Aspect based sentiment analysis (ABSA) can provide more detailed information
than general sentiment analysis, because it aims to predict the sentiment
polarities of the given aspects or entities in text. We summarize previous
approaches into two subtasks: aspect-category sentiment analysis (ACSA) and
aspect-term sentiment analysis (ATSA)...
Most previous approaches employ long
short-term memory and attention mechanisms to predict the sentiment polarity of
the concerned targets, which are often complicated and need more training time. We propose a model based on convolutional neural networks and gating
mechanisms, which is more accurate and efficient. First, the novel Gated
Tanh-ReLU Units can selectively output the sentiment features according to the
given aspect or entity. The architecture is much simpler than attention layer
used in the existing models. Second, the computations of our model could be
easily parallelized during training, because convolutional layers do not have
time dependency as in LSTM layers, and gating units also work independently. The experiments on SemEval datasets demonstrate the efficiency and
effectiveness of our models.