SIAAC: Sentiment Polarity Identification on Arabic Algerian Newspaper Comments

  • Hichem RahabEmail author
  • Abdelhafid Zitouni
  • Mahieddine Djoudi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 662)


It is a challenging task to identify sentiment polarity in Arabic journals comments. Algerian daily newspapers interest more and more people in Algeria, and due to this fact they interact with it by comments they post on articles in their websites. In this paper we propose our approach to classify Arabic comments from Algerian Newspapers into positive and negative classes. Publicly-available Arabic datasets are very rare on the Web, which make it very hard to carring out studies in Arabic sentiment analysis. To reduce this gap we have created SIAAC (Sentiment polarity Identification on Arabic Algerian newspaper Comments) a corpus dedicated for this work. Comments are collected from website of well-known Algerian newspaper Echorouk. For experiments two well known supervised learning classifiers Support Vector Machines (SVM) and Naïve Bayes (NB) were used, with a set of different parameters for each one. Recall, Precision and F_measure are computed for each classifier. Best results are obtained in term of precision in both SVM and NB, also the use of bigram increase the results in the two models. Compared with OCA, a well know corpus for Arabic, SIAAC give a competitive results. Obtained results encourage us to continue with others Algerian newspaper to generalize our model.


Opinion mining Sentiment analysis Arabic comments Machine learning Natural Language Processing Newspaper Support Vector Machines Naïve Bayes 


  1. 1.
    Zhang, C., Zeng, D., Li, J., Wang, F.Y., Zuo, W.: Sentiment analysis of Chinese documents: from sentence to document level. J. Am. Soc. Inf. Sci. Technol. 60(12), 2474–2487 (2009)CrossRefGoogle Scholar
  2. 2.
    Mountassir, A., Benbrahim, H., Berraba, I.: Sentiment classification on arabic corpora. A preliminary cross-study. Doc numérique 16(1), 73–96 (2013)CrossRefGoogle Scholar
  3. 3.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing (2010)Google Scholar
  5. 5.
    Jackson, P., Moulinier, I.: Natural Language Processing for Online Applications: Text Retrieval, Extraction and Categorization, vol. 5. John Benjamins Publishing Company, Amsterdam (2002)CrossRefGoogle Scholar
  6. 6.
    Atia, S., Shaalan, K.: Increasing the accuracy of opinion mining in Arabic. In: Proceedings—1st International Conference on Arabic Computational Linguistics: Advances in Arabic Computational Linguistics ACLing 2015, pp. 106–113 (2015)Google Scholar
  7. 7.
    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
  8. 8.
    Cherif, W., Madani, A., Kissi, M.: Towards an efficient opinion measurement in Arabic comments. Procedia Comput. Sci. 73(Awict), 122–129 (2015)CrossRefGoogle Scholar
  9. 9.
    Ziani, A., Tlili Guaissa, Y., Nabiha, A.: Détection de polarité d’opinion dans les forums en langues arabe par fusion de plusieurs SVMs. 7, 17–21 (2013)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Rushdi-Saleh, M., Martín-Valdivia, M.: Bilingual experiments with an Arabic–English corpus for opinion mining. In: Proceedings on International Conference on Recent Advances in Natural Language Processing 2011, pp. 740–745, September 2011Google Scholar
  12. 12.
    Alotaibi, S.S., Anderson, C.W.: Extending the knowledge of the Arabic sentiment classification using a foreign external lexical source. Int. J. Nat. Lang. Comput. 5(3), 1–11 (2016)CrossRefGoogle Scholar
  13. 13.
    Brahimi, B., Touahria, M., Tari, A.: Data and text mining techniques for classifying arabic tweet polarity. J. Digit. Inf. Manag. 14(1), 15–25 (2016)Google Scholar
  14. 14.
    Pustejovsky, J., Stubbs, A.: Natural Language Annotation for Machine Learning: A guide to corpus-building for applications. O'Reilly Media, Inc. (2012)Google Scholar
  15. 15.
    Duwairi, R.M.: Sentiment analysis for dialectical Arabic. In: 6th International Conference on Information and Communication Systems (ICICS), 2015, pp. 166–170, February 2015Google Scholar
  16. 16.
    El-defrawy, M.: Enhancing root extractors using light stemmers. In: 29th Pacific Asia Conference on Language, Information and Computation, pp. 157–166 (2015)Google Scholar
  17. 17.
    Agarwal, B., Mittal, N.: Prominent feature extraction for sentiment analysis (2016)Google Scholar
  18. 18.
    Huan, L., Lei, Y.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17(4), 491–502 (2005)CrossRefGoogle Scholar
  19. 19.
    McCallum, A., Nigam, K.: A comparison of event models for Naive Bayes text classification. In: AAAI/ICML-98 Work Learning Text Category, pp. 41–48 (1998)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Hichem Rahab
    • 1
    Email author
  • Abdelhafid Zitouni
    • 2
  • Mahieddine Djoudi
    • 3
  1. 1.ICOSI LabsUniversity of KhenchelaKhenchelaAlgeria
  2. 2.Lire LabsAbdelhamid Mehri Constantine 2 UniversityConstanineAlgeria
  3. 3.TECHNE LabsUniversity of PoitiersPoitiers Cedex 9France

Personalised recommendations