In this paper, we describe a sentiment analysis system implemented for the semantic-evaluation task of message polarity classification for English on Twitter. Our system contains modules of data pre-processing, word embedding, and sentiment classification. In order to decrease the data complexity and increase the coverage of the word vector model for better learning, we perform a series of data pre-processing tasks, including emoticon normalization, specific suffix splitting, and hashtag segmentation. In word embedding, we utilize the pre-trained word vector provided by GloVe. We believe that emojis in tweets are important characteristics for Twitter sentiment classification, but most pre-trained sets of word vectors contain few or no emoji representations. Thus, we propose embedding emojis into the vector space by neural network models. We train the emoji vector with relevant words that contain descriptions and contexts of emojis. The models of long short-term memory (LSTM) and convolutional neural network (CNN) are used as our sentiment classifiers. The proposed emoji embedding is evaluated on the SemEval 2017 tasks. Using emoji embedding, we achieved recall rates of 0.652 with the LSTM classifier and 0.640 with the CNN classifier.