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.

中文翻译：

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自然语言处理的深度结构化学习

网络信息的实时性和传播性使得网络舆情成为越来越重要的食品安全预警资源，但PB（PB）规模增长的数据也给网络舆情的研判带来很大困难，特别是如何从这些数据中提取网络舆情的事件角色，分析舆情评论的情绪倾向。首先，本文以食品安全网络舆情为研究点，将BLSTM与条件随机场有机结合，提出了一种自动标记事件角色的BLSTM-CRF模型。其次，引入食品安全领域基于词汇的Attention机制，利用BLSTM提取距离相关的序列语义特征，利用CNN实现序列语义特征的情感分类。提出了一种用于分析食品安全领域舆论和情绪倾向的Att-BLSTM-CNN模型。最后，基于时间序列，将食品安全事件的角色提取和情绪倾向分析相结合，根据事件的热度和事件的热度，构建食品安全领域的网络舆情预警模型。公众对食品安全舆论事件的情绪强度。