  This paper addresses the problem of sentence-level sentiment analysis. In
recent years, Convolution and Recursive Neural Networks have been proven to be
effective network architecture for sentence-level sentiment analysis.
Nevertheless, each of them has their own potential drawbacks. For alleviating
their weaknesses, we combined Convolution and Recursive Neural Networks into a
new network architecture. In addition, we employed transfer learning from a
large document-level labeled sentiment dataset to improve the word embedding in
our models. The resulting models outperform all recent Convolution and
Recursive Neural Networks. Beyond that, our models achieve comparable
performance with state-of-the-art systems on Stanford Sentiment Treebank.
