Microblogging websites such as twitter and Sina Weibo have attracted many users to share their experiences and express their opinions on a variety of topics. Sentiment classification of microblogging texts is of great significance in analyzing users' opinion on products, persons and hot topics. However, conventional bag-of-words-based sentiment classification methods may meet some problems in processing Chinese microblogging texts because they does not consider semantic meanings of texts. In this paper, we proposed a global RNN-based sentiment method, which use the outputs of all the time-steps as features to extract the global information of texts, for sentiment classification of Chinese microblogging texts and explored different RNN-models. The experiments on two Chinese microblogging datasets show that the proposed method achieves better performance than conventional bag-of-words-based methods.