Sentiment Analysis for Arabizi: Application to Algerian Dialect

Asma Chader, Dihia Lanasri, Leila Hamdad, Mohamed Belkheir, Wassim Hennoune


Sentiment Analysis and its applications have spread to many languages and domains. With regard to Arabic and its dialects, we witness an increasing interest simultaneously with increase of Arabic texts volume in social media. However, the Algerian dialect had received little attention, and even less in Latin script (Arabizi). In this paper, we propose a supervised approach for sentiment analysis of Arabizi Algerian dialect using different classifiers such as Naive Bayes and Support Vector Machines. We investigate the impact of several preprocessing techniques, dealing with dialect specific aspects. Experimental evaluation on three manually annotated datasets shows promising performance where the approach yielded the highest classification accuracy using SVM algorithm. Moreover, our results emphasize the positive impact of proposed preprocessing techniques. The adding of vowels removal and transliteration, to overcome phonetic and orthographic varieties, allowed us to lift the F-score of SVM from 76 % to 87 %, which is considerable.


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