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\begin{document}

\title{Sarcasm-based Tweet-level Stress Detection}



\author[1]{Prashanth KVTKN}%
\author[1]{Tene Ramakrishnudu}%
\affil[1]{National Institute of Technology Warangal}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\begin{abstract}
Psychological stress has evolved as an important health concern across
the globe. The vulnerability to stress and the ramifications of it have
only worsened during the time of the COVID-19 pandemic. This
necessitates a timely diagnosis of stress before the condition
progresses to chronicity. In this context, the popularity of social
media like Twitter, where large numbers of users share opinions without
any social stigma, has emerged as a major resource of human opinions.
This has led to an increased research interest in social media-based
stress detection techniques. However, tweet-level stress detection
techniques in the literature have left a void in leveraging the text
information in tweets, especially the presence of sarcastic expressions
in the tweet's text content. To this end, a novel method called
\emph{``Sarcasm-based Tweet-Level Stress Detection''} (STSD) is proposed
in this work with the modification of the logistic loss function to
detect tweet-level stress by availing the information of sarcasm that
exists in the tweet-content. The principle of the STSD model is to
minimise the loss for non-sarcastic tweets while maximising the loss for
sarcastic tweets. Furthermore, an extensive preprocessing and
dimensionality reduction is performed using \emph{kernel principal
component analysis} (kernel PCA) to improve the performance by reducing
the dimensions. The experimental results show that the proposed STSD
model, when applied along with kernel PCA, records a significant
improvement in accuracy by a minimum of 5.25\% and a maximum of 9.19\%
over baseline models. Also, there is an increment in F1-score by at
least 0.085 points and a maximum of 0.164 points when compared to the
baseline models.%
\end{abstract}%



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