Existing sentiment analysis focus on the emotional tendency, which are lack of detailed description of all kinds of emotions, they can’t intuitively reflect the emotional change of social groups. An emotional analysis method based on the combination of dependency parsing and artificial tagging was proposed. Facial expression animation to present emotions analysis was realized. The microblog crowd’s emotion in different areas for different social events was visualized. The experimental results show that the model could closely and effectively simulate the crowd emotion, and it could provide a new way of the analysis of network public opinion based on large data.

A novel test data compression technique named CacheCompress is proposed. Different from the previous static dictionary based techniques, this dictionary is dynamic. During testing the dictionary is accessed by read and write operations and only needs to keep the most frequency used data thus to largely decrease the memory size requirement and eliminate the explicit dictionary initialization step. Experiments show that CacheCompress achieves 30% higher compression ratio than other recent compression schemes while the dictionary size dramatically reduces to 1‰.