Detecting Dependency-Related Sentiment Features for Aspect-Level Sentiment Classification
Xing Zhang, Jingyun Xu, Yi Cai, Xingwei Tan, Changxi Zhu
Abstract
Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence toward a given aspect term or aspect category. For sentiment classification toward a given aspect term, some opinions may exist that are not the given aspect term's modifiers because a sentence may contain more than one aspect term. Hence, It is necessary to capture relevant opinion for a certain aspect term. To capture the nearest opinion of the aspect term, researchers have used the relative distance between an aspect term and all other words in a sentence. However, this can be infeasible when the sentence has a complex syntactic structure. In this paper, we introduce dependency relation to detect the dependency-related sentiment feature for the aspect term in the dependency parse tree, and integrate this relationship into the convolutional neural network and bidirectional long short-term memory. Experiments show that the related sentiment features for an aspect term help models discriminate its sentiment polarity. The proposed models achieve state-of-the-art results among neural networks. The codes and datasets are released on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LittleSummer114/DW-CNN</uri> .