Short text filtering based on gradient boosting decision tree

Abstract

With the rapid development of the Internet, event detection from massive data has become a research hotspot. However, the existing event detection methods for social networks rarely consider the noise data in short text data in detail. Therefore, there is a lot of noise in the input of event detection, resulting in a large number of false-positive events in the event detection results, which affects the efficiency and accuracy of event detection. In this paper, three types of features are proposed to mine text features, and the gradient boosting decision tree algorithm is used. Experiments show that the algorithm has good filtering performance and interpretability for short texts in social networks.