Enhanced Classification of Sentiment Analysis of Arabic Reviews

  • Loai Alnemer
  • Bayan Alammouri
  • Jamal AlsakranEmail author
  • Omar El Ariss
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 29)


Sentiment analysis is the process of mining textual data in order to extract the author’s opinion, typically expressed as a positive, neutral, or negative attitude towards the written text. It is of great interest and has been extensively studied in the English language. However, sentiment analysis in the Arabic language has not received wide attention and most of the research done on Arabic either focuses on introducing new datasets or new sentiment lexicons. In this paper, we introduce a preprocessing suite that includes morphological processing, emoticon extraction, and negation processing to improve the sentiment analysis. Furthermore, we conduct experiments on sentiment analysis of hotel reviews that target two classification tasks: positive/negative and positive/negative/neutral. Our experimental results using various supervised learning algorithms, including deep learning algorithm, demonstrate the effectiveness of the proposed techniques.


  1. 1.
    Abdul-Mageed, M., Diab, M.T., Korayem, M.: Subjectivity and sentiment analysis of modern standard Arabi0063. In: ACL–HLT 2011–Proceedings of 49th Annual Meet. Association for Computational Linguistics, vol. 2, pp. 587–591 (2011)Google Scholar
  2. 2.
    Abdul-Mageed, M., Diab, M.: SANA: a large scale multi-genre, multi-dialect Lexicon for Arabic subjectivity and sentiment analysis. In: Proceedings of the Language Resources and Evaluation Conference, pp. 1162–1169 (2014)Google Scholar
  3. 3.
    Aly, M., Atiya, A.: LABR: a large scale Arabic book reviews dataset. In: The 51st Annual Meeting of the Association for Computational Linguistics, pp. 494–498 (2013)Google Scholar
  4. 4.
    Al-Ayyoub, M., Essa, S.B., Alsmadi, I.: Lexicon-based sentiment analysis of Arabic tweets. Int. J. Soc. Netw. Min. 2, 101–114 (2015)CrossRefGoogle Scholar
  5. 5.
    Al-Smadi, M., Qawasmeh, O., Talafha, B., Quwaider, M.: Human annotated Arabic dataset of book reviews for aspect based sentiment analysis. In: Proceedings of 2015 International Conference on Future Internet of Things and Cloud, FiCloud 2015 and 2015 International Conference on Open and Big Data, OBD 2015, pp. 726–730 (2015)Google Scholar
  6. 6.
    Ariss, O.E., Alnemer, L.M.: Morphology based Arabic sentiment analysis of book reviews. In: Gelbukh, A. (eds.) Computational Linguistics and Intelligent Text Processing, CICLing 2017. Lecture Notes in Computer Science, vol. 10762. Springer, Cham (2018)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Cherif, W., Madani, A., Kissi, M.: Towards an efficient opinion measurement in Arabic comments. Procedia Comput. Sci. 73, 122–129 (2015)CrossRefGoogle Scholar
  9. 9.
    Dahou, A., Xiong, S., Zhou, J., Haddoud, M.H., Duan, P.: 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 (2016)Google Scholar
  10. 10.
    Dellarocas, C., (Michael) Zhang, X., Awad, N.F.: Exploring the value of online product reviews in forecasting sales: the case of motion pictures. J. Interact. Mark. 21, 23–45 (2007)CrossRefGoogle Scholar
  11. 11.
    Duan, W., Gu, B., Whinston, A.B.: Do online reviews matter\(?-\)an empirical investigation of panel data. Decis. Support Syst. 45, 1007–1016 (2008)CrossRefGoogle Scholar
  12. 12.
    ElSahar, H., El-Beltagy, S.R.: Building large Arabic multi-domain resources for sentiment analysis. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 23–34. Springer, Cham (2015)Google Scholar
  13. 13.
    Eskander, R., Rambow, O.: SLSA: a sentiment Lexicon for standard Arabic. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 2545–2550 (2015)Google Scholar
  14. 14.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5, 1–167 (2012)CrossRefGoogle Scholar
  15. 15.
    Mohammad, S.M., Salameh, M., Kiritchenko, S.: How translation alters sentiment. J. Artif. Intell. Res. 55, 95–130 (2016)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Nabil, M., Aly, M., Atiya, A.: ASTD: Arabic sentiment tweets dataset. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 2515–2519 (2015)Google Scholar
  17. 17.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2, 1–135 (2008)CrossRefGoogle Scholar
  18. 18.
    Pang, B., Lee, L., Rd, H., Jose, S.: Thumbs up? sentiment classification using machine learning techniques. Lang. (Baltim), 79–86 (2002)Google Scholar
  19. 19.
    Pedregosa, F., Varoquaux, G.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Poria, S., Cambria, E., Winterstein, G., Huang, G.: Bin: Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl. Based Syst. 69, 45–63 (2014)CrossRefGoogle Scholar
  21. 21.
    Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowl. Based Syst. 108, 42–49 (2016)CrossRefGoogle Scholar
  22. 22.
    Refaee, E., Rieser, V.: An Arabic Twitter corpus for subjectivity and sentiment analysis. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation, pp. 2268–2273 (2014)Google Scholar
  23. 23.
    Sarikaya, R., Kirchhoff, K., Schultz, T., Hakkani-Tur, D.: Introduction to the special issue on processing morphologically rich languages. IEEE Trans. Audio Speech Lang. Process. 17, 861–862 (2009)CrossRefGoogle Scholar
  24. 24.
    Al Shboul, B., Al-Ayyouby, M., Jararwehy, Y.: Multi-way sentiment classification of Arabic reviews. In: 2015 6th International Conference on Information and Communication Systems, ICICS 2015, pp. 206–211. IEEE (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Loai Alnemer
    • 1
  • Bayan Alammouri
    • 1
  • Jamal Alsakran
    • 2
    Email author
  • Omar El Ariss
    • 3
  1. 1.The University of JordanAmmanJordan
  2. 2.Higher Colleges of TechnologyFujariahUAE
  3. 3.Texas A&M University-CommerceCommerceUSA

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