为解决网络舆情情感倾向性分析中语义理解不足和仅关注情感词典的现状,本文基于OCC模型认知情感角度建立情感规则,对网络舆情中突发事件的微博文本进行情感分类标注作为训练集,并对深度学习中卷积神经网络模型进行训练得到网络舆情情感识别模型.通过对比实验证明OCC情感规则标注使数据集情感分类更加精确,卷积神经网络的识别效果显著优于传统的机器学习方式(SVM),情感识别模型情感最高可达到90.98%的准确率.%From the perspective of cognition, this study uses the OCC model to establish emotion rules to solve the problem of a lack of semantic understanding. This model is based solely on an emotional dictionary during the process of analyz-ing the affective tendency of Internet users. The affective tendencies of micro-blog texts on public opinion regarding an emergency are classified and labeled as the training set, and are used to train a neural model of deep learning to obtain the emotion recognition model of the public opinion on the network. The results indicate that the training set of emotion classifications labeled using the OCC model is more accurate, and that the distinguishing effect of the con-volutional neural network is significantly better than the traditional machine learning method (SVM), achieving 90.98% accuracy.
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