@inproceedings{zhou-etal-2021-hate,
title = "Hate Speech Detection Based on Sentiment Knowledge Sharing",
author = "Zhou, Xianbing and
Yong, Yang and
Fan, Xiaochao and
Ren, Ge and
Song, Yunfeng and
Diao, Yufeng and
Yang, Liang and
Lin, Hongfei",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.556",
doi = "10.18653/v1/2021.acl-long.556",
pages = "7158--7166",
abstract = "The wanton spread of hate speech on the internet brings great harm to society and families. It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. In other words, getting more affective features from other affective resources will significantly affect the performance of hate speech detection. In this paper, we propose a hate speech detection framework based on sentiment knowledge sharing. While extracting the affective features of the target sentence itself, we make better use of the sentiment features from external resources, and finally fuse features from different feature extraction units to detect hate speech. Experimental results on two public datasets demonstrate the effectiveness of our model.",
}
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<abstract>The wanton spread of hate speech on the internet brings great harm to society and families. It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. In other words, getting more affective features from other affective resources will significantly affect the performance of hate speech detection. In this paper, we propose a hate speech detection framework based on sentiment knowledge sharing. While extracting the affective features of the target sentence itself, we make better use of the sentiment features from external resources, and finally fuse features from different feature extraction units to detect hate speech. Experimental results on two public datasets demonstrate the effectiveness of our model.</abstract>
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%0 Conference Proceedings
%T Hate Speech Detection Based on Sentiment Knowledge Sharing
%A Zhou, Xianbing
%A Yong, Yang
%A Fan, Xiaochao
%A Ren, Ge
%A Song, Yunfeng
%A Diao, Yufeng
%A Yang, Liang
%A Lin, Hongfei
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F zhou-etal-2021-hate
%X The wanton spread of hate speech on the internet brings great harm to society and families. It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. In other words, getting more affective features from other affective resources will significantly affect the performance of hate speech detection. In this paper, we propose a hate speech detection framework based on sentiment knowledge sharing. While extracting the affective features of the target sentence itself, we make better use of the sentiment features from external resources, and finally fuse features from different feature extraction units to detect hate speech. Experimental results on two public datasets demonstrate the effectiveness of our model.
%R 10.18653/v1/2021.acl-long.556
%U https://aclanthology.org/2021.acl-long.556
%U https://doi.org/10.18653/v1/2021.acl-long.556
%P 7158-7166
Markdown (Informal)
[Hate Speech Detection Based on Sentiment Knowledge Sharing](https://aclanthology.org/2021.acl-long.556) (Zhou et al., ACL-IJCNLP 2021)
ACL
- Xianbing Zhou, Yang Yong, Xiaochao Fan, Ge Ren, Yunfeng Song, Yufeng Diao, Liang Yang, and Hongfei Lin. 2021. Hate Speech Detection Based on Sentiment Knowledge Sharing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7158–7166, Online. Association for Computational Linguistics.