Computer Science ›› 2016, Vol. 43 ›› Issue (12): 277-280.doi: 10.11896/j.issn.1002-137X.2016.12.051

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Sentiment Analysis on Food Safety News Using Joint Deep Neural Network Model

LIU Jin-shuo and ZHANG Zhi   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Facing the difficulties in feature expression of Chinese food safety text information,and the loss of semantic information with low classification accuracy,a sentimental text classification model based on joint deep neural network was presented.The proposed model utilizes the corpora of food safety document captured from the internet,and word vector from word embedding method as the input for the neural network to get the pre-trained word vector.The pre-trained word vector is further trained dynamically to get the word features and the sentimental classification of the sentence result,which better express the phrase-level sentimental relations for each sentence and the real semantic meaning in the food safety domain.Then the word feature of the sentence is inputted to the recurrent neural network (RNN) to catch the semantic information of the sentence structure further,realizing the sentimental classification of the text.The experiments show that our joint deep neural network model achieves better results in sentiment analysis on food safety information,compared with the bag-of-words based SVM model.The classification accuracy and F1 value reach 86.7% and 85.9% respectively.

Key words: Joint deep neural networks model,Multi-dimensional textual features,Word-embedding,Food safety,Sentiment analysis

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