  This scientific report presents a novel methodology for the early prediction
of important political events using News datasets. The methodology leverages
natural language processing, graph theory, clique analysis, and semantic
relationships to uncover hidden predictive signals within the data. Initially,
we designed a preliminary version of the method and tested it on a few events.
This analysis revealed limitations in the initial research phase. We then
enhanced the model in two key ways: first, we added a filtration step to only
consider politically relevant news before further processing; second, we
adjusted the input features to make the alert system more sensitive to
significant spikes in the data. After finalizing the improved methodology, we
tested it on eleven events including US protests, the Ukraine war, and French
protests. Results demonstrate the superiority of our approach compared to
baseline methods. Through targeted refinements, our model can now provide
earlier and more accurate predictions of major political events based on subtle
patterns in news data.
