• DocumentCode
    3756111
  • Title

    Increasing the Accuracy of Opinion Mining in Arabic

  • Author

    Sasi Atia;Khaled Shaalan

  • Author_Institution
    IT Dept., Dubai Stat. center, Dubai, United Arab Emirates
  • fYear
    2015
  • fDate
    4/1/2015 12:00:00 AM
  • Firstpage
    106
  • Lastpage
    113
  • Abstract
    Opinion Mining is a raising research field of interest, with its different applications derived by market needs to analyze product reviews or to assess the public opinion, for political reasons, during presidential campaigns. In this paper, we address an approach for improving accuracy of Opinion Mining in Arabic. In order to conduct our study we need Arabic linguistic resources for opinion mining. Investigating the available resources we found that the OCA corpus is available and sufficient to prove our approach. Experimental results showed that applying different parameters of the machine learning classifiers on the OCA corpus leads to increasing the accuracy of the Arabic Opinion Mining.
  • Keywords
    "Predictive models","Data mining","Support vector machines","Niobium","Sentiment analysis","Text processing","Automobiles"
  • Publisher
    ieee
  • Conference_Titel
    Arabic Computational Linguistics (ACLing), 2015 First International Conference on
  • Print_ISBN
    978-1-4673-9154-2
  • Type

    conf

  • DOI
    10.1109/ACLing.2015.22
  • Filename
    7422287