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| Study on Identification and Classification of Emergency News Based on the Combined Deep Learning Model |
| Song Yinghua1,2, Lyu Long1,2, Liu Dan1,2 |
1.China Research Center for Emergency Management, Wuhan University of Technology, Wuhan 430070
2.School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070 |
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Abstract Considering the difference between the keywords in emergency news and general news text, and the existing single consideration of the relationship between words and words and categories based on deep learning news text, we propose a news text classification model based on double input combination deep learning. First, word vector represents the relationship between words and words, discrete vector represents the relationship of words and categories. Second, considering the advantages of the CNN (convolutional neural networks) model learning local spatial feature information, LSTM (long short-term memory) model learning time sequence feature information, and MLP (multilayer perceptron) model learning the relationship between words and categories, we constructed the DCLSTM-MLP deep learning combination model. Finally, this model takes 5,477 emergency news texts with the interrelationship between words and the interrelationship between words and categories, and 2,815 general news texts, and analyzes the performance of the combined model by experimental comparison. The results show that the accuracy, recall rate, and comprehensive value of the first-level emergency identification model all reached 99.55 percent. The accuracy of the second-level emergency classification combination model reached 94.82 percent, and the composite value of the accuracy and recall rate increased by 6.06 percent, 2.36 percent, 2.47 percent, 1.14 percent, and 1.79 percent, respectively, compared with the five models of MLP, Text-CNN, Text-LSTM, CNN-MLP, and CLSTM. Thus, the combined model can implement news text classification tasks more accurately.
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Received: 25 November 2019
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