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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| Autori principali: | , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI AG
2024-03-01
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| Serie: | Applied Sciences |
| Soggetti: | |
| Accesso online: | https://www.mdpi.com/2076-3417/14/7/2737 |
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