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An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

Author

Listed:
  • Bin Wang
  • Enhui Wang
  • Zikun Zhu
  • Yangyang Sun
  • Yaodong Tao
  • Wei Wang

Abstract

“Social sensors†refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the “COVID-19†collected from 1 May 2020 to 9 July 2020. We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. We analyze the influence of users’ features on the sentiment. The prediction accuracy of our model is 64.88%.

Suggested Citation

  • Bin Wang & Enhui Wang & Zikun Zhu & Yangyang Sun & Yaodong Tao & Wei Wang, 2021. "An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors," International Journal of Distributed Sensor Networks, , vol. 17(10), pages 15501477211, October.
    • Handle: RePEc:sae:intdis:v:17:y:2021:i:10:p:15501477211033765
    DOI: 10.1177/15501477211033765
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    References listed on IDEAS

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    1. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    2. Dan Yang & Jing Zhang & Sifeng Wang & XueDong Zhang, 2019. "A Time-Aware CNN-Based Personalized Recommender System," Complexity, Hindawi, vol. 2019, pages 1-11, December.
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