Positive Guidance Learning Strategies and Methods of Social Network Public Opinion Based on Structural Balance

In social network platforms, a topic may evolve into hot public opinion after hundreds of millions of Internet users comments and spreads. Under text big data of the social network, we grasp evolution tendency and development law of the topic. It is great significant for social stability that we adopt appropriate strategies and methods to guide negative and weak positive topics toward the positive development. At present, the domestic and foreign research on guidance strategy of network public opinion is still in its infancy. And so, we systematically put forward the theory and method of the positive guidance strategy of network public opinion in this project. Our mainly researches are listed as follows: studying the approaches to identify the public opinion of the social networks based on the analysis of the time sequence features of topic point view emotional tendency. Discussing the cooperative relationship model among public opinion life cycle, the public opinion evolution of the social networks and the balance dynamics of network structure. We explore the approaches of public opinion analysis of the social network based on its balance theory. Researching on features of public opinion guidance model by combining with structure holes analysis, the key node recognition and homogeneity analysis of social network structure. Designing the positive guidance learning model, algorithm and system of the social network public opinion by using the structural balance theory.