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Subjective Text Mining for Arabic Social Media

Author

Listed:
  • Nourah F. Bin Hathlian

    (College of Arts and Sciences, Nairiyah University of Hafer Albatin, Alkhbar, Saudi Arabia)

  • Alaaeldin M. Hafez

    (College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia)

Abstract

The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.

Suggested Citation

  • Nourah F. Bin Hathlian & Alaaeldin M. Hafez, 2017. "Subjective Text Mining for Arabic Social Media," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(2), pages 1-13, April.
    • Handle: RePEc:igg:jswis0:v:13:y:2017:i:2:p:1-13
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    Cited by:

    1. Sumayh S. Aljameel & Dina A. Alabbad & Norah A. Alzahrani & Shouq M. Alqarni & Fatimah A. Alamoudi & Lana M. Babili & Somiah K. Aljaafary & Fatima M. Alshamrani, 2020. "A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia," IJERPH, MDPI, vol. 18(1), pages 1-12, December.
    2. Haoran Zhu & Lei Lei, 2022. "The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis," SAGE Open, , vol. 12(2), pages 21582440221, April.

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