Research On Auto Analysis And Key Technologies Of Early Warning Based On Web Public Opinion

With the popularization of the internet and the increase in the number of internet users, the internet public opinion has become an important part of the public opinion. At the time, compared with the traditional public opinion, the internet public opinion has the features of being large in number, abrupt in occurrence, scattered in sources and influential in many field. The inspection and piloting of the internet public opinion is very important while the monitoring method adopted most is human monitoring at present. In order to improve the monitoring effect, it is in desperate need to introduce automatic analysis and forecasting method to keep track of the tendencies of the internet public opinion. In this case, it is easier for related departments to intervene on time.This paper first studies the present technologies adopted in the public opinion forecasting and analysis and the related system for public opinion analysis and summarizes the general model. This paper divides the model into two parts:one is the model for the hot issue detection and the other is for the hot issue forecast. Improvements targeting the two parts are suggested. Firstly, on the basis of online comment’s importance in web public opinion, this paper applies web opinion mining in the public opinion forecasting and analysis model. By using web opinion dictionary, comment is quantized; meanwhile, review details vector and opinion vector are proposed to optimize the original character representation of report. In the original model, multisource data was equally treated, this paper put forward public opinion source analysis model to resolve the problem. Secondly, C5.0 decision tree algorithm and BP neural network algorithm are combined to structure the classification and prediction model. The model forecasts the public opinion development tendency by different opinion polarity and strength and improves the shortcoming of unclassified forecast. Finally, experiment demonstrates the improved model lower the fallout ratio and omission factor, at the same time, MAPE in public opinion development tendency is reduced.