An analysis of emotional tendency under the network public opinion: Deep learning

Abstract

Network public opinion refers to the common opinion with tendency and influence formed by the public on certain social events through the Internet. Due to the complexity of interest relations, network public opinion is likely to cause difficulties for individuals, enterprises, or governments. To control the public's emotional tendency to social events, this study designed an OCC sentiment rule system to label the network public opinion case base. The text representation method is Word2Vec in deep learning, and the convolution neural network is used to construct the sentiment tendency analysis model under the network public opinion. Taking the case of Dolce & Gabbana humiliation incident, Xiangshui explosion incident, and baixiangguo girl's murder as the research cases, the accuracy of the model in identifying the above three events was 85.87%, 73.65%, and 85.87%, respectively, under the optimal parameters setting. The experimental results show that the proposed method can improve the accuracy of emotion recognition by 3.00% ~ 8.00% compared with the manual annotation method, i.e., the network public opinion sentiment orientation recognition model constructed in this study has a high recognition accuracy and can be used to assist relevant departments in detecting network public opinion.