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. The hybrid lexicon-based approach utilizes a curated dictionary of politically sensitive terms and phrases, augmented with sentiment analysis to capture the intensity and nature of online discourse related to political security. This lexicon is continuously updated through the use of unsupervised learning techniques, which allow the framework to adapt to new terms and evolving language patterns that may signal emerging threats. By incorporating both traditional lexicon-based sentiment analysis and machine learning models, the framework is able to achieve greater accuracy in identifying potential risks. 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