Improving arabic sentiment analysis on social media: a comparative study on applying different pre-processing techniques

Authors

  • Al-Yasiri EK Department of Software, College of Information Technology, University of Babylon, Iraq
  • Al-Azawei A Department of Software, College of Information Technology, University of Babylon, Iraq

Keywords:

Social Networking Sites, Arabic sentiment analysis, Pre-processing techniques, Classifying Arabic text, Data mining algorithms

Abstract

Regardless of the clear growth of Arabic texts on social networking sites (SNSs), it is still difficult to understand or summarize users' opinions or perspectives on a specific topic. Accordingly, Arabic text classification is one of the most challenging topics. This is because of several issues related to the nature of the Arabic language and words that have different variation in meaning. In this paper, after tokenizing the Arabic words, we investigate the role of several pre-processing techniques before classifying Arabic text into different categories. Arabic words were converted into vectors using the term frequency-inverse document frequency (TF-IDF) technique. The findings show that applying Linear Support Vector Machine (LSVC) with stop words and without stemming techniques can outperform the application of Decision Tree (DT) and Random Forest (RF) methods. It was found that the effectiveness of the proposed LSVC is 99.37%. These outcomes are significant to identify users' opinions on SNSs and can have many implications on political, social, economic, and business sectors.

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Published

2024-02-26

How to Cite

Al-Yasiri, E. K., & Al-Azawei, A. (2024). Improving arabic sentiment analysis on social media: a comparative study on applying different pre-processing techniques. COMPUSOFT: An International Journal of Advanced Computer Technology, 8(06), 3150–3157. Retrieved from https://ijact.in/index.php/j/article/view/495

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Section

Review Article

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