基于多输出神经网络的舆情分析指标拟合及优化研究

通过互联网媒介数据构建出完整的互联网舆情指标体系,是进行舆情预测及评估、网络空间治理的基础。然而,由于数据冲突、数据不完整、计算误差、标注失误等诸多问题,严重降低某些指标的可信度。本文根据可信度高低将舆情指标划分为两类,综合多变量数据拟合、主成分分析(PCA)、多输出神经网络等技术,以及基于数据类型的指标评价方法,能够由高可信度指标推导出低可信度指标,并采用新浪微博用户数据进行性别判断实验与用户粉丝量实验。实验结果表明,所推导出的性别准确率高达96. 7%,用户粉丝量的相对绝对误差(RAE)为16%,说明本方法可以构建高可信度舆情指标体系,为舆情指标体系的构建和量化研究奠定基础。

A multi-target stance detection based on Bi-LSTM network with position-weight

In the task of multi-target stance detection,there are problems the mutual influence of content describing different targets,resulting in reduction in accuracy.To solve this problem,a multi-target stance detection algorithm based on a bidirectional long short-term memory(Bi-LSTM)network with position-weight is proposed.First,the corresponding position of the target in the input text is calculated with the ultimate position-weight vector.Next,the position information and output from the Bi-LSTM layer are fused by the position-weight fusion layer.Finally,the stances of different targets are predicted using the LSTM network and softmax classification.The multi-target stance detection corpus of the American election in 2016 is used to validate the proposed method.The results demonstrate that the Bi-LSTM network with position-weight achieves an advantage of 1.4%in macro average F1 value in the comparison of recent algorithms.