Using Aspect-Based Analysis for Explainable Sentiment Predictions

Abstract

Sentiment Analysis is the study of opinions produced from human written textual sources and it has become popular in recent years. The area is commonly divided into two main tasks: Document-level Sentiment Analysis and Aspect-based Sentiment Analysis. Recent advancements in Deep Learning have led to a breakthrough, reaching state-of-the-art accuracy scores for both tasks, however, little is known about their internal processing of these neural models when making predictions. Aiming for the development of more explanatory systems, we argue that Aspect-based Analysis can help deriving deep interpretation of the sentiment predicted by a Document-level Analysis, working as a proxy method. We propose a framework to verify if predictions produced by a trained Aspect-based model can be used to explain Document-level Sentiment classifications, by calculating an agreement metric between the two models. In our case study with two benchmark datasets, we achieve 90% of agreement between the models, thus showing the an Aspect-based Analysis should be favoured for the sake of explainability.