p-Norm Broad Learning for Negative Emotion Classification in Social Networks
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 prop...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2022-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020008 |
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| author | Guanghao Chen Sancheng Peng Rong Zeng Zhongwang Hu Lihong Cao Yongmei Zhou Zhouhao Ouyang Xiangyu Nie |
| author_facet | Guanghao Chen Sancheng Peng Rong Zeng Zhongwang Hu Lihong Cao Yongmei Zhou Zhouhao Ouyang Xiangyu Nie |
| author_sort | Guanghao Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7738573bcd0a4c4184931530c7538dc3 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-7738573bcd0a4c4184931530c7538dc32025-02-02T06:50:33ZengTsinghua University PressBig Data Mining and Analytics2096-06542022-09-015324525610.26599/BDMA.2022.9020008p-Norm Broad Learning for Negative Emotion Classification in Social NetworksGuanghao Chen0Sancheng Peng1Rong Zeng2Zhongwang Hu3Lihong Cao4Yongmei Zhou5Zhouhao Ouyang6Xiangyu Nie7Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510006, ChinaLaboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510006, ChinaGuangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, South China Normal University, Guangzhou 511400, ChinaSchool of Computer Science and Software, Zhaoqing University, Zhaoqing 526000, ChinaLaboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510006, ChinaSchool of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou 510006, ChinaSchool of Computing, University of Leeds, Leeds LS2 9JT, United KingdomSchool of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou 510006, ChinaNegative 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.https://www.sciopen.com/article/10.26599/BDMA.2022.9020008social networksnegative emotionrobertabroad learningp-norm |
| spellingShingle | Guanghao Chen Sancheng Peng Rong Zeng Zhongwang Hu Lihong Cao Yongmei Zhou Zhouhao Ouyang Xiangyu Nie p-Norm Broad Learning for Negative Emotion Classification in Social Networks Big Data Mining and Analytics social networks negative emotion roberta broad learning p-norm |
| title | p-Norm Broad Learning for Negative Emotion Classification in Social Networks |
| title_full | p-Norm Broad Learning for Negative Emotion Classification in Social Networks |
| title_fullStr | p-Norm Broad Learning for Negative Emotion Classification in Social Networks |
| title_full_unstemmed | p-Norm Broad Learning for Negative Emotion Classification in Social Networks |
| title_short | p-Norm Broad Learning for Negative Emotion Classification in Social Networks |
| title_sort | p norm broad learning for negative emotion classification in social networks |
| topic | social networks negative emotion roberta broad learning p-norm |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020008 |
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