User Behavior Based On Social Network Analysis And Forecasting

In recent years, social networking services are booming in the world with a large number of users, and have become an information platform with huge influence. Mastering social network user behavior, emotion, and the propagation law, is not only to help enterprises to provide better services and products based on users’behavior features, do more effective network marketing and promotion, but also provide theoretical basis for some relevant departments to do reasonable monitoring and intervention of network public opinions.Based on the Sina weibo as an object,the paper around the users’interaction behavior in the social network, the perception of the user’s emotional, studies emotions propagation mechanism, which is combined with the emotional changes to predict the user’s behavior, and put forward the emotional prediction model and advertising click-through rate prediction model based on dynamic factor graph model "moodcast"First, in this paper we explore the methods of natural language processing and semantic analysis, analyze the views expressed by the users on social networks, dig out the user’s interest and emotional bias. Although, this method follows the usual text analysis method, but there will be its particularity by referring to the social networks. We do the short-text analysis on social networks, whose specific sub-word clustering approach is different from the past.Second, previous studies on the emotion of users did not consider the influence of other people. That is emotional communication issues we are talking about here. In this article, the propagation of user·emotional was introduced to the social network, and the emotional prediction model after being influenced by friends on the social network was proposed through the study on dynamic factor graph the model moodcast. Two properties were mainly analyzed here. First, the temporal correlation:a person’s emotional state of current time and her/his recent past emotional state are highly correlated. Second, the social relevance:a person’s emotional state is related to her/his friends. Friend’s impact decays exponentially within three degrees. We shall emulate this model to verify that the user’s emotional state is indeed influenced by friends, and in the cases of different parameters, the degree of influence shows a greater difference.Finally, based on the user’s emotional prediction and interest mining, combined with Bayesian, propose the prediction model of users to click on ads. The ad itself is composed by short text, we here use probability method to sort Advertising Keywords, and through the Bayesian, use posterior probability of click on the ads to analyze the before probability of click on reasons. Because the impact of emotions on the click-through rate is not individual factors, but overall impact, the impact goes through the process of different CTR changes, which needs to take the exponential form into the model. We also do the simulation experiment to verify this prediction model, which turns out that the user’s emotional does impact the user’s behavior.