  The emerging technique of deep learning has been widely applied in many
different areas. However, when adopted in a certain specific domain, this
technique should be combined with domain knowledge to improve efficiency and
accuracy. In particular, when analyzing the applications of deep learning in
sentiment analysis, we found that the current approaches are suffering from the
following drawbacks: (i) the existing works have not paid much attention to the
importance of different types of sentiment terms, which is an important concept
in this area; and (ii) the loss function currently employed does not well
reflect the degree of error of sentiment misclassification. To overcome such
problem, we propose to combine domain knowledge with deep learning. Our
proposal includes using sentiment scores, learnt by quadratic programming, to
augment training data; and introducing the penalty matrix for enhancing the
loss function of cross entropy. When experimented, we achieved a significant
improvement in classification results.
