The Emotion coding and Propagation based on improved Genetic algorithm

TL;DR

This paper introduces an improved genetic algorithm-based model to simulate and analyze emotion propagation in social networks, capturing dynamic emotional states and transmission processes.

Contribution

The paper presents a novel emotion propagation model incorporating an enhanced genetic algorithm with dynamic transmission rates and binary emotion coding.

Findings

Simulation results closely match real-world emotion transmission data.

The model effectively captures the dynamic nature of emotional states during information dissemination.

Proves the proposed approach's validity in representing actual emotion propagation phenomena.

Abstract

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.