Papers › A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis

A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis

1 Nov 2019IJCNLP 2019 11archive 2025-07-28

Qingnan Jiang, Lei Chen, Ruifeng Xu, Xiang Ao, Min Yang

Aspect-based sentiment analysis (ABSA) has attracted increasing attention recently due to its broad applications. In existing ABSA datasets, most sentences contain only one aspect or multiple aspects with the same sentiment polarity, which makes ABSA task degenerate to sentence-level sentiment analysis. In this paper, we present a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset, in which each sentence contains at least two different aspects with different sentiment polarities. The release of this dataset would push forward the research in this field. In addition, we propose simple yet effective CapsNet and CapsNet-BERT models which combine the strengths of recent NLP advances. Experiments on our new dataset show that the proposed model significantly outperforms the state-of-the-art baseline methods

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