An Enhanced Corpus for Arabic Newspapers Comments

An Enhanced Corpus for Arabic Newspapers

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Hichem Rahab1, Abdelhafid Zitouni2, and Mahieddine Djoudi3

1ICOSI Laboratory, University of Khenchela, Algeria

3TechNE Laboratory, University of Poitiers, France

Abstract: In this paper, we propose our enhanced approach to create a dedicated corpus for Algerian Arabic newspapers comments. The developed approach has to enhance an existing approach by the enrichment of the available corpus and the inclusion of the annotation step by following the Model Annotate Train Test Evaluate Revise (MATTER) approach. A corpus is created by collecting comments from web sites of three well know Algerian newspapers. Three classifiers, support vector machines, naïve Bayes, and k-nearest neighbors, were used for classification of comments into positive and negative classes. To identify the influence of the stemming in the obtained results, the classification was tested with and without stemming. Obtained results show that stemming does not enhance considerably the classification due to the nature of Algerian comments tied to Algerian Arabic Dialect. The promising results constitute a motivation for us to improve our approach especially in dealing with non Arabic sentences, especially Dialectal and French ones.

Keywords: Opinion mining, sentiment analysis, K-Nearest Neighbours, Naïve Bayes, Support Vector Machines, Arabic, comment.

Received December 22, 2017; accepted June 18, 2019

https://doi.org/10.34028/iajit/17/5/12

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