The existing unsupervised GML solution for Aspect-Term Sentiment Analysis (ATSA) is limited by inaccurate and insufficient knowledge conveyance. However, the Supervised GML (S-GML) can effectively exploit labeled training data to improve knowledge conveyance. The S-GML has the following characteristics:
It leverages binary polarity relations between instances, the most direct way of knowledge conveyance, to enable supervised gradual learning.
Besides the explicit polarity relations indicated by discourse structures, it also separately supervises a polarity classification DNN and a binary siamese network to extract implicit polarity relations.