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\begin{document}

\title{HSAM: Hybrid Sentiment Analysis Model for COVID-19 Contact Tracing
Applications}



\author[1]{Raghubir Singh }%
\author[2]{Neeraj Kumar }%
\affil[1]{University of Bath}%
\affil[2]{Affiliation not available}%


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  \date{May 22, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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