IDEAS home Printed from https://ideas.repec.org/a/ids/wremsd/v16y2020i6p590-604.html
   My bibliography  Save this article

Fine grain sentiment grading of user-generated big data using contextual cues

Author

Listed:
  • Akshi Kumar
  • Geetanjali Garg

Abstract

With a tremendous growth in the use of online resources, micro-blogging website like Twitter offers a vast amount of user-generated knowledge about marketing. This knowledge can be used for sentiment analysis and finds wide application in both public and private sectors. A novel two-tier classification framework using machine learning algorithms and lexicon-based algorithm is proposed to extract sentiment of tweets and assign a fine grain grade to the tweets among one of the seven classes namely: high negative, moderate negative, low negative, neutral, low positive, moderate positive and high positive. Context is also used by forming SentiCircles for the task of sentiment classification. The evaluation of the study shows that the proposed method provides fine grain grading to tweets with accuracy of 61.84%. The hybrid machine learning and lexicon-based processing technique with the usage of contextual cues is an effective and practical method for sentiment analysis.

Suggested Citation

  • Akshi Kumar & Geetanjali Garg, 2020. "Fine grain sentiment grading of user-generated big data using contextual cues," World Review of Entrepreneurship, Management and Sustainable Development, Inderscience Enterprises Ltd, vol. 16(6), pages 590-604.
    • Handle: RePEc:ids:wremsd:v:16:y:2020:i:6:p:590-604
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=111390
    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.

    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:wremsd:v:16:y:2020:i:6:p:590-604. 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=173 .

    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.