  Computational communication research on information has been prevalent in
recent years, as people are progressively inquisitive in social behavior and
public opinion. Nevertheless, it is of great significance to analyze the
direction of predominant sentiment from the sentiment communication
perspective. In this paper, the information emotion propagation model is
established by introducing revamp genetic algorithms into information emotion.
In the process of information dissemination, both the information emotions and
the network emotions are dynamic. For this model, the information emotions and
the network nodes emotions are quantified as binary codes. The convergence
effects, crossover and mutation algorithms are introduced. These factors all
act on the transmission process via dynamic propagation rate, and the improved
genetic algorithm also acts on the emotion transmission. In particular, the
latter two algorithms are different from the existing biological domain. Based
on the existing research results in other manuscripts, we perform simulation
described above on the hybrid network. The simulation results demonstrate that
the trend approximate to the actual data. As a result, our work can prove that
our proposed model is essentially consistent with the actual emotion
transmission phenomenon.
