  Most of existing work learn sentiment-specific word representation for
improving Twitter sentiment classification, which encoded both n-gram and
distant supervised tweet sentiment information in learning process. They assume
all words within a tweet have the same sentiment polarity as the whole tweet,
which ignores the word its own sentiment polarity. To address this problem, we
propose to learn sentiment-specific word embedding by exploiting both lexicon
resource and distant supervised information. We develop a multi-level
sentiment-enriched word embedding learning method, which uses parallel
asymmetric neural network to model n-gram, word level sentiment and tweet level
sentiment in learning process. Experiments on standard benchmarks show our
approach outperforms state-of-the-art methods.
