Study On Key Technologies Of Internet Public. Sentiment Forecast

People pay more and more attention to the public sentiment in present day society. The public sentiment in different language environment, its meaning is different. In general, the definition of public sentiment is divided into two, broad and narrow definition of public sentiment. We are usually more concerned about public sentiment is narrow. With the rapid development of Internet, public opinion has emerged a new form of internet public sentiment. The establishment of public sentiment collection and analysis mechanism, smooth channels for social conditions and public opinion is 10 Fourth Plenary Session, "the CPC Central Committee on Strengthening the Party's governance capacity-building decisions" in the clear demands. For network analysis of public sentiment, acquisition, prediction will be building a harmonious society, maintain social stability and economic development to ensure rapid and healthy development of great significance.In recent years more and more scholars begin to study internet public sentiment and its associated analysis and research. Relevant scientific paper is increasing. Among them, a large number of documents for text clustering, hot spot analysis and language preference for such direction has been made in-depth study, but the forecast for the internet public sentiment and a complete network system design of the internet public sentiment forecast still not much.Based on the existing skills and knowledge to analyze and summarize the basis of internet public sentiment, A forecastsystem has been designed and the key technologies related to the study. This major work includes:1) The paper analyze a variety of network crawling strategy, using heuristic search strategies based on the network, crawler, crawling the data for the network.2) The public opinion data preprocessing. One of the main tree using the text based on hierarchical clustering method, based on the hot topics of concern about access to technology, optimization-based aggregation of data aggregation strategy table.3) The paper discuss the gray system theory, the use of gray GM (1,1) model for single prediction model was constructed, the steps are given model, and analyzes its limitations.4) The paper discussed three different Markov prediction method, using the weighted Markov Chain Model.5) IPS Forecast System is designed. The paper used a combination forecasting method based on gray theory and Markov chain.