Details

Autor(en) / Beteiligte

• Abo, Mohamed Elhag Mohamed
• Idris, Norisma
• Mahmud, Rohana
• Qazi, Atika
• Hashem, Ibrahim Abaker Targio
• Maitama, Jaafar Zubairu
• Naseem, Usman
• Khan, Shah Khalid
• Yang, Shuiqing

Titel

A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection

Ist Teil von

• Sustainability, 2021-09, Vol.13 (18), p.10018

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• A sentiment analysis of Arabic texts is an important task in many commercial applications such as Twitter. This study introduces a multi-criteria method to empirically assess and rank classifiers for Arabic sentiment analysis. Prominent machine learning algorithms were deployed to build classification models for Arabic sentiment analysis classifiers. Moreover, an assessment of the top five machine learning classifiers’ performances measures was discussed to rank the performance of the classifier. We integrated the top five ranking methods with evaluation metrics of machine learning classifiers such as accuracy, recall, precision, F-measure, CPU Time, classification error, and area under the curve (AUC). The method was tested using Saudi Arabic product reviews to compare five popular classifiers. Our results suggest that deep learning and support vector machine (SVM) classifiers perform best with accuracy 85.25%, 82.30%; precision 85.30, 83.87%; recall 88.41%, 83.89; F-measure 86.81, 83.87%; classification error 14.75, 17.70; and AUC 0.93, 0.90, respectively. They outperform decision trees, K-nearest neighbours (K-NN), and Naïve Bayes classifiers.