Performance Evaluation of the Cyberspace Public Opinion Detection and Tracking

The task of Public Opinion Detection and Tracking monitors the hot topics in the forums, BBS and so on. It collects the latest news and views and then the classifying and clustering algorithms are applied. Finally, the monitoring results are presented to the end users. This task has given the significant impact on the large numbers of internet users. The effective features in the story document are selected and we adopt the vector center model to represent the text document. The clustering algorithm merges the story to the corresponding cluster. We adopt three types of performance evaluation metric and that is F-Value, the entropy value and the Square Error Function JC. We analyze the distribution of the monitoring results on different forums. The results indicate that our approach is effective. The future research will focus on the detection of the timing news reports, and extracting the unique features to study the adaptive detection model and strategies.