How to find negative emotion burst topic in time from massive and noisy microblog text stream is essential for emergency response and handling of emergencies. However, the traditional burst topic detection methods often ignore the differences between negative emotion burst topic and non-negative emotion burst topic. Therefore, a Negative Emotion Burst Topic Detection (NE-BTD) algorithm for microblog text stream was proposed. Firstly, the accelerations of keyword pairs in microblog and the change rate of negative emotion intensity were used as the basis for judging the topics of negative emotion. Secondly, the speeds of burst word pairs were used to determine the window range of negative emotion burst topics. Finally, a Gibbs Sampling Dirichlet Multinomial Mixture model (GSDMM) clustering algorithm was used to obtain the topic structures of the negative emotion burst topics in the window. In the experiments, the proposed NE-BTD algorithm was compared with an existing Emotion-Based Method of Topic Detection (EBM-TD) algorithm. The results show that the NE-BTD algorithm was at least 20% higher in accuracy and recall than the EBM-TD algorithm, and it can detect negative emotion burst topic at least 40 minutes earlier.

Because the model for computing geographic sunshine hours based on Digital Elevation Model (DEM) is complex and time-consuming, it is difficult to consider both high resolution and vast area at the same time when a study on geographic sunshine hours is conducted on a national scale in China. Some scholars proposed some methods for calculating high-resolution geographic sunshine hours across nationwide, however they didn't specify the computing platform and calculation methods. In this study, authors developed a set of automation system for calculating geographic sunshine hours based on existing models and DEM in which the part of curvature of the earth was corrected. This system was developed based on VS2008 platform and ArcGIS Engine component technology. The system could be used to calculate geographic sunshine hours at multi-spatial-scales and multi-resolutions. The raster data of geographic sunshine hours were generated as long as the user input DEM data with geographic coordinates of the region and the specific date.