HSAM: Hybrid Sentiment Analysis Model for COVID-19 Contact Tracing Applications

To understand the public’s perception of COVID-19 tracing applications, previous studies were primarily based on exploratory research, surveys or machine learning methods, which are semantically weak and time-consuming. To increase the reliability of this analytical methodology, hybrid-based Twitter sentiment analysis can be applied. In this paper, we propose a hybrid model for sentiment analysis by using Valence Aware Dictionary for Sentiment Reasoning (VADER) + Support Vector Machine (SVM). We demonstrate from the numerical analysis that a VADER and SVM-based hybrid model provides the best performance with 82.3% accuracy, 0.84 precision, 0.83 recall and 0.82 F1-score. The use of hybrid-based methods is shown to be effective in analysing the public’s perception towards COVID-19 contact tracing applications using tweets collected from the UK, USA and India. Positive responses clearly outweighed negatives responses towards contact tracing, but this was contradicted by the low uptake of apps in all three nations. Our analysis, however, showed that neutral responses were 52% of the collected tweets; these tweets did not express positive or negative opinions, and subsequent tweets from the same users could not be verified, thus limiting the number of analyzed tweets available.