Our method contains two part: (1) a representation learning algorithm to effectively learn the continuous representation of phrases, which are used as features for phrase-level sentiment classification, (2) a seed expansion algorithm that enlarge a small list of sentiment seeds to collect training data for building the phrase-level classifier.
we learn sentiment-specific phrase embedding (SSPE), which is a low-dimensional, dense and real-valued vector, by encoding the sentiment information and Learning Algorithm Figure 1: The representation learning approach for building Twitter-specific sentiment lexicon.