Propagation-Based Domain-Transferable Gradual Sentiment Analysis

Célia da Costa Pereira, Claude Pasquier, Andrea Tettamanzi

2025

Abstract

We propose a novel refinement of a gradual polarity propagation method to learn the polarities of concepts and their uncertainties with respect to various domains from a labeled corpus. Our contribution consists of introducing a positive correction term in the polarity propagation equation to counterbalance negative psychological bias in reviews. The proposed approach is evaluated using a standard benchmark, showing an improved performance relative to the state of the art, good cross-domain transfer and excellent coverage.

Paper Citation

in Harvard Style

Pereira C., Pasquier C. and Tettamanzi A. (2025). Propagation-Based Domain-Transferable Gradual Sentiment Analysis. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 520-527. DOI: 10.5220/0013158200003890

in Bibtex Style

@conference{icaart25,
author={Célia Pereira and Claude Pasquier and Andrea Tettamanzi},
title={Propagation-Based Domain-Transferable Gradual Sentiment Analysis},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={520-527},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013158200003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Propagation-Based Domain-Transferable Gradual Sentiment Analysis
SN - 978-989-758-737-5
AU - Pereira C.
AU - Pasquier C.
AU - Tettamanzi A.
PY - 2025
SP - 520
EP - 527
DO - 10.5220/0013158200003890
PB - SciTePress