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Sentiment Analysis of COVID-19 Tweets Using Voting Ensemble-Based Model

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Image Based Computing for Food and Health Analytics: Requirements, Challenges, Solutions and Practices

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Abstract

In the whole world, the microblog has become a key forum for people to share their thoughts and viewpoints on current events. When there is a sudden epidemic of coronavirus, the posts about it are normally accompanied by a burst of microblog numbers, which offers a perfect opportunity to investigate public opinion about the incidents. In this case, sentiment research should be used to investigate how coronavirus impacts public opinion. Deep learning (DL) and machine learning (ML) models have become very popular for sentiment analysis. This research paper uses a voting ensemble model, for making a voting ensemble model We have divided our implementation part into four stages. The first stage is data preprocessing, which includes stopping words elimination and cleaning the dataset. The second stage is pipelining, in which we have passed two methods first one is TFIDF vectorizer and the second one is a machine learning classifier. TFIDF vectorizer and classifier model are used in the third stage for making a single classifier model. In the fourth stage, we have applied a hard voting Ensemble classifier technique for the overall average accuracy of machine learning models.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Dr. B.R. Ambedkar, National Institute of Technology, Jalandhar, Punjab, India

    Kamal & Amritpal Singh

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  1. Kamal
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  2. Amritpal Singh
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Correspondence to Kamal .

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Editors and Affiliations

  1. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Rajeev Tiwari

  2. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Deepika Koundal

  3. School of Health Sciences and Technology, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Shuchi Upadhyay

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Kamal, Singh, A. (2023). Sentiment Analysis of COVID-19 Tweets Using Voting Ensemble-Based Model. In: Tiwari, R., Koundal, D., Upadhyay, S. (eds) Image Based Computing for Food and Health Analytics: Requirements, Challenges, Solutions and Practices. Springer, Cham. https://doi.org/10.1007/978-3-031-22959-6_9

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