Aspect-Sentiment-Multiple-Opinion Triplet Extraction

TL;DR

This paper introduces ASMOTE, a new task for extracting aspect, sentiment, and multiple opinions triplets from sentences, addressing limitations of previous ASTE methods by capturing comprehensive opinions and reasons behind sentiments.

Contribution

The paper proposes ASMOTE and an Aspect-Guided Framework with Sequence Labeling Attention to improve multi-opinion triplet extraction accuracy.

Findings

The proposed method outperforms existing ASTE models on multiple datasets.

ASMOTE effectively captures multiple opinions per aspect, providing richer sentiment explanations.

Experimental results demonstrate improved sentiment classification accuracy.

Abstract

Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term (aspect), sentiment and opinion term (opinion) triplets from sentences and can tell a complete story, i.e., the discussed aspect, the sentiment toward the aspect, and the cause of the sentiment. ASTE is a charming task, however, one triplet extracted by ASTE only includes one opinion of the aspect, but an aspect in a sentence may have multiple corresponding opinions and one opinion only provides part of the reason why the aspect has this sentiment, as a consequence, some triplets extracted by ASTE are hard to understand, and provide erroneous information for downstream tasks. In this paper, we introduce a new task, named Aspect Sentiment Multiple Opinions Triplet Extraction (ASMOTE). ASMOTE aims to extract aspect, sentiment and multiple opinions triplets. Specifically, one triplet extracted by ASMOTE contains all opinions about the aspect and can tell the exact reason that the aspect has the sentiment. We propose an Aspect-Guided Framework (AGF) to address this task. AGF first extracts aspects, then predicts their opinions and sentiments. Moreover, with the help of the proposed Sequence Labeling Attention(SLA), AGF improves the performance of the sentiment classification using the extracted opinions. Experimental results on multiple datasets demonstrate the effectiveness of our approach.