Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

Abstract

Political security is a critical component of national stability, with growing threats often manifesting in the form of political unrest, terrorism, and cyber-attacks. To effectively mitigate these threats, predictive models are essential to identifying early warning signs from vast amounts of unstructured data, such as social media posts, news reports, and other online content. This paper presents a novel political security threat prediction framework that leverages a hybrid lexicon-based approach, combined with machine learning techniques, to provide timely and accurate threat assessments.
In addition to the lexicon-based method, machine learning algorithms such as support vector machines (SVM), decision trees, and deep learning models are employed to classify and predict political threats. These models are trained on large datasets derived from real-world political events, including public demonstrations, governmental disputes, and acts of violence, ensuring the system is robust and capable of generalizing across various threat scenarios. The integration of these two approaches—lexicon-based sentiment analysis and machine learning—creates a comprehensive framework that enhances prediction capabilities.
The proposed framework is validated through extensive experiments, demonstrating its ability to predict political security threats with high precision and recall. The results indicate that the hybrid approach not only outperforms standalone lexicon-based or machine learning models but also provides explainable insights into the nature of the threats detected. This makes it a valuable tool for government agencies and security analysts in proactive threat mitigation efforts. Future work will focus on refining the model’s performance by incorporating more advanced natural language processing techniques and expanding the dataset to include a broader range of political contexts.