Transformation Networks for Target-Oriented Sentiment Classification

Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence.RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance.After re-examining the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this classification task, we propose a new model to overcome these issues.Instead of attention, our model employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RNN layer.Between the two layers, we propose a component to generate target-specific representations of words in the sentence, meanwhile incorporate a mechanism for preserving the original contextual information from the RNN layer.Experiments show that our model achieves a new state-of-the-art performance on a few benchmarks. 1