Detecting Dependency-Related Sentiment Features for Aspect-Level Sentiment Classification

Aspect level sentiment classification aims to classify the sentiment polarity of a given aspect term or aspect category in a sentence. For sentiment classification towards a given aspect term, since a sentence may contain more than one aspect term, there may exist some opinions which are not the modifiers of the given aspect term. It is necessary to capture relevant opinion for a certain aspect term. Previous works use the relative distance between an aspect term and all other words in a sentence, in order to capture the nearest opinion of the aspect term. This can be infeasible when the sentence has a complex syntactic structure. In this paper, we detect the dependency relation between an aspect term and its related sentiment words in the dependency parse tree. Then, we integrate this relationship into CNN and Bi-LSTM respectively. Experiments show that the related sentiment features for an aspect term is helpful for models to discriminate its sentiment polarity, and our proposed models achieve state-of-the-art results among neural network models.