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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)

Abstract

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.

Keywords

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

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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

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