Sentiment Analysis of Textual Data in Social Networks: A Survey of Existing Approaches
TL;DR
Examination of sentiment analysis methods applied to social media text data, covering lexicon-based, machine learning, and deep learning approaches, including transformerbased architectures, as well as widely used datasets, shows how sentiment analysis can be applied to the detection of threats that exploit human emotions.
Abstract
The growth of social media platforms results in billions of user-generated messages daily, making automated text analysis critical. The global impact of disinformation, the evolution of cyber threats toward psychological tactics, and the growing political and commercial value of public opinion have made sentiment analysis an important and relevant area of research. Although existing reviews have examined sentiment analysis approaches in terms of methods and application areas, few have evaluated these approaches in the context of cyber threat detection. This paper examines sentiment analysis methods applied to social media text data, covering lexicon-based, machine learning, and deep learning approaches, including transformerbased architectures, as well as widely used datasets. The paper also discusses how sentiment analysis can be applied to the detection of threats that exploit human emotions, including phishing, disinformation, and social engineering. To support this, an empirical analysis of large language model performance is conducted, measuring their ability to detect emotionally manipulative content. The purpose of this paper is to provide readers with an objective understanding of sentiment analysis and its role as a defense against socially engineered cyber threats.