IDEAS home Printed from https://ideas.repec.org/a/ids/ijdmmm/v8y2016i4p369-381.html
   My bibliography  Save this article

A framework for Arabic sentiment analysis using supervised classification

Author

Listed:
  • Rehab M. Duwairi
  • Islam Qarqaz

Abstract

Sentiment analysis aims to determine the polarity that is embedded in people comments and reviews. Sentiment analysis is important for companies and organisations which are interested in evaluating their products or services. The current paper deals with sentiment analysis in Arabic reviews. Three classifiers were applied on an in-house developed dataset of tweets/comments. In particular, the Naïve Bayes, SVM and K-nearest neighbour classifiers were employed. This paper also addresses the effects of term weighting schemes on the accuracy of the results. The binary model, term frequency and term frequency inverse document frequency were used to assign weights to the tokens of tweets/comments. The results show that alternating between the three weighting schemes slightly affects the accuracies. The results also clarify that the classifiers were able to remove false examples (high precision) but were not that successful in identifying all correct examples (low recall).

Suggested Citation

  • Rehab M. Duwairi & Islam Qarqaz, 2016. "A framework for Arabic sentiment analysis using supervised classification," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 8(4), pages 369-381.
    • Handle: RePEc:ids:ijdmmm:v:8:y:2016:i:4:p:369-381
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=81247
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Al-Razgan, Muna & Alrowily, Asma & Al-Matham, Rawan N. & Alghamdi, Khulood M. & Shaabi, Maha & Alssum, Lama, 2021. "Using diffusion of innovation theory and sentiment analysis to analyze attitudes toward driving adoption by Saudi women," Technology in Society, Elsevier, vol. 65(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijdmmm:v:8:y:2016:i:4:p:369-381. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=342 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.