Study On News Text Sets Visualization Model

Background study shows that the world’s energy crisis will appear in the next60years. Shale gas as a new energy it has huge development potential and in China it’s ina period of rapid development. Shale gas news are growing so fast and so huge thathow to quickly get the shale gas information needed from these news has become ameaningful subject.This study select news about shale gas from December2009to March2013constitutes the news text set visualization research. This study analyzed the content andstructure of the shale gas news text set defines four categories of information entitiesof time, place, subject and events, to form a closed loop through these four types ofentities in the view of network visualization to represent each a news information. Andwe use the Chinese Lexical Analysis System ICTCLAS developed by the ChineseAcademy of Sciences, do part of the implementation of the automatic extraction ofinformation entities; Given the point of the network, say that if the information entitiesappears in a news text information at the same time produce an edge. And startedwriting software to achieve automatic extraction of information entities associated.Then, create a conceptual model of the shale gas news text sets visualization. The modelcontains three main processes: data conversion, visual mapping, and picture changing.For visualization of the results, the paper first analyzed as a whole and found thatdomestic shale gas concerns many enterprises, but three major company still firmlyoccupy a dominant position. While Chesapeake enterprises which focused on shale gasare concerned about also many, and so on; Followed by a combination of the centralityand betweenness centrality，we selected Government, Beijing, USA, Sichuan, Sinopec,Petro China to make further in-depth analysis as the so-called important informationentities. It founded out that Petro China pay more attention to foreign shale gas market;Finally, the analysis of Subject–Event and Location-Event information entitiesassociated shows that foreign shale gas enterprises are more into investment，acquisition and cooperation than the state-owned enterprises.In conclusion, this study successfully combines the complex network theory analysis methods and text set visualization. The one hand intuitively visualize the textinformation, on the other hand mines the relationship between the information.Effectively visualizes the shale gas industry related enterprises and regions. Goals ofsaving the relevant people’s time and improving their efficiency has achieved.