关键词: 重大事件, 趋势预测, 主题模型, 事件抽取, 特征融合

Abstract:

To overcome the feature selection limitation in the research field of mega-event trend prediction based on massive public news data, based on former researchesand artificial intelligence technologies, it proposes a trend prediction method for mega-event by fusing semantics and event characteristics. First, it uses web crawler technology to assist in capturing relevant news data. Second, it uses topic model and technology of event extraction to assist in feature set construction and vector representation, because the high frequency noise word contained by news data may weak the feature capturing ability of traditional LDA topic model, it proposes an improved topic model IDFLDA. Finally, it uses a machine learning classification model to output prediction result. Taking North Korea’s nuclear behavior trend prediction as the research object to verify this method, the experimental results show that the prediction performance of this method is better than the traditional method based on expert knowledge for feature set construction. It can effectively predict the trend of mega-events and provide support for strategic decision-making.

Key words: mega-event, trend prediction, topic model, event extraction, feature fusion