| Peer-Reviewed

Group Emotion Recognition for Weibo Topics Based on BERT with TextCNN

Received: 6 June 2023     Accepted: 26 June 2023     Published: 11 July 2023
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Abstract

Social media platforms, including Weibo, have become an integral part of people's daily lives, where users engage in discussions, share opinions, and express their emotions regarding trending topics. However, as the volume of information and content continues to increase, individuals face challenges in accessing relevant information. To address this issue, sentiment analysis has been employed in this study to focus on group sentiment identification for Weibo topics. Due to the potential involvement of multiple sentiment categories in Weibo topics, the main algorithm used in this research combines BERT and TextCNN for text multi-label classification. This approach aims to predict the possible collective emotional reactions of the public. Macro-F1 has been chosen as the evaluation criterion, with the baseline algorithm achieving a score of 0.3339, while our model achieved a slightly improved score of 0.3514. This improvement demonstrates the efficacy of the proposed algorithm. This paper makes full use of the self-attentive mechanism of BERT combined with the convolutional layer and pooling operation of TextCNN to extract local features. The generalization ability and sentiment classification accuracy of the model are improved. The results of text multi-label classification for group sentiment recognition of microblog topics demonstrate the superiority of the model algorithm in this paper. This study carries significant implications for understanding the public's emotional responses to popular topics on social media. It provides valuable insights for further exploration and advancement in the field of sentiment analysis within the realm of social media.

Published in American Journal of Information Science and Technology (Volume 7, Issue 3)
DOI 10.11648/j.ajist.20230703.11
Page(s) 95-100
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

BERT, TextCNN, Text Classification, NLP

References
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[5] Strubell E, Ganesh A, McCallum A. Energy and policy considerations for deep learning in NLP [J]. arXiv preprint arXiv: 1906.02243, 2019.
[6] Kang Y, Cai Z, Tan C W, et al. Natural language processing (NLP) in management research: A literature review [J]. Journal of Management Analytics, 2020, 7 (2): 139-172.
[7] Sadeghian A, Sharafat A R. Bag of words meets bags of popcorn [J]. CS224N Proj, 2015: 4-9.9.
[8] Xiao, K., Wang, C., Zhang, Q., Qian, Z., 2019. Food safety event detection based on multi-feature fusion. Symmetry 11, 1222.
[9] Qaiser S, Ali R. Text mining: use of TF-IDF to examine the relevance of words to documents [J]. International Journal of Computer Applications, 2018, 181 (1): 25-29.
[10] Yun-tao Z, Ling G, Yong-cheng W. An improved TF-IDF approach for text classification [J]. Journal of Zhejiang University-Science A, 2005, 6: 49-55.
[11] Zaremba, W., Sutskever, I., Vinyals, O., 2014. Recurrent neural network regularization. Eprint Arxiv.
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[13] Liu, P., Qiu, X., Huang, X., 2016. Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv: 1605.05101.
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  • APA Style

    Donghong Shan, Huili Li. (2023). Group Emotion Recognition for Weibo Topics Based on BERT with TextCNN. American Journal of Information Science and Technology, 7(3), 95-100. https://doi.org/10.11648/j.ajist.20230703.11

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    ACS Style

    Donghong Shan; Huili Li. Group Emotion Recognition for Weibo Topics Based on BERT with TextCNN. Am. J. Inf. Sci. Technol. 2023, 7(3), 95-100. doi: 10.11648/j.ajist.20230703.11

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    AMA Style

    Donghong Shan, Huili Li. Group Emotion Recognition for Weibo Topics Based on BERT with TextCNN. Am J Inf Sci Technol. 2023;7(3):95-100. doi: 10.11648/j.ajist.20230703.11

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  • @article{10.11648/j.ajist.20230703.11,
      author = {Donghong Shan and Huili Li},
      title = {Group Emotion Recognition for Weibo Topics Based on BERT with TextCNN},
      journal = {American Journal of Information Science and Technology},
      volume = {7},
      number = {3},
      pages = {95-100},
      doi = {10.11648/j.ajist.20230703.11},
      url = {https://doi.org/10.11648/j.ajist.20230703.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajist.20230703.11},
      abstract = {Social media platforms, including Weibo, have become an integral part of people's daily lives, where users engage in discussions, share opinions, and express their emotions regarding trending topics. However, as the volume of information and content continues to increase, individuals face challenges in accessing relevant information. To address this issue, sentiment analysis has been employed in this study to focus on group sentiment identification for Weibo topics. Due to the potential involvement of multiple sentiment categories in Weibo topics, the main algorithm used in this research combines BERT and TextCNN for text multi-label classification. This approach aims to predict the possible collective emotional reactions of the public. Macro-F1 has been chosen as the evaluation criterion, with the baseline algorithm achieving a score of 0.3339, while our model achieved a slightly improved score of 0.3514. This improvement demonstrates the efficacy of the proposed algorithm. This paper makes full use of the self-attentive mechanism of BERT combined with the convolutional layer and pooling operation of TextCNN to extract local features. The generalization ability and sentiment classification accuracy of the model are improved. The results of text multi-label classification for group sentiment recognition of microblog topics demonstrate the superiority of the model algorithm in this paper. This study carries significant implications for understanding the public's emotional responses to popular topics on social media. It provides valuable insights for further exploration and advancement in the field of sentiment analysis within the realm of social media.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Group Emotion Recognition for Weibo Topics Based on BERT with TextCNN
    AU  - Donghong Shan
    AU  - Huili Li
    Y1  - 2023/07/11
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ajist.20230703.11
    DO  - 10.11648/j.ajist.20230703.11
    T2  - American Journal of Information Science and Technology
    JF  - American Journal of Information Science and Technology
    JO  - American Journal of Information Science and Technology
    SP  - 95
    EP  - 100
    PB  - Science Publishing Group
    SN  - 2640-0588
    UR  - https://doi.org/10.11648/j.ajist.20230703.11
    AB  - Social media platforms, including Weibo, have become an integral part of people's daily lives, where users engage in discussions, share opinions, and express their emotions regarding trending topics. However, as the volume of information and content continues to increase, individuals face challenges in accessing relevant information. To address this issue, sentiment analysis has been employed in this study to focus on group sentiment identification for Weibo topics. Due to the potential involvement of multiple sentiment categories in Weibo topics, the main algorithm used in this research combines BERT and TextCNN for text multi-label classification. This approach aims to predict the possible collective emotional reactions of the public. Macro-F1 has been chosen as the evaluation criterion, with the baseline algorithm achieving a score of 0.3339, while our model achieved a slightly improved score of 0.3514. This improvement demonstrates the efficacy of the proposed algorithm. This paper makes full use of the self-attentive mechanism of BERT combined with the convolutional layer and pooling operation of TextCNN to extract local features. The generalization ability and sentiment classification accuracy of the model are improved. The results of text multi-label classification for group sentiment recognition of microblog topics demonstrate the superiority of the model algorithm in this paper. This study carries significant implications for understanding the public's emotional responses to popular topics on social media. It provides valuable insights for further exploration and advancement in the field of sentiment analysis within the realm of social media.
    VL  - 7
    IS  - 3
    ER  - 

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Author Information
  • SoftWare School, Pingdingshan University, Pingdingshan, China

  • SoftWare School, Pingdingshan University, Pingdingshan, China

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