Sentiment Analysis on Social Media for Emotional Prediction During COVID‐19 Pandemic Using Efficient Machine Learning Approach

Sivanantham, Kalimuthu

doi:10.1002/9781119818717.ch12

Cited by 11 publications

(1 citation statement)

References 9 publications

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“…From the publications (n = 47), eight studies compared machine learning models and chose the best-performing algorithm. In these comparisons, SVM [43,44,70] and LR [43,46,68] stood out with highly accurate performances. When comparing deep learning models and machine learning models, BERT showed an outstanding performance [51,55,64,69], whereas in comparison to deep learning models in [61], BiLSTM yielded the best performance.…”

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confidence: 92%

Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends

et al. 2022

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Pandemics and infectious diseases are overcome by vaccination, which serves as a preventative measure. Nevertheless, vaccines also raise public concerns; public apprehension and doubts challenge the acceptance of new vaccines. COVID-19 vaccines received a similarly hostile reaction from the public. In addition, misinformation from social media, contradictory comments from medical experts, and reports of worse reactions led to negative COVID-19 vaccine perceptions. Many researchers analyzed people’s varying sentiments regarding the COVID-19 vaccine using artificial intelligence (AI) approaches. This study is the first attempt to review the role of AI approaches in COVID-19 vaccination-related sentiment analysis. For this purpose, insights from publications are gathered that analyze the (a) approaches used to develop sentiment analysis tools, (b) major sources of data, (c) available data sources, and (d) the public perception of COVID-19 vaccine. Analysis suggests that public perception-related COVID-19 tweets are predominantly analyzed using TextBlob. Moreover, to a large extent, researchers have employed the Latent Dirichlet Allocation model for topic modeling of Twitter data. Another pertinent discovery made in our study is the variation in people’s sentiments regarding the COVID-19 vaccine across different regions. We anticipate that our systematic review will serve as an all-in-one source for the research community in determining the right technique and data source for their requirements. Our findings also provide insight into the research community to assist them in their future work in the current domain.

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