A New Hybrid Deep Learning Method with Neutrosophic Sets for Social Media Sentiment Analysis of the COVID-19 Vaccine

The COVID-19 pandemic has launched historic public debates on vaccines, and more significantly, on social media posts regarding vaccines. In this study, we investigate how the public thinks about COVID19 vaccinations using state-of-the-art machine learning and deep learning techniques. Our proposed hybrid CNN + LSTM model achieved the best performance (94.7% accuracy, 95% precision, 95% recall, and 95% F1- score) among individual and traditional deep learning models. Deep learning techniques outperform the conventional classifiers in a comparative evaluation of different models, i.e., Attention-based GRU, Bi-LSTM, Bi-GRU, Hybrid Bi-LSTM + GRU, and Logistic Regression (Unigram, Bigram, Trigram). Attention Layer on GRU and Attention Layer on CNN + Bi-LSTM models also performed well, registering accuracy values of 94.62% and 94.37%, respectively. Also, we propose a decision-making model to select the best model under different evaluation matrices. Seven models are evaluated under four evaluation matrices. We use the neutrosophic sets to deal with uncertainty in the evaluation process. The RAM method is used to select the best model. The dataset included over 200,000 tweets that had been preprocessed for sentiment categorization using VADER (Valence Aware Dictionary and Sentiment Reasoner) and Text Blob, feature extraction, and TFIDF. Temporal sentiment analysis also found that while negative sentiment experienced transient peaks in reaction to negative vaccination narratives and disinformation, positive sentiment rose consistently over time and was tied to vaccine rollout efforts and public health messages. The results show how hybrid deep learning models may be used in sentiment analysis, and they give public health officials useful information to combat false information and enhance the targeting of vaccination campaigns.

Abstract licence: CC BY-NC

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.