  Neural sequence models have achieved great success in sentence-level
sentiment classification. However, some models are exceptionally complex or
based on expensive features. Some other models recognize the value of existed
linguistic resource but utilize it insufficiently. This paper proposes a novel
and general method to incorporate lexicon information, including sentiment
lexicons(+/-), negation words and intensifiers. Words are annotated in
fine-grained and coarse-grained labels. The proposed method first encodes the
fine-grained labels into sentiment embedding and concatenates it with word
embedding. Second, the coarse-grained labels are utilized to enhance the
attention mechanism to give large weight on sentiment-related words.
Experimental results show that our method can increase classification accuracy
for neural sequence models on both SST-5 and MR dataset. Specifically, the
enhanced Bi-LSTM model can even compare with a Tree-LSTM which uses expensive
phrase-level annotations. Further analysis shows that in most cases the lexicon
resource can offer the right annotations. Besides, the proposed method is
capable of overcoming the effect from inevitably wrong annotations.
