COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets

Shahriar, Khandaker Tayef; Islam, Muhammad Nazrul; Anwar, Musfique; Sarker, Iqbal H.

doi:10.1016/j.imu.2022.100969

Cited by 7 publications

(7 citation statements)

References 36 publications

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“…Additional machine learning techniques for polarity classification were Microsoft Azure cognitive services, Amazon Web Services (AWS), and Baidu’s AipNLP [ 59 , 64 , 75 ]. Deep learning techniques mainly used convolutional neural networks, recurrent neural networks, bidirectional long short-term memory (LSTM), and Bidirectional Encoder Representations from Transformers [ 63 , 65 , 66 , 68 , 71 , 77 ].…”

Section: Resultsmentioning

confidence: 99%

“…Nineteen sentiment analysis studies were based on machine learning, and they trained machine learning classifiers by annotating tweets in the data set [59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77]. Machine learning approaches used supervised classification algorithms to extract information regarding sentiment polarity.…”

Section: Sentiment/emotion Analysismentioning

confidence: 99%

“…Seven studies employed hybrid methods combining lexicons and machine learning for polarity classification [7,[78][79][80][81][82][83]. Accuracy, precision, recall, and F1 score were typically used to evaluate the performance of classification models [61,[68][69][70]. In terms of model performance, 3 studies showed that LSTM outperformed other classifiers [68,70,78].…”

Section: Sentiment/emotion Analysismentioning

confidence: 99%

“…Accuracy, precision, recall, and F1 score were typically used to evaluate the performance of classification models [61,[68][69][70]. In terms of model performance, 3 studies showed that LSTM outperformed other classifiers [68,70,78].…”

Section: Sentiment/emotion Analysismentioning

confidence: 99%

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Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

et al. 2023

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Background Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines. Objective We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination. Methods We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media–related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Results A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization. Conclusions The applications of social media and digital technologies in addressing COVID-19 vaccination–related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

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Section: Resultsmentioning

confidence: 99%

Section: Sentiment/emotion Analysismentioning

confidence: 99%

Section: Sentiment/emotion Analysismentioning

confidence: 99%

Section: Sentiment/emotion Analysismentioning

confidence: 99%

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Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

et al. 2023

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“…All nations now share the shared objective of eradicating COVID-19 and resuming daily operations. Since the pandemic's start, several research teams at significant pharmaceutical corporations and academic institutions throughout the globe have been creating vaccines [4]. WHO has granted emergency use authorizations for eight COVID-19 vaccines, including Sinovac, Sinopharm/BIBP, Moderna, AstraZeneca/Oxford, Johnson & Johnson, Covishield, and Pfizer/BioNTech.…”

Section: Introductionmentioning

confidence: 99%

Opinions for Receiving COVID-19 Vaccines Based on Sentiment Analysis

2022

pnr

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Due to vaccination hesitancy/refusal, a call to action is urgently needed for the COVID-19 vaccine deployment. Find out what the public thinks about the COVID-19 vaccine. Sentiment analysis is a technique for getting text sentiment scores. Therefore, we proposed architecture to analyze the textual data collection of people's opinions on COVID-19 vaccines using two of the best sentiment analysis techniques, the Bidirectional Encoder Representations from Transformers (BERT) technique and the Valence Aware Dictionary for sEntiment Reasoning (VADER) technique of Natural Language Processing (NLP). A questionnaire survey of corona vaccines recipients who recommend COVID-19 collected the data. Finally, recommendations for the corona vaccine were investigated, and various studies were done to determine its efficacy. Accuracy, precision, recall, and f1-score are standard evaluation criteria. The data shows the proposed model's excellent sentiment analysis performance, indicating that most vaccine users prefer to recommend others to get the vaccines.

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Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models

Shahriar

Anwar

Sarker

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets

Cited by 7 publications

References 36 publications

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

Opinions for Receiving COVID-19 Vaccines Based on Sentiment Analysis

Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models

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