  Dependency parse trees are helpful for discovering the opinion words in
aspect-based sentiment analysis (ABSA). However, the trees obtained from
off-the-shelf dependency parsers are static, and could be sub-optimal in ABSA.
This is because the syntactic trees are not designed for capturing the
interactions between opinion words and aspect words. In this work, we aim to
shorten the distance between aspects and corresponding opinion words by
learning an aspect-centric tree structure. The aspect and opinion words are
expected to be closer along such tree structure compared to the standard
dependency parse tree. The learning process allows the tree structure to
adaptively correlate the aspect and opinion words, enabling us to better
identify the polarity in the ABSA task. We conduct experiments on five
aspect-based sentiment datasets, and the proposed model significantly
outperforms recent strong baselines. Furthermore, our thorough analysis
demonstrates the average distance between aspect and opinion words are
shortened by at least 19% on the standard SemEval Restaurant14 dataset.
