Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Villavicencio, Charlyn Nayve; Macrohon, Julio Jerison E.; Inbaraj, X. Alphonse; Jeng, Jyh-Horng; Hsieh, Jer‐Guang

doi:10.3390/info12050204

Cited by 138 publications

(84 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…After labeling and preparing the data using Twitter crawling and validating them with 10-fold crossvalidation, a support vector machine (SVM), the naive Bayes algorithm, and random forest were used to evaluate the performances and obtain results with the proper labeled predictions. The authors of this study [ 13 ] collected information on the Filipino citizens' feelings about the efforts of the Philippine government using the Twitter web. For the government to analyze these responses, NLP techniques were applied to understand the overall sentiment.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Alam

Khan

Dhruba

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people’s minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Alam

Khan

Dhruba

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Previous studies have explored sentiment surrounding COVID-19 vaccinations on Twitter [ 72 , 73 ]. Xue et al (2020) used Latent Dirichlet Allocation (LDA)—a machine learning approach—and collected four million tweets on COVID-19 using 25 search words.…”

Section: Discussionmentioning

confidence: 99%

Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis

Roe

Lowe

Williams

et al. 2021

IJERPH

View full text Add to dashboard Cite

Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter’s Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.

show abstract

“…There have been several works related to analyzing the Twitter dataset on different topics during the COVID-19 pandemic [12][13][14][15]. Only a few studies focus on the Twitter data related to COVID-19 vaccination [16,17].…”

Section: Related Workmentioning

confidence: 99%

“…Their result show that Twitter users reveal fear when tweeting about COVID-19 new cases or death rather than trust. Pano and Kashef [14] presented sentiment analysis on tweets related to bitcoins during COVID-19 pandemic using VADER. They compared 13 different text preprocessing strategies for correlating the sentiment scores of the tweets with bitcoin price.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Vaccination Awareness and Aftermath: Public Sentiment Analysis on Twitter Data and Vaccinated Population Prediction in the USA

Sattar

Arifuzzaman

2021

Applied Sciences

View full text Add to dashboard Cite

Social media, such as Twitter, is a source of exchanging information and opinion on global issues such as COVID-19 pandemic. In this study, we work with a database of around 1.2 million tweets collected across five weeks of April–May 2021 to draw conclusions about public sentiments towards the vaccination outlook when vaccinations become widely available to the population during the COVID-19 pandemic. We deploy natural language processing and sentiment analysis techniques to reveal insights about COVID-19 vaccination awareness among the public. Our results show that people have positive sentiments towards taking COVID-19 vaccines instead of some adverse effects of some of the vaccines. We also analyze people’s attitude towards the safety measures of COVID-19 after receiving the vaccines. Again, the positive sentiment is higher than that of negative in terms of maintaining safety measures against COVID-19 among the vaccinated population. We also project that around 62.44% and 48% of the US population will get at least one dose of vaccine and be fully vaccinated, respectively, by the end of July 2021 according to our forecast model. This study will help to understand public reaction and aid the policymakers to project the vaccination campaign as well as health and safety measures in the ongoing global health crisis.

show abstract

Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Cited by 138 publications

References 13 publications

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis

COVID-19 Vaccination Awareness and Aftermath: Public Sentiment Analysis on Twitter Data and Vaccinated Population Prediction in the USA

Contact Info

Product

Resources

About