  Opinion phrase extraction is one of the key tasks in fine-grained sentiment
analysis. While opinion expressions could be generic subjective expressions,
aspect specific opinion expressions contain both the aspect as well as the
opinion expression within the original sentence context. In this work, we
formulate the task as an instance of token-level sequence labeling. When
multiple aspects are present in a sentence, detection of opinion phrase
boundary becomes difficult and label of each word depend not only upon the
surrounding words but also with the concerned aspect. We propose a neural
network architecture with bidirectional LSTM (Bi-LSTM) and a novel attention
mechanism. Bi-LSTM layer learns the various sequential pattern among the words
without requiring any hand-crafted features. The attention mechanism captures
the importance of context words on a particular aspect opinion expression when
multiple aspects are present in a sentence via location and content based
memory. A Conditional Random Field (CRF) model is incorporated in the final
layer to explicitly model the dependencies among the output labels.
Experimental results on Hotel dataset from Tripadvisor.com showed that our
approach outperformed several state-of-the-art baselines.
