Sentiment Analysis of COVID-19 Vaccine on Twitter Social Media: Systematic Literature Review

Agustiningsih, Kartikasari Kusuma; Utami, Ema; Fatta, Hanif Al

doi:10.1109/icitisee53823.2021.9655960

Cited by 14 publications

(11 citation statements)

References 17 publications

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“…The majority of studies rely on English Tweet datasets. Due to linguistic and semantic differences between languages, it cannot be inferred that what works well on an English dataset, works to the same degree on a non-English dataset [25], [134]. This is particularly relevant in the context of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

“…To the best of our knowledge, there exists no such literature review. Existing literature reviews focused on multiple diseases at an early stage of COVID-19 [24], investigated different social media platforms, or had a single focus, such as vaccination [25], [26]. We examine the classification techniques used for sentiment analysis to a greater extent and, thereby, aim at accelerating the development of new and better approaches that can support governments and health authorities in their daily fight against COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

et al. 2023

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On Twitter, COVID-19 is a highly discussed topic. People worldwide have used Twitter to express their viewpoints and feelings during the pandemic. Previous research has focused on particular topics such as the public's sentiment during the lockdown, their opinion on governmental measures, or their stance towards COVID-19 vaccines. However, until today, there is no comprehensive overview that presents possible areas of application for sentiment analysis of COVID-19 Twitter data. Therefore, this study reveals how sentiment analysis can provide relevant insights for managing the pandemic by applying a behavioral and social science lens. In this context, our systematic literature review focuses on machine learning-based sentiment analysis techniques and compares the best-performing classification algorithms for COVID-19related Twitter data. We performed a search in five databases, which are: IEEE Xplore DL, ScienceDirect, SpringerLink, ACM DL, and AIS Electronic Library. This search resulted in 40 papers published between October 2019 and January 2022 that used sentiment analysis to evaluate the public opinion on COVID-19-related topics, which we further investigated. Our research indicates that the best performing models in terms of accuracy are ensemble models that comprise various machine learning classifiers. Especially BERT and RoBERTa models provide the most promising results when fine-tuned on Twitter data. Our study aims to combine machine learning-based sentiment analysis and insights from social and behavioral science to provide decision-makers and public health experts with guidance on the application of sentiment analysis in the fight against the spread of COVID-19.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

et al. 2023

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“…One of the methods frequently used to collect data is Twint. 27 Tweets were collected using the Twint library, which has no application programming interface (API) restrictions.…”

Section: Methodsmentioning

confidence: 99%

Discussions About COVID-19 Vaccination on Twitter in Turkey: Sentiment Analysis

Özsezer

Mermer²

2022

Disaster med. public health prep.

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Objectives: The present study aims to examine COVID-19 vaccination discussions on Twitter in Turkey and conduct sentiment analysis. Methods: The current study performed sentiment analysis of Twitter data with artificial intelligence (AI)’s Natural Language Processing (NLP) method. The tweets were retrieved retrospectively from March 10, 2020, when the first Covid-19 case was seen in Turkey, to April 18, 2022. 10308 tweets accessed. The data were filtered before analysis due to excessive noise. First, the text is tokenized. Many steps were applied in normalizing texts. Tweets about the COVID-19 vaccines were classified according to basic emotion categories using sentiment analysis. The resulting dataset was used for training and testing machine learning classifiers. Results: It was determined that 7.50% of the tweeters had positive, 0.59% negative, and 91.91% neutral opinions about the COVID-19 vaccination. When the accuracy values of the ML algorithms used in this study were examined, it was seen that the XGB algorithm had higher scores. Conclusions: Three out of four tweets consist of negative and neutral emotions. The responsibility of professional chambers and the public is essential in transforming these neutral and negative feelings into positive ones.

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“…The next preprocessing step was converting all tweet data into lowercase letters (case folding). According to the author [18], the most widely used preprocessing techniques in sentiment analysis of the COVID-19 vaccine are stopword removal and removing punctuation. Stopword removal is a technique to remove irrelevant or potentially irrelevant words [10].…”

Section: Data Preprocessingmentioning

confidence: 99%

Sentiment Analysis and Topic Modelling of The COVID-19 Vaccine in Indonesia on Twitter Social Media Using Word Embedding

Agustiningsih

Utami²,

Alsyaibani³

2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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This study aims to analyze the sentiments of the Indonesian people towards the COVID-19 vaccine on Twitter. Data collection was carried out from September 2020 to June 2021 with the keyword "covid vaccine," which resulted in 262306 tweets. After filtering and cleaning, there are 83384 tweets left. The labeling process was done manually by an expert. The label composition in the data is 35209 tweets of positive sentiment, 41596 tweets of neutral sentiment, and 6579 tweets of negative sentiment. The remaining data is preprocessed using case folding, removing punctuation, stopword removal, stemming, and the application of slang words. The highest number of tweets appeared in January 2021, after Joko Widodo became the first person in Indonesia to receive a vaccine injection. The number of tweets reached 23492 tweets. At the topic modeling stage, measurements were conducted using the Coherence Score. The distribution of the optimal number of topics is 3 topics. The first topic, with a token percentage value of 51.8%, leads to positive sentiment, while the second and third topics, with token percentage values of 24.5% and 23.7%, lead to neutral sentiment. Bidirectional LSTM architecture was implemented to perform sentiment classification. Fasttext and GloVe word embedding was tested to vectorize tweet data. The test accuracy generated by Fasttext word embedding reached 75,7690%, while the test accuracy produced with GloVe word embedding reached 74.7017%. The usage of slang words could not increase the test accuracy in this study. The use of the Modelcheckpoint to monitor model performance during training could produce a model with a slightly higher test accuracy, about 1.07% (in scenario 1 and scenario 6), compared to a model whose performance was monitored using Early Stopping. In future research, it can be tried to apply a lower learning rate to produce better accuracy in a large number of epochs, or it could be by changing the dropout parameter.

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Sentiment Analysis of COVID-19 Vaccine on Twitter Social Media: Systematic Literature Review

Cited by 14 publications

References 17 publications

Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

Discussions About COVID-19 Vaccination on Twitter in Turkey: Sentiment Analysis

Sentiment Analysis and Topic Modelling of The COVID-19 Vaccine in Indonesia on Twitter Social Media Using Word Embedding

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