A Pipeline-Based Approach for Enhancing Political Threat Detection Using Machine Learning

Especially on social media and other online platforms, the internet provides a strong venue for the expression of ideas, feelings, and views. The public's sentiments are frequently reflected in these internet messages, which, if unregulated, might cause major problems like rioting or instability, which could affect the safety of the country. In order to avoid any security issues, it is essential to monitor such emotions. A novel approach to predicting political security threats is presented in this project. It combines two methods: (i) lexical analysis, which involves searching for words that typically convey strong emotions like anger or fear, and (ii) machine learning, which involves training computers to identify patterns in data in order to improve threat prediction. Examining online material for emotional content using classifiers such as Decision Tree, Naive Bayes, and Support Vector Machine (SVM) allows the method to detect and anticipate indications of instability. From data processing to prediction, the whole process is automated in this project thanks to the establishment of a pipeline. The data cleaning, model training, and prediction processes are all integrated into one streamlined flow thanks to scikit-learn. To improve outcomes, the project intends to test different algorithm combinations within the pipeline and see which ones work best. In order to avert political instability, it efficiently analyzes internet messages for indicators of disturbance and allows authorities to take swift action.