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Application of Support Vector Machine for Arabic Sentiment Classification Using Twitter-Based Dataset

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  • Sarah N. Alyami

    (College of Computer Science and Information Technology, Community College Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia)

  • Sunday O. Olatunji

    (College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia)

Abstract

Sentiment classification is the process of classifying emotions and opinions in texts. In this study, the problem of Arabic sentiment analysis was addressed. A support vector machine (SVM) model was proposed to classify opinions in Arabic micro-texts as being positive or negative. To evaluate the performance of the SVM model, a dataset was built from tweets discussing several social issues in Saudi Arabia. These issues include changes that were implemented by the country as part of a newly established vision, known as Saudi Arabia Vision 2030. The constructed dataset was manually annotated according to the sentiment conveyed in the text. To achieve the best sentiment classification accuracy, several procedures were implemented within the proposed framework including light stemming, feature extraction (Ngrams, emoji and tweet-topic features), parameter optimisation and feature-set reduction. The experimental results revealed excellent outcomes. An accuracy of 89.83% was achieved using the proposed SVM model.

Suggested Citation

  • Sarah N. Alyami & Sunday O. Olatunji, 2020. "Application of Support Vector Machine for Arabic Sentiment Classification Using Twitter-Based Dataset," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-13, April.
    • Handle: RePEc:wsi:jikmxx:v:19:y:2020:i:01:n:s0219649220400183
    DOI: 10.1142/S0219649220400183
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    References listed on IDEAS

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    1. Wang, Haifeng & Zheng, Bichen & Yoon, Sang Won & Ko, Hoo Sang, 2018. "A support vector machine-based ensemble algorithm for breast cancer diagnosis," European Journal of Operational Research, Elsevier, vol. 267(2), pages 687-699.
    2. Mohammed Rushdi-Saleh & M. Teresa Martín-Valdivia & L. Alfonso Ureña-López & José M. Perea-Ortega, 2011. "OCA: Opinion corpus for Arabic," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(10), pages 2045-2054, October.
    3. Petra Kralj Novak & Jasmina Smailović & Borut Sluban & Igor Mozetič, 2015. "Sentiment of Emojis," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-22, December.
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