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Twitter Based Classification for Personal and Non-personal Heart Disease Claims

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Information and Software Technologies (ICIST 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1283))

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Abstract

The popularity of Twitter has created a massive social interaction between users that generates a large amount of data containing their opinions and feelings in different subjects including their health conditions, these data contain important information that can be used in disease monitoring and detection, therefore, Twitter has attracted the attention of many researchers as it has proven to be an important source of health information on the Internet.

In this work, we conducted a systematic literature review to discover state-of-the-art methods used in the analysis of Twitter posts related to health, then we proposed an approach based on machine learning, sentiment analysis methods and Big Data technologies to ensure optimal classification of the health status of a population related to cardiovascular diseases in a Twitter environment.

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Author information

Authors and Affiliations

  1. IASSE Laboratory, National School of Applied Sciences, USMBA, Fez, Morocco

    Ghita Amrani

  2. TTI Laboratory, Higher School of Technology, USMBA, Fez, Morocco

    Fadoua Khennou & Nour El Houda Chaoui

Authors
  1. Ghita Amrani
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  2. Fadoua Khennou
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  3. Nour El Houda Chaoui
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Corresponding author

Correspondence to Ghita Amrani .

Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Audrius Lopata

  2. Kaunas University of Technology, Kaunas, Lithuania

    Rita Butkienė

  3. Kaunas University of Technology, Kaunas, Lithuania

    Daina Gudonienė

  4. Kaunas University of Technology, Kaunas, Lithuania

    Vilma Sukackė

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Amrani, G., Khennou, F., Chaoui, N.E.H. (2020). Twitter Based Classification for Personal and Non-personal Heart Disease Claims. In: Lopata, A., Butkienė, R., Gudonienė, D., Sukackė, V. (eds) Information and Software Technologies. ICIST 2020. Communications in Computer and Information Science, vol 1283. Springer, Cham. https://doi.org/10.1007/978-3-030-59506-7_21

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  • DOI: https://doi.org/10.1007/978-3-030-59506-7_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59505-0

  • Online ISBN: 978-3-030-59506-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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