  Word embeddings have been widely used in sentiment classification because of
their efficacy for semantic representations of words. Given reviews from
different domains, some existing methods for word embeddings exploit sentiment
information, but they cannot produce domain-sensitive embeddings. On the other
hand, some other existing methods can generate domain-sensitive word
embeddings, but they cannot distinguish words with similar contexts but
opposite sentiment polarity. We propose a new method for learning
domain-sensitive and sentiment-aware embeddings that simultaneously capture the
information of sentiment semantics and domain sensitivity of individual words.
Our method can automatically determine and produce domain-common embeddings and
domain-specific embeddings. The differentiation of domain-common and
domain-specific words enables the advantage of data augmentation of common
semantics from multiple domains and capture the varied semantics of specific
words from different domains at the same time. Experimental results show that
our model provides an effective way to learn domain-sensitive and
sentiment-aware word embeddings which benefit sentiment classification at both
sentence level and lexicon term level.
