  The use of user/product information in sentiment analysis is important,
especially for cold-start users/products, whose number of reviews are very
limited. However, current models do not deal with the cold-start problem which
is typical in review websites. In this paper, we present Hybrid Contextualized
Sentiment Classifier (HCSC), which contains two modules: (1) a fast word
encoder that returns word vectors embedded with short and long range dependency
features; and (2) Cold-Start Aware Attention (CSAA), an attention mechanism
that considers the existence of cold-start problem when attentively pooling the
encoded word vectors. HCSC introduces shared vectors that are constructed from
similar users/products, and are used when the original distinct vectors do not
have sufficient information (i.e. cold-start). This is decided by a
frequency-guided selective gate vector. Our experiments show that in terms of
RMSE, HCSC performs significantly better when compared with on famous datasets,
despite having less complexity, and thus can be trained much faster. More
importantly, our model performs significantly better than previous models when
the training data is sparse and has cold-start problems.
