional random fields. In: Gelbukh, A. (ed.) CICLing 2016. LNCS, vol. 9624, pp. 211–220. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75487-1_16

    Chapter  Google Scholar 

  7. 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 

  8. Darwish, K., Magdy, W.: Arabic information retrieval. Foundations and Trends® in Information Retrieval, 7(4), 239–342 (2014)

    Google Scholar 

  9. Alotaiby, T.N., Alshebeili, S.A., Alshawi, T., Ahmad, I., Abd El-Samie, F.E.: EEG seizure detection and prediction algorithms: a survey. EURASIP J. Adv. Sign. Proces. 2014(1), 1–21 (2014). https://doi.org/10.1186/1687-6180-2014-183

    Article  Google Scholar 

  10. 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 

  11. Rozovskaya, A., Sproat, R., Benmamoun, E.: Challenges in processing colloquial Arabic. In Proceedings of the International Conference on the Challenge of Arabic for NLP/MT (pp. 4–14) (2006)

    Google Scholar 

  12. Habash, N., Soudi, A., Buckwalter, T.: On Arabic Transliteration. Arabic Computational Morphology: Knowledge-Based and Empirical Methods, pp. 15–22 (2007)

    Google Scholar 

  13. Abdellaoui, H., Zrigui, M.: Using Tweets and emojis to build TEAD: an Arabic dataset for sentiment analysis. Computación y Sistemas 22(3), 777–786 (2018)

    Article  Google Scholar 

  14. Sghaier, M.A., Zrigui, M.: Tunisian dialect-modern standard Arabic bilingual lexicon. In: 14th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, pp. 973–979. IEEE, Hammamet, Tunisia (2017)

    Google Scholar 

  15. Gayed, S., Mallat, S., Zrigui, M.: Exploring word embedding for arabic sentiment analysis. In: Szczerbicki, E., Wojtkiewicz, K., Nguyen, S.V., Pietranik, M., Krótkiewicz, M. (eds.) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2022. Communications in Computer and Information Science, vol. 1716, pp. 92–101. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-8234-7_8

  16. Sghaier, M.A., Zrigui, M. : Rule-based machine translation from Tunisian dialect to modern standard Arabic. In: 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, pp. 310–319. Elsevier, a virtual conference (2020)

    Google Scholar 

  17. Sayadi, K., Liwicki, M., Ingold, R., Bui, M.: Tunisian dialect and modern standard Arabic dataset for sentiment analysis: Tunisian election context. In: 2nd International Conference on Arabic Computational Linguistics. Turkey (2016)

    Google Scholar 

  18. Mulki, H., Haddad, H., Bechikh Ali, C., Babaoglu, I.: Tunisian dialect sentiment analysis: a natural language processing-based approach. Computaci´on y Sistemas 22(4), 1223– 1232 (2018)

    Google Scholar 

  19. Medhaffar, S. Bougares, F., Estève, Y. Hadrich-Belguith, L.: Sentiment analysis of Tunisian dialects: linguistic resources and experiments. In: 3rd Arabic Natural Language Processing Workshop, pp. 55–61. Association for Computational Linguistics, Valencia (2017)

    Google Scholar 

  20. Antit, C., Mechti, S., Faiz, R.: TunRoBERTa: a Tunisian robustly optimized BERT approach model for sentiment analysis. In: 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI) (2021)

    Google Scholar 

  21. Mulki, H., Haddad, H., Gridach, M., Babaoglu, I.: Syntax-Ignorant N-gram embeddings for sentiment analysis of Arabic dialects. In: 4th Arabic Natural Language Processing Workshop, pp. 30–39. Association for Computational Linguistics, Florence (2019)

    Google Scholar 

  22. Zahran, M.A., Magooda, A., Mahgoub, A.Y., Raafat, H.M., Rashwan, M., Atyia, A.: Word representations in vector space and their applications for arabic. CICLing 1, 430–443 (2015)

    Google Scholar 

  23. Mahmoud, A., Zrigui, M.: Semantic similarity analysis for corpus development and paraphrase detection in Arabic. Int. Arab J. Inf. Technol. 18(1), 1–7 (2021)

    Google Scholar 

  24. Haffar, N., Hkiri, E., Zrigui, M.: Using bidirectional LSTM and shortest dependency path for classifying Arabic temporal relations. In: 24th International Conference Knowledge-Based and Intelligent Information & Engineering Systems (KES), pp. 370–379. Elsevier, a virtual conference (2020)

    Google Scholar 

  25. Mahmoud, A., Zrigui, M.: Arabic semantic textual similarity identification based on convolutional gated recurrent units. In : International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1–7. IEEE, Kocaeli (2021)

    Google Scholar 

  26. Haffar, N., Ayadi, R., Hkiri, E., Zrigui, M.: Temporal ordering of events via deep neural networks. In: 16th International Conference on Document Analysis and Recognition (ICDAR), pp. 762–777. Lausanne (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Higher Institute of Computer Science and Communication Techniques ISITCom, University of Sousse, 4011, Hammam Sousse, Tunisia

    Sana Gayed

  2. Research Laboratory in Algebra, Numbers Theory and Intelligent Systems RLANTIS, University of Monastir, 5019, Monastir, Tunisia

    Sana Gayed, Souheyl Mallat & Mounir Zrigui

Authors
  1. Sana Gayed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Souheyl Mallat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mounir Zrigui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sana Gayed .

Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. KES International Research, Shoreham-by-sea, UK

    R.J. Howlett

  3. KES International, Selby, UK

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gayed, S., Mallat, S., Zrigui, M. (2023). A Systematic Review of Sentiment Analysis in Arabizi. In: Czarnowski, I., Howlett, R., Jain, L.C. (eds) Intelligent Decision Technologies. KESIDT 2023. Smart Innovation, Systems and Technologies, vol 352. Springer, Singapore. https://doi.org/10.1007/978-981-99-2969-6_11

Download citation

Publish with us

Policies and ethics

Search

Navigation