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Covid-19 Related Sentiment Analysis on Twitter data using Machine Learning based Technologies

Author

Listed:
  • T.Veena
  • G. Medhaswi
  • B. Vani
  • P. Asha Jyothi
  • S. Khaja Mohidden

Abstract

During the crisis situation caused due to COVID-19 disease, managing mental health and psychological well-being is as important as physical health of people. As web based life is broadly utilized by individuals to communicate their feeling and supposition, our framework utilizes Twitter information posted by individuals during this emergency circumstance to dissect the feelings of individuals. For processing the cleaned data NRC Word-Emotion Association Lexicon (have aka EmoLex) is used. NRC Word-Emotion Association Lexicon is a list of English with real-valued scores of intensity for eight basic emotion words ns (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust). The text content of tweeter dataset created by fetching tweets across the world have classified into basic emotions like anger, anticipation, disgust, fear, joy, sadness, surprise and trust. This analysis can be used by authorities to understand the mental health of the people and can take necessary measures to decide on policies to fight against coronavirus which is affecting the social well-being as well as economy of the whole world.

Suggested Citation

  • T.Veena & G. Medhaswi & B. Vani & P. Asha Jyothi & S. Khaja Mohidden, 2024. "Covid-19 Related Sentiment Analysis on Twitter data using Machine Learning based Technologies," International Journal of Scientific Research in Science, Engineering and Technology, Technoscience Academy, vol. 11(2), pages 417-421, April.
    • Handle: RePEc:ijs:ijsrse:v11:y2024:i2:id:71
    DOI: 10.32628/IJSRSET2411247
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