Hungarian Aspect-based Sentiment Analysis with finetuned XLM-RoBERTa model
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- Pretrained model used: XLM-RoBERTa
- Finetuned on OpinHuBank (OHB) Corpus
- Labels: 0 (negative), 2 (neutral), 3 (positive)
- Separator: </s>
Limitations
- max_seq_length = 256
Results
Model | OHB |
---|---|
huBERT | 82.30 |
XLM-R | 80.59 |
Usage with pipeline
from transformers import pipeline
classification = pipeline(task="sentiment-analysis", model="NYTK/sentiment-ohb3-xlm-roberta-hungarian")
input_text = "Kovácsné Nagy Erzsébet </s> A Kovácsné Nagy Erzsébet nagyon jól érzi magát a Nokiánál, azonban a Németországból érkezett Kovács Péter nehezen boldogul a beilleszkedéssel."
print(classification(input_text)[0])
Citation
If you use this model, please cite the following paper:
@article {laki-yang-sentiment,
author = {Laki, László János and Yang, Zijian Győző},
title = {Sentiment Analysis with Neural Models for Hungarian},
journal = {Acta Polytechnica Hungarica},
year = {2023},
publisher = {Obuda University},
volume = {20},
number = {5},
doi = {10.12700/APH.20.5.2023.5.8},
pages= {109--128},
url = {https://acta.uni-obuda.hu/Laki_Yang_134.pdf}
}
@inproceedings {yang-asent,
title = {Neurális entitásorientált szentimentelemző alkalmazás magyar nyelvre},
booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)},
year = {2023},
publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
address = {Szeged, Hungary},
author = {Yang, Zijian Győző and Laki, László János},
pages = {107--117}
}
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