Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis

TL;DR

This paper introduces aspect sentiment triplet extraction (ASTE), a comprehensive approach to extract detailed sentiment triplets including aspects, their sentiment polarities, and reasons, advancing the field of aspect-based sentiment analysis.

Contribution

It proposes a novel subtask and a two-stage framework for complete triplet extraction, setting a new benchmark and outperforming existing methods.

Findings

Achieved state-of-the-art performance on the ASTE task.

Outperformed strong baseline models.

Established a new benchmark for comprehensive aspect-based sentiment analysis.

Abstract

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from "Waiters are very friendly and the pasta is simply average" could be ('Waiters', positive, 'friendly'). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.