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Chinese microblogging texts are always short and casual, which bring some troubles to the traditional sentiment classification methods based on learning. To overcome this problem, we use a rule-based approach to classify the sentiment of Chinese microblogging texts. According to the characteristics of Chinese microblogging texts, we construct a thesaurus of subjective words for it, summarize the basic semantic rules expressing emotion and propose a rule-based approach to sentiment classification of Chinese microblogging texts. Finally, we compare our approach with a SVM-based approach. Our rule-based approach achieves an accuracy of 0.865, which is better than that of SVM-based approach.

With the development of mobile smartphones equipped with a large number of sensor devices, mobile crowd computing has become the hotspot of current research. In the application, the publishers use the application platform to release the task and then select the appropriate users to participate in the task by bidding and collect their data, in which the users’ identity, location, and other private information face the risk of disclosure. To solve the problem of privacy disclosure in mobile crowd computing, we propose a privacy preserving method based on Voronoi Polygon (PP - Voronoi). The users construct the Voronoi diagram to form a cloaked region hiding users identity and then calculate the anchor point of the cloaked region, by which the users communicate with the task publisher to achieve privacy protection of the cloaked region and K anonymity. The experimental results show that compared with other privacy preserving methods, PP-Voronoi method has better effectiveness of privacy protection and lower network traffic.