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OCA: Opinion corpus for Arabic

Author

Listed:
  • Mohammed Rushdi‐Saleh
  • M. Teresa Martín‐Valdivia
  • L. Alfonso Ureña‐López
  • José M. Perea‐Ortega

Abstract

Sentiment analysis is a challenging new task related to text mining and natural language processing. Although there are, at present, several studies related to this theme, most of these focus mainly on English texts. The resources available for opinion mining (OM) in other languages are still limited. In this article, we present a new Arabic corpus for the OM task that has been made available to the scientific community for research purposes. The corpus contains 500 movie reviews collected from different web pages and blogs in Arabic, 250 of them considered as positive reviews, and the other 250 as negative opinions. Furthermore, different experiments have been carried out on this corpus, using machine learning algorithms such as support vector machines and Nave Bayes. The results obtained are very promising and we are encouraged to continue this line of research.

Suggested Citation

  • Mohammed Rushdi‐Saleh & M. Teresa Martín‐Valdivia & L. Alfonso Ureña‐López & José M. Perea‐Ortega, 2011. "OCA: Opinion corpus for Arabic," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(10), pages 2045-2054, October.
    • Handle: RePEc:bla:jamist:v:62:y:2011:i:10:p:2045-2054
    DOI: 10.1002/asi.21598
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    References listed on IDEAS

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    1. Rehab Duwairi & Mohammad Nayef Al‐Refai & Natheer Khasawneh, 2009. "Feature reduction techniques for Arabic text categorization," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2347-2352, November.
    2. Changli Zhang & Daniel Zeng & Jiexun Li & Fei‐Yue Wang & Wanli Zuo, 2009. "Sentiment analysis of Chinese documents: From sentence to document level," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(12), pages 2474-2487, December.
    3. Rehab M. Duwairi, 2006. "Machine learning for Arabic text categorization," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(8), pages 1005-1010, June.
    4. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
    5. Khaled Shaalan & Hafsa Raza, 2009. "NERA: Named Entity Recognition for Arabic," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(8), pages 1652-1663, August.
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