A detection method of online public opinion based on element co-occurrence

Abstract

Discovering and identifying public opinion timely and efficiently from web text are of great significance. The present methods of public opinion supervision suffer from being rough and less targeted. To overcome these shortcomings, this paper provides a public opinion detection method of network public opinion based on element co-occurrence for specific domain. This method, considering the nature of public opinion, represents three main factors (subject, object and semantic orientation) that constitute public opinion by employing their feature words, which can be dynamically combined according to their syntagmatic and associative relations. Thus, this method can not only generate topics related to public opinion in specific fields, but also identify public opinion information of these fields efficiently. The method has found its practical usage in “Language Public Opinion Monitoring System” and “Higher Education Public Opinion Monitoring System” with accuracies 92% and 93% respectively.