Sentiment Triplet Extraction With Multi-View Contrastive Learning

Abstract

Sentiment Triplet Extraction (STE) is a challenging Aspect-based Sentiment Analysis task that involves identifying aspect terms, aspect categories, opinion terms, and their corresponding sentiment polarities in sentences. However, the complex relationships and implicit elements constituting sentiment triplets (aspect, opinion, polarity) or (aspect, category, polarity) pose a significant challenge. This paper proposes a novel model called Multi-view Contrastive Learning (MCL) for STE. We treat STE as a text generation task and employ Contrastive Learning at both the triplet and sentiment views. At the triplet view, the source text is used as an anchor, and the target text is regarded as positive samples, while negative samples are obtained by destroying triplet elements in the target text. At the sentiment view, aspect terms are concatenated with their corresponding opinion terms or categories, and the same sentiment polarity in the dataset is used as positive samples, while different polarities are considered negative samples. Our experimental results show that the proposed model outperforms the baseline GAS-EXTRACTION by a significant margin, with every improvement on F1 of 5.21 for Aspect Sentiment Triplet Extraction and 2.98 for Aspect Category Sentiment Detection. These results highlight the effectiveness of incorporating Contrastive Learning in the STE task.