基于社交网络数据的交通突发事件识别方法

为了从社交网络数据中挖掘出交通突发事件，研究了基于机器学习的文本识别方法。通过关键词和地点定位，利用网页爬虫“Beautiful Soup”爬取到原始文本。采用正则匹配、重复度计算以及“0-1”标记预处理原始文本。基于预处理后文本特征，研究了基于特征权重的特征词选取方法；其中，特征权重的计算综合了词语的出现频率和含有该词语的文本所占比例，通过将二者归一化并加权合并，获得训练集突发事件文本中各个无重复词语的特征权重；依据此值选择确定特征词，并用于后续分类器的输入。测试对比了不同的分类器以及特征词选择方法，结果表明，所提特征词选取方法与XGBoost分类器结合，在交通突发事件识别上具有最好的综合表现，精确率为0.679 6，召回率为0.648 1，F1值为0.663 5，AUC值为0.759 4。 

A Method to Identify Traffic Incidents Based on Social Network Data

A text classification method based on machine learning is studied to identify traffic incidents by mining the data from the social networks. The original texts are crawled by web crawler"Beautiful Soup"based on the keywords and location. These texts are preprocessed using regular expression matching, duplicate removing, and"0-1"mark? ing. According to the features of preprocessed texts, the paper proposes a method to select feature words based on fea? ture weights. The feature weight is calculated by normalizing, weighting, and combining the word frequency and the ratio of the text containing that word. Accordingly, the feature weight of each unique word in the training set of the traf? fic incident text can be achieved, used as a criterion for selecting feature words, and as the inputs of classifiers. A test is conducted to compare different classifiers and methods to select feature words. The results show that the proposed method to select feature words combined with the XGBoost classifier has the optimal performance, with a precision rate of 0.679 6, a recall rate of 0.648 1, an F1 value of 0.663 5, and an AUC value of 0.759 4.