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Open Access

p-Norm Broad Learning for Negative Emotion Classification in Social Networks

Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510006, China
Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, South China Normal University, Guangzhou 511400, China
School of Computer Science and Software, Zhaoqing University, Zhaoqing 526000, China
School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou 510006, China
School of Computing, University of Leeds, Leeds LS2 9JT, United Kingdom
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Negative emotion classification refers to the automatic classification of negative emotion of texts in social networks. Most existing methods are based on deep learning models, facing challenges such as complex structures and too many hyperparameters. To meet these challenges, in this paper, we propose a method for negative emotion classification utilizing a Robustly Optimized BERT Pretraining Approach (RoBERTa) and p-norm Broad Learning ( p-BL). Specifically, there are mainly three contributions in this paper. Firstly, we fine-tune the RoBERTa to adapt it to the task of negative emotion classification. Then, we employ the fine-tuned RoBERTa to extract features of original texts and generate sentence vectors. Secondly, we adopt p-BL to construct a classifier and then predict negative emotions of texts using the classifier. Compared with deep learning models, p-BL has advantages such as a simple structure that is only 3-layer and fewer parameters to be trained. Moreover, it can suppress the adverse effects of more outliers and noise in data by flexibly changing the value of p. Thirdly, we conduct extensive experiments on the public datasets, and the experimental results show that our proposed method outperforms the baseline methods on the tested datasets.


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Big Data Mining and Analytics
Pages 245-256
Cite this article:
Chen G, Peng S, Zeng R, et al. p-Norm Broad Learning for Negative Emotion Classification in Social Networks. Big Data Mining and Analytics, 2022, 5(3): 245-256.








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Received: 17 December 2021
Revised: 30 March 2022
Accepted: 31 March 2022
Published: 09 June 2022
© The author(s) 2022.

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