The dissemination effect of human-computer interactive advertising news—Using the theory of media audience and emotion management

Abstract

The development and application of network media has seriously impacted the social information dissemination environment dominated by traditional media. To break the dissemination barriers encountered by traditional media, this work probes into the dissemination effect of human-computer interactive advertising news. An in-depth analysis of the current dissemination situation of interactive online advertising (IOA) is firstly conducted, and then the methods to effectively guide and manage audience emotions are studied. Finally, an improved LeNet-5 model is established to identify audience emotions. The improvement of the LeNet-5 model in this work is composed of the following four points. (1) The convolution module sets Inception_conv3 and Inception_conv5 are adopted to replace the third convolutional layer Conv3 and the fifth layer Conv5 of the LeNet-5, respectively. (2) The size of the convolution kernel is changed. The original convolution kernel is replaced by two 3 × 3 convolution kernels in the Inception_conv3 and Inception_conv5 module sets. (3) The number of convolution kernels is reasonably changed. (4) The Batch Normalization (BN) layer is used. The experimental results show that interactive advertisements have the better dissemination effects among the audiences with older age, higher education, and in more developed cities. The improved LeNet-5 network can effectively solve the over-fitting and gradient disappearance, with a good robustness. The recognition rate reaches more than 81%, which is higher than the traditional LeNet-5 network by 3%. It can be known that the accuracy of the improved LeNet-5 network image recognition is significantly promoted. This research provides a certain reference for the optimization of news dissemination.