Application Of Microblogging Public Opinion Data Analysis In Chinese Disease Surveillance System

In the growing popularity of information technology,home computers and handheld mobile devices,the application of micro blog growing popularity,way for people to get information continuously expanding,and available resources,the number is on the rise and the lifestyle is changing constantly.People not only can obtain information of interest to them through the network,it can also interact with other users with text,pictures,video and other means.Traditional disease surveillance systems rely on clinical data and medical units around the CDC uploaded,the system can inform the characteristics of the disease in the country after a pooled analysis of clinical data,which can not only help medical staff identify,predict disease can also spread to some extent changes in the scope.However,publishing data feedback and early warning signals of this system there is a certain lag,the system cost and maintenance cost data collection methods are quite expensive,hence this essay designed with complementary Internet social networking platform to collect public opinion data as a reference of traditional disease surveillance system.Due to the rise of social networks,people can quickly access many information on daily life habits and lifestyles of residents.Users often publish and upload photos of their daily activities,health data,and their geographic location on social networks such as Twitter,Weibo,and Foursquare.The author found that crawler software can effectively collect a large amount of Weibo public opinion data.The effective information obtained after screening keywords and setting corresponding conditions to eliminate interference information can help the public health department to monitor and track the real-time response of the affected people during the epidemic and determine the relative public opinion guidance strategy.On this basis,further analysis of these data can not only grasp the location of epidemic emergencies,but also can use the corresponding geo-location data to track the travel routes of social network users to predict the development trend of the epidemic.In this article,the author designed and applied a scheme for collecting,screening,and analyzing text content of social media and applied this program to collect the Weibo season of the H7N9 flu season.At the same time,support vector regression was used to analysis the previously collected microblog public opinion data and the 20 epidemic city case data published by CDC to find the variables most relevant to the influenza epidemic trend.In the experiments in this paper,the author set the population flow,last year’s influenza trend similarity,and spatial distance as the variables of support vector regression,concluded that the correlation and goodness of fit of population flow and influenza trends are the highest.Therefore,the author concludes that the collection of information on the population flow in the affected area will have a very significant effect on predicting the development trend of the next stage of the epidemic.The innovation of this paper lies in the combination of network public opinion and epidemic monitoring,which provides new ideas for the application of network public opinion data analysis and epidemic monitoring in China.The author also believes that the experimental design methods and experimental results in this paper can provide some reference for further research in this area and contribute to the improvement of China’s epidemic disease surveillance system.