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Ooredoo Rayek: A Business Decision Support System Based on Multi-Language Sentiment Analysis of Algerian Operator Telephones

Ooredoo Rayek: A Business Decision Support System Based on Multi-Language Sentiment Analysis of Algerian Operator Telephones

Badia Klouche, Sidi Mohamed Benslimane, Sakina Rim Bennabi
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 16
ISSN: 1947-9301|EISSN: 1947-931X|EISBN13: 9781799806387|DOI: 10.4018/IJTD.2020040105
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MLA

Klouche, Badia, et al. "Ooredoo Rayek: A Business Decision Support System Based on Multi-Language Sentiment Analysis of Algerian Operator Telephones." IJTD vol.11, no.2 2020: pp.66-81. https://doi.org/10.4018/IJTD.2020040105

APA

Klouche, B., Benslimane, S. M., & Bennabi, S. R. (2020). Ooredoo Rayek: A Business Decision Support System Based on Multi-Language Sentiment Analysis of Algerian Operator Telephones. International Journal of Technology Diffusion (IJTD), 11(2), 66-81. https://doi.org/10.4018/IJTD.2020040105

Chicago

Klouche, Badia, Sidi Mohamed Benslimane, and Sakina Rim Bennabi. "Ooredoo Rayek: A Business Decision Support System Based on Multi-Language Sentiment Analysis of Algerian Operator Telephones," International Journal of Technology Diffusion (IJTD) 11, no.2: 66-81. https://doi.org/10.4018/IJTD.2020040105

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Abstract

Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article developed a set of rules allowing the transliteration from Algerian Arabizi to Algerian dialect. Furthermore, the authors used Naïve Bayes (NB) and (Support Vector Machine) SVM classifiers to assign polarity tags to Facebook comments from the official pages of Ooredoo written in multilingual and multi-dialect context. Experimental results show that the system obtains good performance with 83% of accuracy.

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