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

The internet offers a powerful medium for expressing opinions, emotions and ideas, using online platforms supported by smartphone usage high penetration. Most posts are textual based can include people’s emotional feelings particular moment or sentiment. Monitoring sentiments opinions is important detecting any excessive triggered citizens which lead to unintended consequences threats national security. Riots civil war, instance, must be addressed due the risk of jeopardizing social stability political security, crucial elements Mining according security domain relevant research topic that enhanced. Mechanisms techniques mine in aspect require significant improvements obtain optimum results. Researchers have noted there strong relationship between emotion, sentiment threats. This study proposes new theoretical framework predicting hybrid technique: combination lexicon-based approach machine learning cyberspace. In proposed framework, Decision Tree, Naive Bayes, Support Vector Machine been deployed as threat classifiers. To validate our an experimental analysis accomplished. performance each technique used experiments reported. this study, reveals Lexicon-based with Tree classifier recorded highest score These findings offer valuable insight ongoing on opinion mining domain.