Food safety news text classification based on EWord2Vec-TextCNN-SE
With the development of the Internet and social media,food safety-related news data has exploded rapidly,which poses new challenges for the screening and classification of the related information.In order to enhance the semantic information capturing ability and new word processing ability,an EWord2Vec-TextCNN-SE classification model is proposed.The proposed model uses the enhanced Word2Vec method to model Chinese vocabulary at the word level,combining the semantic advantages of word segmentation and the fine-grained advantages of character-level processing for word embedding training;then,by introducing the SE attention module.The proposed model improves the ability to focus on global information and improves text classification performance effectively.Through comparative experiments with other models,the results show that the accuracy of the EWord2Vec-TextCNN-SE model in the food safety news data set reached 91.07%,and the macro F1 value reached 91.29%,which significantly outperforms other models in solving food safety news classification and has gread advantage on this issue.
food safetynews text classificationWord2VecTextCNN
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