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.

翻译：本科学报告提出了一种利用新闻数据集早期预测重大政治事件的新方法论。该方法融合了自然语言处理、图论、团分析及语义关系，以揭示数据中隐藏的预测信号。初始阶段，我们设计了方法的初步版本并在少数事件上进行测试。该分析揭示了初始研究阶段的局限性。随后，我们通过两个关键方式增强了模型：其一，添加过滤步骤，仅保留与政治相关的新闻以供后续处理；其二，调整输入特征，使预警系统对数据中的显著峰值更为敏感。完成改进方法后，我们针对包括美国抗议、乌克兰战争及法国抗议在内的11个事件进行了测试。结果表明，我们的方法相较于基线方法具有优越性。通过有针对性的优化，我们的模型现能基于新闻数据中的细微模式，更早且更准确地预测重大政治事件。