Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning

Aygün, İrfan; Kaya, Buket; Kaya, Mehmet

doi:10.1109/jbhi.2021.3133103

Cited by 59 publications

(37 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature selection is a standard procedure to filter or select the important attributes that determine the sentiments of the public [ 25 ], and we further pinpointed the sentiment determinants related to the vaccine booster by using multivariance logistics regression. This study’s procedures are standard across other infectious diseases [ 15 , 16 , 25 ], and the results of our model showing two representative attributes—“pfizer” and “mix”—were supported by Ahmed et al [ 13 ], Aygun et al [ 16 ], Marcec et al [ 17 ], etc., in that the brand of vaccine played a crucial role when the public plan was to administer an additional vaccine booster.…”

Section: Discussionsupporting

confidence: 80%

“…Ansari et al [ 15 ] used COVID-19 vaccine related tweets and conducted sentiment analysis to uncover the latest information on the effect of location and gender on the current vaccination. Aygun et al [ 16 ] used aspect-based sentiment analysis for Twitter users from the USA, UK, Canada, Turkey, France, Germany, Spain, and Italy and used four different aspects (policy, health, media, and other) and four different BERT models (mBERT-base, BioBERT, ClinicalBERT, and BERTurk) to understand peoples’ views about vaccination and types of vaccines. Marcec et al [ 17 ] retrieved all English-language tweets mentioning AstraZeneca/Oxford and Pfizer/BioNTech and conducted sentiment analysis using the AFINN lexicon to calculate the daily average sentiment of tweets to understand the sentiment of tweets on each vaccine.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia

2022

View full text Add to dashboard Cite

Vaccination is the primary preventive measure against the COVID-19 infection, and an additional vaccine dosage is crucial to increase the immunity level of the community. However, public bias, as reflected on social media, may have a significant impact on the vaccination program. We aim to investigate the attitudes to the COVID-19 vaccination booster in Malaysia by using sentiment analysis. We retrieved 788 tweets containing COVID-19 vaccine booster keywords and identified the common topics discussed in tweets that related to the booster by using latent Dirichlet allocation (LDA) and performed sentiment analysis to understand the determinants for the sentiments to receiving the vaccination booster in Malaysia. We identified three important LDA topics: (1) type of vaccination booster; (2) effects of vaccination booster; (3) vaccination program operation. The type of vaccination further transformed into attributes of “az”, “pfizer”, “sinovac”, and “mix” for determinants’ assessments. Effect and type of vaccine booster associated stronger than program operation topic for the sentiments, and “pfizer” and “mix” were the strongest determinants of the tweet’s sentiments after the Boruta feature selection and validated from the performance of regression analysis. This study provided a comprehensive workflow to retrieve and identify important healthcare topic from social media.

show abstract

Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia

2022

View full text Add to dashboard Cite

show abstract

“…However, when lay language is processed in this domain, BERT's performance may still be superior to the specially trained language models. For example, when BERT was used to understand people's opinion towards vaccination, a multilingual BERT model outperformed both BioBERT and ClinicalBERT [32]. BERT was successfully fine-tuned to perform SA of drug reviews [33], albeit without focusing on specific aspects.…”

Section: Related Workmentioning

confidence: 99%

The Case of Aspect in Sentiment Analysis: Seeking Attention or Co-Dependency?

Žunić

Corcoran

Spasić

2022

MAKE

View full text Add to dashboard Cite

(1) Background: Aspect-based sentiment analysis (SA) is a natural language processing task, the aim of which is to classify the sentiment associated with a specific aspect of a written text. The performance of SA methods applied to texts related to health and well-being lags behind that of other domains. (2) Methods: In this study, we present an approach to aspect-based SA of drug reviews. Specifically, we analysed signs and symptoms, which were extracted automatically using the Unified Medical Language System. This information was then passed onto the BERT language model, which was extended by two layers to fine-tune the model for aspect-based SA. The interpretability of the model was analysed using an axiomatic attribution method. We performed a correlation analysis between the attribution scores and syntactic dependencies. (3) Results: Our fine-tuned model achieved accuracy of approximately 95% on a well-balanced test set. It outperformed our previous approach, which used syntactic information to guide the operation of a neural network and achieved an accuracy of approximately 82%. (4) Conclusions: We demonstrated that a BERT-based model of SA overcomes the negative bias associated with health-related aspects and closes the performance gap against the state-of-the-art in other domains.

