Abstract
Caroline Brun, Vassilina Nikoulina |
WASSA (Workshop EMNLP), Brussels, Belgium, 31 October-4 November, 2018 |
@inproceedings{brun-nikoulina-2018-aspect, title = "Aspect Based Sentiment Analysis into the Wild", author = "Brun, Caroline and Nikoulina, Vassilina", booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis", month = oct, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W18-6217", pages = "116--122", }
Abstract
In this paper, we test a state-of-the-art Aspect Based Sentiment Analysis system trained on a widely used dataset on “real” data. We created a new manually annotated dataset of user generated data from the same domain as the training dataset, but from other sources and analyse the differences between the new and the standard ABSA dataset. We then analyse the results in performance of different versions of the same system on both datasets. We also propose light adaptation methods to increase system robustness.
Details on the gender equality index score 2023 (related to year 2022) for NAVER France of 81/100.
NAVER France targets are as follows:
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2023 au titre des données 2022 : 81/100
Détail des indicateurs :
Les objectifs de progression de NAVER France sont :
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