Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis

TL;DR

This paper introduces AC-MIMLLN, a novel multi-instance multi-label learning network that improves aspect-category sentiment analysis by aggregating sentiments of key words, leading to better performance on public datasets.

Contribution

The paper proposes a new framework that models sentences as bags of words and identifies key instances for aspect categories, enhancing sentiment prediction accuracy.

Findings

Outperforms existing methods on three public datasets

Effectively identifies key words influencing sentiment

Improves aspect-category sentiment analysis accuracy

Abstract

Aspect-category sentiment analysis (ACSA) aims to predict sentiment polarities of sentences with respect to given aspect categories. To detect the sentiment toward a particular aspect category in a sentence, most previous methods first generate an aspect category-specific sentence representation for the aspect category, then predict the sentiment polarity based on the representation. These methods ignore the fact that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category in the sentence, which leads to suboptimal performance. In this paper, we propose a Multi-Instance Multi-Label Learning Network for Aspect-Category sentiment analysis (AC-MIMLLN), which treats sentences as bags, words as instances, and the words indicating an aspect category as the key instances of the aspect category. Given a sentence and the aspect categories mentioned in the sentence, AC-MIMLLN first predicts the sentiments of the instances, then finds the key instances for the aspect categories, finally obtains the sentiments of the sentence toward the aspect categories by aggregating the key instance sentiments. Experimental results on three public datasets demonstrate the effectiveness of AC-MIMLLN.