TextCNN based appeal text event classification for postal express delivery industry
To solve the problems of time-consuming,labor-intensive,and inefficient classification and summary of the causes of a large number of appeal incidents by postal security regulatory authorities,a Word2vec and TextCNN combined method is proposed to achieve automatic classification of appeal reasons for a large number of express delivery industry appeal texts.Firstly,the self-collected appeal text is preprocessed and divided into five types:delay,delivery,loss or shortage,damage,and others.Then,Word2vec is used to convert the text into word vectors,and TextCNN model is constructed and trained to obtain a classification model for the appeal text.The experimental results on real data show that this method can effectively classify appeal texts,with an accuracy of 94.05%,a recall rate of 93.03%,and an F1 value of 0.9325.
appeal events in the express delivery industrytext classificationWord2vecTextCNN
NETL
NSTL
万方数据
维普
