Audio Public opinion Analysis Model based on heterogeneous Neural Network

With the rapid development of the information age, the main body of public opinion information is no longer just structured data such as text and numbers, but the proportion of unstructured data such as audio and video has increased significantly. Therefore, the analysis and processing of unstructured public opinion data has become the key to deal with the problem of public opinion. Based on this, this paper designs an audio public opinion early warning model based on heterogeneous neural network. Heterogeneous neural network refers to a variety of different types of neural networks to participate in the completion of specified tasks under the premise of data sharing, while retaining the autonomy of each neural network. In this paper, the audio data is converted into a spectrum as an input feature. The convolution neural network can obtain the feature representation of the audio in the spectrum graph, which is used as the input of the recurrent neural network, and then the features related to the text representation are obtained. In this paper, Bert is used to modify the semantic results combined with public opinion domain knowledge, and finally the results are introduced into the shallow neural network Fasttext for public opinion early warning processing. In this paper, experiments are carried out based on the open source voice data set ST-CMDS-20170001-1 Chinese mandarin corpus and Pytorch platform. The results show that this model can effectively analyze and process public opinion of unstructured audio and video data.