Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE)

TL;DR

This paper introduces a hybrid neural-symbolic approach using Dependency Tree-LSTM and symbolic rules to extract sentiment triplets, reducing data dependency and enhancing interpretability in aspect-based sentiment analysis.

Contribution

The paper presents a novel hybrid method combining Tree-LSTM and symbolic rules for triplet extraction without requiring triplet training data.

Findings

Performs comparably to state-of-the-art methods

Reduces need for elaborate annotated datasets

Offers improved interpretability

Abstract

Sentiment analysis has transitioned from classifying the sentiment of an entire sentence to providing the contextual information of what targets exist in a sentence, what sentiment the individual targets have, and what the causal words responsible for that sentiment are. However, this has led to elaborate requirements being placed on the datasets needed to train neural networks on the joint triplet task of determining an entity, its sentiment, and the causal words for that sentiment. Requiring this kind of data for training systems is problematic, as they suffer from stacking subjective annotations and domain over-fitting leading to poor model generalisation when applied in new contexts. These problems are also likely to be compounded as we attempt to jointly determine additional contextual elements in the future. To mitigate these problems, we present a hybrid neural-symbolic method utilising a Dependency Tree-LSTM's compositional sentiment parse structure and complementary symbolic rules to correctly extract target-sentiment-cause triplets from sentences without the need for triplet training data. We show that this method has the potential to perform in line with state-of-the-art approaches while also simplifying the data required and providing a degree of interpretability through the Tree-LSTM.