Application of Text Analysis Technology in Aviation Safety Information Analysis

Abstract

This research is aimed to obtain potential safety hazards quickly, accurately, and efficiently from a large amount of daily collected aviation safety information, and provide a clear improvement direction for safety risk control. By combing with the text analysis and machine learning, clustered a given type of aviation safety information according to its content is an important basis of mining information effectively. Taken the system failure/jam/fault events collected by Chinese civil aviation in 2017 as a sample. By means of text pre-processing, feature extraction by using logarithmic IF-IDF and k-means method under the environment of python3.6, an automatic clustering model of the sample information is established, and then the visualized results are output based on Multi Dimensional Scale (MDS). The analysis results show that text clustering and visualization can quickly and automatically file information, identify the similarity among sample information, easily lock key information, and provide targeted measures for the next step of risk management and control.