IDEAS home Printed from https://ideas.repec.org/a/igg/jirr00/v10y2020i2p40-58.html
   My bibliography  Save this article

Hybrid Ensemble Learning With Feature Selection for Sentiment Classification in Social Media

Author

Listed:
  • Sanur Sharma

    (Guru Gobind Singh Indraprastha University, Delhi, India)

  • Anurag Jain

    (Guru Gobind Singh Indraprastha University, Delhi, India)

Abstract

This article presents a study on ensemble learning and an empirical evaluation of various ensemble classifiers and ensemble features for sentiment classification of social media data. The data was collected from Twitter in real-time using Twitter API and text pre-processing and ranking-based feature selection is applied to textual data. A framework for a hybrid ensemble learning model is presented where a combination of ensemble features (Information Gain and CHI-Squared) and ensemble classifier that includes Ada Boost with SMO-SVM and Logistic Regression has been implemented. The classification of Twitter data is performed where sentiment analysis is used as a feature. The proposed model has shown improvements as compared to the state-of-the-art methods with an accuracy of 88.2% with a low error rate.

Suggested Citation

  • Sanur Sharma & Anurag Jain, 2020. "Hybrid Ensemble Learning With Feature Selection for Sentiment Classification in Social Media," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 10(2), pages 40-58, April.
    • Handle: RePEc:igg:jirr00:v:10:y:2020:i:2:p:40-58
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2020040103
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jirr00:v:10:y:2020:i:2:p:40-58. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.