Implicit Syntactic Features for Targeted Sentiment Analysis

Target-dependent sentiment analysis investigates the sentiment polarities on given target mentions from input texts. Different from sentence-level sentiment, it offers more fine-grained knowledge on each entity mention. While early work leveraged syntactic information, recent research has used neural representation learning to induce features automatically, thereby avoiding error propagation of syntactic parsers, which are particularly severe on social media texts. We study a method to leverage syntactic information without explicitly building parser outputs, by training an encoderdecoder structure parser model on standard syntactic treebanks, and then leveraging its hidden encoder layers when analysing tweets. Such hidden vectors do not contain explicit syntactic outputs, yet encode rich syntactic features. We use them to augment the inputs to a baseline state-of-the-art target-dependent sentiment classifier, observing significant improvements on various benchmark datasets. We obtain the best accuracies on two different test sets for targeted sentiment.