Public Opinion Hotspot Discovery Algorithm Based on Fuzzy Clustering LDA
The discovery of public opinion hotspot is an important aspect of public opinion research, and because many similarities and relevance exist between hot topics, we propose a hot topic clustering algorithm to find the hotspot in public opinions. Since fuzzy set can handle non-precision data well, the fuzzy algorithm can reduce the influences of the uncertainty of public opinion data. Based on LDA topic extraction we cluster the topical words by fuzzy method, and take the topic probability as word membership to the cluster. It can reduce the noise data and improve the ability of hotspot discovery that aggregate the similar and related topic to one class. The topical key words with high probability in cluster are the hotspot, and singular cluster with few words can be looked as outlier. The algorithm is demonstrated by example analysis in detail.