Sentiment Analysis of Social Media Content Using Deep Learning for Enhanced Situational Awareness and Risk Monitoring

Abstract

Social media platforms contain a wealth of data that can provide valuable insights into public sentiment, trends, and emerging risks. Sentiment analysis aims to computationally determine the attitude, emotions, and opinions expressed in text. Deep learning methods for sentiment analysis have shown promising results in recent years due to their ability to understand semantic and contextual information. This paper explores the use of deep learning techniques for sentiment analysis of social media content to enhance situational awareness and risk monitoring capabilities. A systematic literature review identifies current state-of-the-art methods, key challenges, and opportunities for future work. Comparative experiments are conducted using deep learning architectures including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and BERT-based models. Results indicate that fine-tuned BERT models achieve the highest accuracy for multiclass sentiment classification across Facebook, Twitter, and Reddit datasets. An integrative framework is proposed for real-time monitoring of social platforms using sentiment analysis to extract actionable insights and early warning signals. The paper concludes with an analysis of limitations and an outlook for further research to develop more flexible and generalizable approaches. Enhanced situational awareness through sentiment analysis could provide invaluable support for security, policymaking, governance, and risk management across domains.