  Aspect-based sentiment analysis (ABSA) aims to predict fine-grained
sentiments of comments with respect to given aspect terms or categories. In
previous ABSA methods, the importance of aspect has been realized and verified.
Most existing LSTM-based models take aspect into account via the attention
mechanism, where the attention weights are calculated after the context is
modeled in the form of contextual vectors. However, aspect-related information
may be already discarded and aspect-irrelevant information may be retained in
classic LSTM cells in the context modeling process, which can be improved to
generate more effective context representations. This paper proposes a novel
variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates
aspect information into LSTM cells in the context modeling stage before the
attention mechanism. Therefore, our AA-LSTM can dynamically produce
aspect-aware contextual representations. We experiment with several
representative LSTM-based models by replacing the classic LSTM cells with the
AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the
effectiveness of AA-LSTM.
