| dc.description.abstract | The proliferation of user-generated content on social media platforms has enabled new
opportunities for computational emotion and threat analysis using machine learning
(ML) techniques. This paper presents a comprehensive systematic review of recent
advancements in detecting emotional states and threats from social media data, covering
literature published between 2019 and 2025. The review follows PRISMA guidelines
and includes ten peer reviewed studies selected from an initial pool of 162 records
across databases such as IEEE Xplore, Google Scholar, Elsevier, and SpringerLink. The
included works are categorized into three domains: emotion detection, threat and crisis
detection, and hybrid emotion-aware applications such as hate speech classification and
social signal processing. The analysis reveals that deep learning models, particularly
CNN, Bi-LSTM, GRU, and transformer-based architectures like BERT, significantly
outperform traditional methods in emotion classification and threat recognition tasks.
Several works have focused on leveraging machine learning (ML) and deep learning
(DL) models to detect emotional expressions in online content. A real time emotion
detection framework Smart Mood Detection using deep neural networks to enhance
human computer interaction. Integration of emotion features enhances detection of
cyber threats and hate speech, while optimization algorithms further improve accuracy
and generalizability. Key challenges identified include dataset imbalance, lack of
multilingual resources, limited explainability, and real-time deployment barriers. This
review synthesizes current methodologies, highlights gaps, and offers a research agenda
for building intelligent, emotion-aware systems for social media-based threat detection. | en_US |
