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Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques

Author

Listed:
  • Brahami Menaouer

    (National Polytechnic School of Oran, Algeria)

  • Abdeldjouad Fatma Zahra

    (National Polytechnic School of Oran, Algeria)

  • Sabri Mohammed

    (National Polytechnic School of Oran, Algeria)

Abstract

Social media has revolutionized the way people disclose their personal health concerns and express opinions on public health issues. In this paper a new approach for multi-class sentiment classification using supervised learning techniques. The aim of this multi-class sentiment classification is to assign the healthcare Tweets automatically into predetermined categories on the basis of their linguistic characteristics, their contents, and some of the words that characterize each category from the others. Briefly, relevant health datasets are collected from Twitter using Twitter API; then, use of the methodology is illustrated and evaluated against one with only three different algorithms was used, to improve the accuracy of Decision Trees, SMO, and K-NN classifiers. Many experiments accomplished to prove the validity and efficiency of the approach using datasets tweets and it accomplished the data reduction process to achieve considerable size reduction with the preservation of significant dataset's attributes

Suggested Citation

  • Brahami Menaouer & Abdeldjouad Fatma Zahra & Sabri Mohammed, 2022. "Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 13(1), pages 1-23, January.
  • Handle: RePEc:igg:jssmet:v:13:y:2022:i:1:p:1-23
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    References listed on IDEAS

    as
    1. Menatalla Kaoud, 2017. "Investigation of Customer Knowledge Management: A Case Study Research," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 8(2), pages 12-22, April.
    2. Ankit Srivastava & Vijendra Singh & Gurdeep Singh Drall, 2019. "Sentiment Analysis of Twitter Data: A Hybrid Approach," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 14(2), pages 1-16, April.
    3. Vishu Singhvi & Prateek Srivastava, 2021. "Evaluation of Consumer Reviews for adidas Sports Brands Using Data Mining Tools and Twitter APIs," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 12(6), pages 89-104, November.
    4. Ramandeep Kaur & Sandeep Kautish, 2019. "Multimodal Sentiment Analysis: A Survey and Comparison," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 10(2), pages 38-58, April.
    5. Ridgeway, Greg, 2002. "Looking for lumps: boosting and bagging for density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 379-392, February.
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