Using Text Semantic Mining to Calculate Emotional Heat of Social Network Nodes

Social network is an important channel for users to vent their emotions, such as microblog community. This paper proposes a method to calculate the emotional heat of social network nodes based on text sentiment analysis and text classification model. Firstly, the sentiment tendency of the text content is analyzed by sentiment dictionary and semantic analysis, and the emotional value of each text content is calculated. Secondly, the text classification model is used to distinguish the different text topics of each node. And finally, the weighted algorithm is used to calculate the comprehensive evaluation value of different topic texts published by the node as the emotional heat. Taking the microblog community as an example, we use this method to collect text information published by social network nodes. The results show that integration of sentiment analysis, topic analysis, semantic analysis of text mining is conducive to the intuitive display of users' emotions in social networks, and helps regulators guide netizens' emotions.