Aspect Based Sentiment Analysis by Pre-Trained Language Representations

Given a paragraph of text, the objective of aspect-level sentiment classification is to identify the sentiment polarity of a specific phrase. Most exisiting work employed LSTM model and attention mechanisms to predict the sentiment polarity of the question targets. Unfortunately, these approaches haven't fully utilize the independent modeling of these target phrases. We propose a model based on TextCNN and Transformer pretrained model. In our model, the representations are generated for the targets and the contexts separately. We use Transformer model to help represent a target and its context via attention learning, which improves the performance of aspect-level sentiment classification. Experiments on COAE2014 and COAE2015 task show the effectiveness of our new model.