Evaluating Public Sentiment of Covid-19 Vaccine Tweets Using Machine Learning Techniques

Akpatsa, Samuel Kofi; Li, Xiaoyü; Lei, Hang; Obeng, Victor-Hillary Kofi Setornyo

doi:10.31449/inf.v46i1.3483

Cited by 9 publications

(8 citation statements)

References 21 publications

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“…Then, the Multinomial Naive Bayes and Linear Support Vector Classifier algorithms were applied to perform the classification [58]. Authors in [59] , [60] studied COVID-19 and recognized the strange situation of pressure that was considered on each country to establish plans to manage the population and utilize the existing resources in more convenient way. The data was collected from Twitter based on hashtags that including COVID-19, coronavirus, new case, deaths, recovered, and so on.…”

Section: Recent Techniques In Sentiment Analysismentioning

confidence: 99%

A Review and Comparative Analysis of Sentiment Analysis Techniques

Al-Otaibi

Al-Rasheed

2022

IJCAI

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Social networking platforms have become a major source of information, which covers a wide range of topics and has gained a large volume of usage by people around the world. Platforms such as Twitter, Facebook, Instagram, and LinkedIn have attracted huge numbers of users who create public profiles and communicate with other users in the network. They exchange videos, posts, and comments. Social networking requires appropriate techniques to analyze the huge amount of complex, and frequently updated data generated. Sentiment Analysis is one such method of handling this vast volume of data and extracting useful knowledge from it. Social networking contents are analyzed using different techniques to gain insight from this data and use it in decision-making processes. The aim of this work is to study the sentiment analysis concept and present state-of-the-art techniques as well as provide a comparative study of these techniques. Povzetek: Pregledni članek opisuje metode za analizo mnenj v socialnih omrežij tipa Twitter, Faceook, Instagram in LinkedIn.

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Section: Recent Techniques In Sentiment Analysismentioning

confidence: 99%

A Review and Comparative Analysis of Sentiment Analysis Techniques

Al-Otaibi

Al-Rasheed

2022

IJCAI

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“…The results obtained from their approach show that public opinions tend towards getting the vaccine. Lately, Akpatsa et al, [ 38 ] have studied and evaluated the public opinions of vaccine tweets. They applied different machine learning algorithms namely SVM, LR, RF, and NB to classify tweets on positive and negative reactions of people towards the COVID-19 vaccine.…”

Section: Related Workmentioning

confidence: 99%

“…e results obtained from their approach show that public opinions tend towards getting the vaccine. Lately, Akpatsa et al, [38] have studied and evaluated the public opinions of vaccine tweets.…”

Section: Covid-19-related Classification Workmentioning

confidence: 99%

COVID-19 Outbreak Forecasting Based on Vaccine Rates and Tweets Classification

Didi

Walha

Halima

et al. 2022

Computational Intelligence and Neuroscience

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The spread of COVID-19 has affected more than 200 countries and has caused serious public health concerns. The infected cases are on the increase despite the effectiveness of the vaccines. An efficient and quick surveillance system for COVID-19 can help healthcare decision-makers to contain the virus spread. In this study, we developed a novel framework using machine learning (ML) models capable of detecting COVID-19 accurately at an early stage. To estimate the risks, many models use social networking sites (SNSs) in tracking the disease outbreak. Twitter is one of the SNSs that is widely used to create an efficient resource for disease real-time analysis and can provide an early warning for health officials. We introduced a pipeline framework of outbreak prediction that incorporates a first-step hybrid method of word embedding for tweet classification. In the second step, we considered the classified tweets with external features such as vaccine rate associated with infected cases passed to machine learning algorithms for daily predictions. Thus, we applied different machine learning models such as the SVM, RF, and LR for classification and the LSTM, Prophet, and SVR for prediction. For the hybrid word embedding techniques, we applied TF-IDF, FastText, and Glove and a combination of the three features to enhance the classification. Furthermore, to improve the forecast performance, we incorporated vaccine data as input together with tweets and confirmed cases. The models’ performance is more than 80% accurate, which shows the reliability of the proposed study.

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“…According to reports, the Support Vector Machine (SVM) classifier is the best match for the dataset with an accuracy of 84.32 percent. This study illustrates how Twitter data and machine learning techniques may be used to analyze the developing public discourse and attitudes on the Covid-19 vaccine deployment campaign [24]. In more recent study, Hutama and Suhartono used the pre-trained transformer multilingual model (XLM-R and mBERT) in conjunction with a BERTopic model as a topic distribution model to categorize Indonesian fake news.…”

Section: Introductionmentioning

confidence: 99%

A Highly Accurate Internet-Based Fake Information Detection Tool for Indonesian Twitter

Arifin

Syaifuddiin

Desriyanti

et al. 2023

IJCAI

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The dissemination of fake information through social media has several harmful effects on the social life of a nation. Indonesia has been afflicted by the dissemination of erroneous information regarding the negative health consequences of vaccination, resulting in widespread unwillingness to undergo immunization. Therefore, it is necessary to combat such misleading information. We developed a web application using machine learning technologies to identify bogus information flowing on Indonesian Twitter. A Passive-Aggressive Classifier and n-gram tokenization are used to handle data. The application test results indicate that the detection accuracy, precision, and recall for 1-3 grams of tokenization are higher than 90%. In addition, the black box approach yields reliable findings for all application functionalities. Povzetek: Opisana je metoda strojnega učenja za zaznavanje lažnih novic za Indonezijo za Twitter.

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Evaluating Public Sentiment of Covid-19 Vaccine Tweets Using Machine Learning Techniques

Cited by 9 publications

References 21 publications

A Review and Comparative Analysis of Sentiment Analysis Techniques

A Review and Comparative Analysis of Sentiment Analysis Techniques

COVID-19 Outbreak Forecasting Based on Vaccine Rates and Tweets Classification

A Highly Accurate Internet-Based Fake Information Detection Tool for Indonesian Twitter

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