show abstract

“…( 2021 ) 2021 Understanding public perceptions of COVID-19 vaccines Manual annotation ML/DL 5000 Tweets Nov20–Jan21 No English TF-IDF BERT, LR, RF, SVM N/A Aygün et al. ( 2021 ) 2021 Analyzing public sentiments related to COVID-19 vaccines Aspect-Based DL 928,402 Tweets Nov20–Mar21 No English, Turkish TF-IDF, Word2Vec BERT 8 Countries Yang and Sornlertlamvanich ( 2021 ) 2021 Investigating Public Perception of COVID-19 Vaccine TextBlob ML 190,000 Tweets Dec20–Jun21 No English, Japanese BoW Naïve Bayes Japan, USA, UK Jayasurya et al. ( 2021 ) 2021 Analyzing of Public Sentiment on COVID-19 Vaccination VADER ML 431,986 Tweets Feb20–Apr21 Yes English TF-IDF 14 Models N/A Cotfas et al.…”

Section: Related Workmentioning

confidence: 99%

Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

Aljedaani

Abuhaimed

Rustam

et al. 2022

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

Introduction The development of COVID-19 vaccines has been a great relief in many countries that have been affected by the pandemic. As a result, many governments have made significant efforts to purchase and administer vaccines to their populations. However, accommodating such vaccines is typically confronted with people’s reluctance and fear. Like any other important event, COVID-19 vaccines have attracted people’s discussions on social media and impacted their opinions about vaccination. Objective The goal of this study is twofold: First, it conducts a sentiment analysis around COVID-19 vaccines by automatically analyzing Arabic users’ tweets. This analysis has been spread over time to better capture the changes in vaccine perceptions. This will provide us with some insights into the most popular and accepted vaccine(s) in the Arab countries, as well as the reasons behind people’s reluctance to take the vaccine. Second, it develops models to detect any vaccine-related tweets, to help with gathering all information related to people’s perception of the virus, and potentially detecting vaccine-related tweets that are not necessarily tagged with the virus’s main hashtags. Methods Arabic Tweets were collected by the authors, starting from January 1st, 2021, until April 20th, 2021. We deployed various Natural Language Processing (NLP) to distill our selected tweets. The curated dataset included in the analysis consisted of 1,098,376 unique tweets. To achieve the first goal, we designed state-of-the-art sentiment analysis techniques to extract knowledge related to the degree of acceptance of all existing vaccines and what are the main obstacles preventing the wide audience from accepting them. To achieve the second goal, we tackle the detection of vaccine-related tweets as a binary classification problem, where various Machine Learning (ML) models were designed to identify such tweets regardless of whether they use the vaccine hashtags or not. Results Generally, we found that the highest positive sentiments were registered for Pfizer-BioNTech, followed by Sinopharm-BIBP and Oxford-AstraZeneca. In addition, we found that 38% of the overall tweets showed negative sentiment, and only 12% had a positive sentiment. It is important to note that the majority of the sentiments vary between neutral and negative, showing the lack of conviction of the importance of vaccination among the large majority of tweeters. This paper extracts the top concerns raised by the tweets and advocates for taking them into account when advertising for the vaccination. Regarding the identification of vaccine-related tweets, the Logistic Regression model scored the highest accuracy of 0.82. Our findings are concluded with implications for public health authorities and the scholarly community to take into account to improve the vaccine’s acceptance.

show abstract

Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning

Cited by 59 publications

References 24 publications

Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia

Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia

The Case of Aspect in Sentiment Analysis: Seeking Attention or Co-Dependency?

Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

Contact Info

Product

Resources

About