Network Public Opinion Monitoring Simulation Based on Fuzzy Correlation combined with Web Crawler
Due to the large scale of online public opinion information and its diverse forms of expression,as well as the presence of a large amount of misleading content,the difficulty of monitoring has increased and the accuracy is relatively low.Therefore,a simulation method for network public opinion monitoring combining fuzzy correlation and web crawlers was proposed.At first,network public opinion data was collected by web crawler technology.Then,a fuzzy association rule mining algorithm was used to split the online public opinion text data into words,filter and select all words,so that the keywords could be obtained.Moreover,network public opinion was mined,and the method of scanning statistics was introduced to scan and calculate different types and topics of network public opinion in real time.Finally,the aggregation time of network public opinion was determined.Thus,the network public opinion moni-toring was achieved.The simulation analysis proves that the proposed method can accurately monitor the change of network public opinion.After the network public opinion is monitored,the coverage rate can be as high as 95%,and the false detection rate is always below 5%.This method can provide decision support for the benign development of network public opinion.
Fuzzy associationNetwork public opinionMonitorScan statistic
万方数据
维普
