【摘要】 近几年,食品安全网络舆情事件数量激增,引起了国家的高度重视。目前的食品安全网络舆情预警指标体系对主题属性和传播扩散指标考虑不全面,未深入考虑舆情自身特性和演化规律,而且目前的网络舆情预警模型也不能很好地考虑舆情不同特征之间的相互联系,导致舆情预警准确率不高。针对以上问题,提出包括主题属性、传播扩散等5个维度的指标体系,并在此基础上提出长短时记忆网络Re-LSTM模型,使用正则化方法约束网络中各单元输入权重并用softsign函数替代tanh激活函数。与其他经典模型对比,所构建的模型不仅能够提高预警准确率,而且还能够更好地避免梯度消失和过拟合问题。更多还原

【Abstract】 In recent years, increasing events of internet public opinion on food security have attracted great attention of the Chinese government. The current early warning index system of internet public opinion on food security lacks a comprehensive consideration of theme attributes, propagation and diffusion indexes, and does not consider the inherent characteristics and evolution law of the public opinion in depth. Moreover, as the current early warning model of internet public opinion fails to take the interrelationship between different characteristics of the public opinion into consideration, which leads to the low accuracy of early warning of public opinion. Aiming at the above problems, we construct an index system consisting of five dimensions, including theme attributes and propagation and diffusion indexes. Based on this, we propose a regularization long short term memory(Re-LSTM) model, which uses the regularization method to constrain the input weight of each unit in the network, and replaces the tanh activation function by the softsign activation function. Compared with other classic models, the proposed model can not only improve the accuracy of early warning, but also better avoid the problem of gradient disappearance and overfitting.更多还原