Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction

In this paper, we present a novel self-supervised framework for Sentiment Cue Extraction (SCE) aimed at enhancing the interpretability of text sentiment analysis models. Our approach leverages self-supervised learning to identify and highlight key textual elements that significantly influence sentim...

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Auteurs principaux:	Yawei Sun, Saike He, Xu Han, Yan Luo
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2024-03-01
Collection:	Applied Sciences
Sujets:	sentiment cue extraction self-supervised learning interpretable machine learning
Accès en ligne:	https://www.mdpi.com/2076-3417/14/7/2737
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