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Sentiment analysis becomes one of the most active research hotspots in the field of natural language processing tasks in recent years. However, the inability to fully and effectively use emotional information is a problem in present deep learning models. A single Chinese character has different meanings in different words, and the character embeddings are combined with the word embeddings to extract more precise meaning information. In this paper, a single Chinese character and word are used as input units to train. Based on BLSTM, the attention mechanism based on vocabulary semantics in food field is introduced to realize distance-related sequence semantic feature extraction. CNN is used to realize semantic sentiment classification of sequence semantic features. Therefore, a model based on multi-neural network for sentiment information extraction and analysis is proposed. Experiments show that the model has excellent characteristics in sentiment analysis and obtains high accuracy and F value.

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The real-time and dissemination characteristics of network information make net-mediated public opinion become more and more important food safety early warning resources, but the data of petabyte (PB) scale growth also bring great difficulties to the research and judgment of network public opinion, especially how to extract the event role of network public opinion from these data and analyze the sentiment tendency of public opinion comment. First, this article takes the public opinion of food safety network as the research point, and a BLSTM-CRF model for automatically marking the role of event is proposed by combining BLSTM and conditional random field organically. Second, the Attention mechanism based on vocabulary in the field of food safety is introduced, the distance-related sequence semantic features are extracted by BLSTM, and the emotional classification of sequence semantic features is realized by using CNN. A kind of Att-BLSTM-CNN model for the analysis of public opinion and emotional tendency in the field of food safety is proposed. Finally, based on the time series, this article combines the role extraction of food safety events and the analysis of emotional tendency and constructs a net-mediated public opinion early warning model in the field of food safety according to the heat of the event and the emotional intensity of the public to food safety public opinion events.