Correlation Analysis and Text Classification of Chemical Accident Cases Based on Word Embedding

Word embedding and deep learning methods have shown to be very effective in automated text information mining. In this work, a chemical accident case analysis method is proposed and tested based on the methods. Firstly, word vectors for the text corpus of chemical accident cases were produced based on word2vec. And then Bidirectional LSTM model with attention mechanism for text classification was constructed. Finally, case studies on the correlation analysis of the common trends of chemical accidents and automated text classification of chemical accident cases documents were performed respectively. The results revealed that the proposed method can identify common principles of chemical accidents and classify chemical accident cases. These findings highlight the feasibility of chemical accident case information mining based on word embedding through case studies. Compared with rule-based information mining, our method improves the efficiency, automation, and intelligence of information mining from chemical accident cases documents.