Abstract
Aspect-based sentiment classification aims to identify sentiment polarity expressed towards a given
opinion target in a sentence. The sentiment polarity
of the target is not only highly determined by sentiment semantic context but also correlated with the
concerned opinion target. Existing works cannot
effectively capture and store the inter-dependence
between the opinion target and its context. To solve
this issue, we propose a novel model of Attentive
Neural Turing Machines (ANTM). Via interactive
read-write operations between an external memory
storage and a recurrent controller, ANTM can learn
the dependable correlation of the opinion target to
context and concentrate on crucial sentiment information. Specifically, ANTM separates the information of storage and computation, which extends the capabilities of the controller to learn and
store sequential features. The read and write operations enable ANTM to adaptively keep track of
the interactive attention history between memory
content and controller state. Moreover, we append
target entity embeddings into both input and output of the controller in order to augment the integration of target information. We evaluate our
model on SemEval2014 dataset which contains reviews of Laptop and Restaurant domains and Twitter review dataset. Experimental results verify that
our model achieves state-of-the-art performance on
aspect-based sentiment classification.