Aspect Based Sentiment Analysis using French Pre-Trained Models

Abderrahman Essebbar, Bamba Kane, Ophélie Guinaudeau, Valeria Chiesa, Ilhem Quénel, Stéphane Chau

Abstract

Aspect Based Sentiment Analysis (ABSA) is a fine-grained task compared to Sentiment Analysis (SA). It aims to detect each aspect evoked in a text and the sentiment associated to each of them. For English language, many works using Pre-Trained Models (PTM) exits and many annotated open datasets are also available. For French Language, many works exits in SA and few ones for ABSA. We focus on aspect target sentiment analysis and we propose an ABSA using French PTM like multilingual BERT (mBERT), CamemBERT and FlauBERT. Three different fine-tuning methods: Fully-Connected, Sentences Pair Classification and Attention Encoder Network, are considered. Using the SemEval2016 French reviews datasets for ABSA, our fine-tuning models outperforms the state-of-the-art French ABSA methods and is robust for the Out-Of-Domain dataset.

Download


Paper Citation


in Harvard Style

Essebbar A., Kane B., Guinaudeau O., Chiesa V., Quénel I. and Chau S. (2021). Aspect Based Sentiment Analysis using French Pre-Trained Models.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI, ISBN 978-989-758-484-8, pages 519-525. DOI: 10.5220/0010382705190525


in Bibtex Style

@conference{nlpinai21,
author={Abderrahman Essebbar and Bamba Kane and Ophélie Guinaudeau and Valeria Chiesa and Ilhem Quénel and Stéphane Chau},
title={Aspect Based Sentiment Analysis using French Pre-Trained Models},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI,},
year={2021},
pages={519-525},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010382705190525},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI,
TI - Aspect Based Sentiment Analysis using French Pre-Trained Models
SN - 978-989-758-484-8
AU - Essebbar A.
AU - Kane B.
AU - Guinaudeau O.
AU - Chiesa V.
AU - Quénel I.
AU - Chau S.
PY - 2021
SP - 519
EP - 525
DO - 10.5220/0010382705190525