COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets.

Popular social media platforms, such as Twitter, have become an excellent source of information with their swift information dissemination. Individuals with different backgrounds convey their opinions through social media platforms. Consequently, these platforms have become a profound instrument for collecting enormous datasets. We believe that compiling, organizing, exploring, and analyzing data from social media platforms, such as Twitter, can offer various perspectives to public health organizations and decision makers in identifying factors that contribute to vaccine hesitancy. In this study, public tweets were downloaded daily from Tweeter using the Tweeter API. Before performing computation, the tweets were preprocessed and labeled. Vocabulary normalization was based on stemming and lemmatization. The NRCLexicon technique was deployed to convert the tweets into ten classes: positive sentiment, negative sentiment, and eight basic emotions (joy, trust, fear, surprise, anticipation, anger, disgust, and sadness). t-test was used to check the statistical significance of the relationships among the basic emotions. Our analysis shows that the p-values of joy-sadness, trust-disgust, fear-anger, surprise-anticipation, and negative-positive relations are close to zero. Finally, neural network architectures, including 1DCNN, LSTM, Multiple-Layer Perceptron, and BERT, were trained and tested in a COVID-19 multi-classification of sentiments and emotions (positive, negative, joy, sadness, trust, disgust, fear, anger, surprise, and anticipation). Our experiment attained an accuracy of 88.6% for 1DCNN at 1744 s, 89.93% accuracy for LSTM at 27,597 s, while MLP achieved an accuracy of 84.78% at 203 s. The study results show that the BERT model performed the best, with an accuracy of 96.71% at 8429 s.

Covid-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset.

In 2019 there was an outbreak of coronavirus pandemic also known as COVID-19. Many scientists believe that the pandemic originated from Wuhan, China, before spreading to other parts of the globe. To reduce the spread of the disease, decision makers encouraged measures such as hand washing, face masking, and social distancing. In early 2021, some countries including the United States began administering COVID-19 vaccines. Vaccination brought a relief to the public; it also generated a lot of debates from anti-vaccine and pro-vaccine groups. The controversy and debate surrounding COVID-19 vaccine influenced the decision of several people in either to accept or reject vaccination. Because of data limitations, social media data, collected through live streaming public tweets using an Application Programming Interface (API) search, is considered a viable and reliable resource to study the opinion of the public on Covid-19 vaccine hesitancy. Thus, this study examines 3 sentiment computation methods (Azure Machine Learning, VADER, and TextBlob) to analyze COVID-19 vaccine hesitancy. Five learning algorithms (Random Forest, Logistics Regression, Decision Tree, LinearSVC, and Naïve Bayes) with different combination of three vectorization methods (Doc2Vec, CountVectorizer, and TF-IDF) were deployed. Vocabulary normalization was threefold; potter stemming, lemmatization, and potter stemming with lemmatization. For each vocabulary normalization strategy, we designed, developed, and evaluated 42 models. The study shows that Covid-19 vaccine hesitancy slowly decreases over time; suggesting that the public gradually feels warm and optimistic about COVID-19 vaccination. Moreover, combining potter stemming and lemmatization increased model performances. Finally, the result of our experiment shows that TextBlob + TF-IDF + LinearSVC has the best performance in classifying public sentiment into positive, neutral, or negative with an accuracy, precision, recall and F1 score of 0.96752, 0.96921, 0.92807 and 0.94702 respectively. It means that the best performance was achieved when using TextBlob sentiment score, with TF-IDF vectorization and LinearSVC classification model. We also found out that combining two vectorizations (CountVectorizer and TF-IDF) decreases model accuracy.