Method of Internet Public Opinion Hot Topic Mining under Hadoop
Zhou Jian-hu · Journal of Hebei North University · 2014
A model for analyzing public opinion based on Hadoop was developed to solve the problem of the real-time accurate mining hot topic of network public opinion.For topic discovery core module,a WCGFMR algorithm for hot topic mining was given,using a weighted opinion text feature grouping strategy based on Map(mapping)and Reduce(Protocol)rules.Experiments show that the average recall of hot topic excavation reaches to 85.32%,the average pure of the topic cluster reaches to 95.36%,with the public opinion data set increases to 2GB,execution time of multi tasks was much less than small amount of tasks in the certain Map tasks,hot topic mining in large data speed significantly enhanced.