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Authors: Bamba Kane ; Ali Jrad ; Abderrahman Essebbar ; Ophélie Guinaudeau ; Valeria Chiesa ; Ilhem Quénel and Stéphane Chau

Affiliation: Research and Innovation Direction, ALTRAN Sophia-Antipolis, France

Keyword(s): Natural Language Processing (NLP), Aspect-Based Sentiment Analysis (ABSA), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Conditional Random Field (CRF), SemEval.

Abstract: Aspect Based Sentiment Analysis (ABSA) aims to detect the different aspects addressed in a text and the sentiment associated to each of them. There exists a lot of work on this topic for the English language, but only few models are adapted for French ABSA. In this paper, we propose a new model for ABSA, named CLC, which combines CNN (Convolutional Neural Network), Bidirectional LSTM (Long Short-Term Memory) and CRF (Conditional Random Field). We demonstrate herein its great performance on the SemEval2016 French dataset. We prove that our CLC model outperforms the state-of-the-art models for French ABSA. We also prove that CLC is well adapted for other languages such as English. One main strength of CLC is its ability to detect the aspects and the associated sentiments in a joint manner, unlike the state-of-the-art models which detect them separately.

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Paper citation in several formats:
Kane, B.; Jrad, A.; Essebbar, A.; Guinaudeau, O.; Chiesa, V.; Quénel, I. and Chau, S. (2021). CNN-LSTM-CRF for Aspect-Based Sentiment Analysis: A Joint Method Applied to French Reviews. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 498-505. DOI: 10.5220/0010382604980505

@conference{nlpinai21,
author={Bamba Kane. and Ali Jrad. and Abderrahman Essebbar. and Ophélie Guinaudeau. and Valeria Chiesa. and Ilhem Quénel. and Stéphane Chau.},
title={CNN-LSTM-CRF for Aspect-Based Sentiment Analysis: A Joint Method Applied to French Reviews},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI},
year={2021},
pages={498-505},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010382604980505},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI
TI - CNN-LSTM-CRF for Aspect-Based Sentiment Analysis: A Joint Method Applied to French Reviews
SN - 978-989-758-484-8
IS - 2184-433X
AU - Kane, B.
AU - Jrad, A.
AU - Essebbar, A.
AU - Guinaudeau, O.
AU - Chiesa, V.
AU - Quénel, I.
AU - Chau, S.
PY - 2021
SP - 498
EP - 505
DO - 10.5220/0010382604980505
PB - SciTePress

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