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

Abstract

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.

Computational Intelligence-Based Model for Exploring Individual Perception on SARS-CoV-2 Vaccine in Saudi Arabia

“…From the collected tweets, it has seen that people in KSA are usually either provaccination (positive class) or neutral, while very few people are against vaccination that is around 12% of the tweets in the dataset. Furthermore, the number of neutral tweets in the current dataset is 49.7% similar to that of Alam et al [ 19 ] and also contains the least number of tweets related to the negative perception of the SARS-CoV-2 vaccines. Similarly, Al-Mohaithef and Padhi [ 20 ] explore the acceptance of the SARS-CoV-2 vaccine in KSA using the questionnaire approach and found that 64.7% of people accept the vaccination from four main cities of KSA.…”

“…Furthermore, Alam et al [ 19 ] proposed a Deep Learning model for the sentiment analysis of the SARS-CoV-2 vaccination using Kaggle tweet dataset. Initially the polarity of the tweets was calculated using the dictionary approach.…”

COVID-19 vaccine sentiment analysis using public opinions on Twitter

“…Alam et al [9] , they have used valence aware dictionary for sentiment reasoned, a natural language processing (NLP) tool, was used to assess people's feelings towards certain vaccines (VADER). They were able to illustrate the entire scenario by grouping the received attitudes into three categories (positive, negative, and neutral).…”

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

“…Deep-learning-based algorithms are becoming more prevalent in medical image analysis. Deep-learning-based models have been shown to perform well in different tasks like object detection, sentiment analysis [ 14 ], medical image classification [ 15 ], and disease detection [ 16 ]. The automatic identification of illnesses is a key step in reducing an ophthalmologist's workload.…”