Recently, sentiment analysis on microblogs has received extensive attention recently. Most of previous studies focus on identifying sentiment orientation by encoding as many word properties as possible. However, most of them ignore the long-range dependencies of words (i.e., contextual features), which are essentially important in the sentiment analysis. In this paper, we propose a sentiment analysis method by incorporating Continuous Bag-of-Words (CBOW) model and Stacked Bidirectional long short-term memory (Stacked Bi-LSTM) model to enhance the performance of sentiment prediction. Firstly, a word embedding model, CBOW model, is employed to capture semantic features of words and transfer words into high dimensional word vectors. Secondly, we introduce Stacked Bi-LSTM model to conduct the feature extraction of sequential word vectors at a deep level. Finally, a binary softmax classifier utilizes semantic and contextual features to predict the sentiment orientation. Extensive experiments on real dataset collected from Weibo (i.e., one of the most popular Chinese microblogs) show that our proposed approach achieves better performance than other machine learning models.