Authors:
Abderrahman Essebbar
;
Bamba Kane
;
Ophélie Guinaudeau
;
Valeria Chiesa
;
Ilhem Quénel
and
Stéphane Chau
Affiliation:
Research and Innovation Direction, ALTRAN Sophia-Antipolis, France
Keyword(s):
Natural Language Processing, Aspect Based Sentiment Analysis, Sentiment Analysis, Pre-Trained Models, Sentence Pair Classification, Attention Encoder Network, Aspect Sentiment Classification, SemEval.
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.