A Study on Online Social Networks Theme Semantic Computing Model

The widespread use of Mobile Intelligent Terminals and ubiquitous access to networks has enabled online information sources including Weibo and Wechat to bring huge impact to the society. Only a few words of network information can expand rapidly and catalyze the generation of a huge amount of information. The highly real-time content, fission-like spreading rate and enormous public opinion guiding forces created in this process will cast great influence on the society. Thus, semantic computing on online social networks and research on topics about emergencies have great significance. In this article, a numerical model of text semantic analysis based on artificial neural network is proposed, and a semantic computational algorithm for social network texts as well as a discovery algorithm for emergencies is provided with reference to the information provided by the social nodes itself and the semantic of the text. Through the numerization of text, the calculation and comparison of semantic distance, the classification of nodes and the discovery of community can be realized. In this article, semantic vector of micro-information for nodes and closure extension of semantic extensions are defined in order to build up an equivalence of short sentences, and in turn realize the discovery of emergencies. Then, huge quantities of Sina Weibo contents are collected to verify the model and algorithm put forward in this article. In the end, outlooks for future jobs are provided. KeyWORdS: Artificial Neural Nets, Burst Topics Discovering, Online Social Networks, Semantic Distance Theme Mining, Theme Semantic Computing