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.

本文研究了利用卷积神经网络和门机制的模型，提高方面基情感分析的准确性和效率。该模型的提出主要在于采用了 Gated Tanh-ReLU Units 来根据给定的方面或实体选择性地输出情感特征。实验结果显示其在 SemEval 数据集上的性能优越。