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A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets

Author

Listed:
  • Harleen Kaur

    (Jamia Hamdard)

  • Shafqat Ul Ahsaan

    (Jamia Hamdard)

  • Bhavya Alankar

    (Jamia Hamdard)

  • Victor Chang

    (Teesside University)

Abstract

With the rise in cases of COVID-19, a bizarre situation of pressure was mounted on each country to make arrangements to control the population and utilize the available resources appropriately. The swiftly rising of positive cases globally created panic, anxiety and depression among people. The effect of this deadly disease was found to be directly proportional to the physical and mental health of the population. As of 28 October 2020, more than 40 million people are tested positive and more than 1 million deaths have been recorded. The most dominant tool that disturbed human life during this time is social media. The tweets regarding COVID-19, whether it was a number of positive cases or deaths, induced a wave of fear and anxiety among people living in different parts of the world. Nobody can deny the truth that social media is everywhere and everybody is connected with it directly or indirectly. This offers an opportunity for researchers and data scientists to access the data for academic and research use. The social media data contains many data that relate to real-life events like COVID-19. In this paper, an analysis of Twitter data has been done through the R programming language. We have collected the Twitter data based on hashtag keywords, including COVID-19, coronavirus, deaths, new case, recovered. In this study, we have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores. We have also compared the performance of the proposed algorithm on certain parameters like precision, recall, F1 score and accuracy with Recurrent Neural Network (RNN) and Support Vector Machine (SVM).

Suggested Citation

  • Harleen Kaur & Shafqat Ul Ahsaan & Bhavya Alankar & Victor Chang, 2021. "A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets," Information Systems Frontiers, Springer, vol. 23(6), pages 1417-1429, December.
    • Handle: RePEc:spr:infosf:v:23:y:2021:i:6:d:10.1007_s10796-021-10135-7
    DOI: 10.1007/s10796-021-10135-7
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    References listed on IDEAS

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    1. Luisa Massari, 2010. "Analysis of MySpace user profiles," Information Systems Frontiers, Springer, vol. 12(4), pages 361-367, September.
    2. Mohammed Kuko & Mohammad Pourhomayoun, 2020. "Single and Clustered Cervical Cell Classification with Ensemble and Deep Learning Methods," Information Systems Frontiers, Springer, vol. 22(5), pages 1039-1051, October.
    3. Mohammed Kuko & Mohammad Pourhomayoun, 0. "Single and Clustered Cervical Cell Classification with Ensemble and Deep Learning Methods," Information Systems Frontiers, Springer, vol. 0, pages 1-13.
    4. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.
    5. Michelle Richey & Aparna Gonibeed & M. N. Ravishankar, 2018. "The Perils and Promises of Self-Disclosure on Social Media," Information Systems Frontiers, Springer, vol. 20(3), pages 425-437, June.
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    Cited by:

    1. Intan Nurma Yulita & Victor Wijaya & Rudi Rosadi & Indra Sarathan & Yusa Djuyandi & Anton Satria Prabuwono, 2023. "Analysis of Government Policy Sentiment Regarding Vacation during the COVID-19 Pandemic Using the Bidirectional Encoder Representation from Transformers (BERT)," Data, MDPI, vol. 8(3), pages 1-17, February.
    2. Victor Chang & Carole Goble & Muthu Ramachandran & Lazarus Jegatha Deborah & Reinhold Behringer, 2021. "Editorial on Machine Learning, AI and Big Data Methods and Findings for COVID-19," Information Systems Frontiers, Springer, vol. 23(6), pages 1363-1367, December.
    3. Waseem Ahmad & Bang Wang & Philecia Martin & Minghua Xu & Han Xu, 2023. "Enhanced sentiment analysis regarding COVID-19 news from global channels," Journal of Computational Social Science, Springer, vol. 6(1), pages 19-57, April.
    4. Carlos Henríquez Miranda & German Sanchez-Torres & Dixon Salcedo, 2023. "Exploring the Evolution of Sentiment in Spanish Pandemic Tweets: A Data Analysis Based on a Fine-Tuned BERT Architecture," Data, MDPI, vol. 8(6), pages 1-18, May.

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