News topic recognition of Chinese microblog based on word co-occurrence graph
Hou Xiaoke · Caai Transactions on Intelligent Systems · 2012
The traditional topic detection algorithm is applied to longer texts such as: news website pages or blogs,causing it to be hard to deal with sparse microblog data effectively.In this paper,a method based on the word cooccurrence graph was provided to detect news topics of microblogs.Firstly,the relative word frequency and the word frequency increase rate were considered to extract new keywords from microblog text after pretreatment.Secondly,a word co-occurrence graph was built by co-occurrence degrees of keywords;each unconnected cluster in a word co-occurrence graph was taken as a news topic by calculating several keywords.These keywords contain much more information in each cluster,was used to represent a news topic of microblog.Finally,data analysis provided evidence on how the approach is most effective and also revealed the microblog data set recognized news topic recognition.