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Community Governance Based on Sentiment Analysis: Towards Sustainable Management and Development

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  • Xudong Zhang

    (College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China)

  • Zejun Yan

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Qianfeng Wu

    (Zhejiang Economic Information Center, Hangzhou 310006, China)

  • Ke Wang

    (College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China)

  • Kelei Miao

    (College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China)

  • Zhangquan Wang

    (College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China)

  • Yourong Chen

    (College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China)

Abstract

The promotion of community governance by digital means is an important research topic in developing smart cities. Currently, community governance is mostly based on reactive response, which lacks timely and proactive technical means for emergency monitoring. The easiest way for residents to contact their properties is to call the property call center, and the call centers of many properties store many speech data. However, text sentiment classification in community scenes still faces challenges such as small corpus size, one-sided sentiment feature extraction, and insufficient sentiment classification accuracy. To address such problems, we propose a novel community speech text sentiment classification algorithm combining two-channel features and attention mechanisms to obtain effective emotional information and provide decision support for the emergency management of public emergencies. Firstly, text vectorization based on word position information is proposed, and a SKEP-based community speech–text enhancement model is constructed to obtain the corresponding corpus. Secondly, a dual-channel emotional text feature extraction method that integrates spatial and temporal sequences is proposed to extract diverse emotional features effectively. Finally, an improved cross-entropy loss function suitable for community speech text is proposed for model training, which can achieve sentiment analysis and obtain all aspects of community conditions. The proposed method is conducive to improving community residents’ sense of happiness, satisfaction, and fulfillment, enhancing the effectiveness and resilience of urban community governance.

Suggested Citation

  • Xudong Zhang & Zejun Yan & Qianfeng Wu & Ke Wang & Kelei Miao & Zhangquan Wang & Yourong Chen, 2023. "Community Governance Based on Sentiment Analysis: Towards Sustainable Management and Development," Sustainability, MDPI, vol. 15(3), pages 1-17, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2684-:d:1055049
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    References listed on IDEAS

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    1. Jose Ramon Saura & Pedro Palos-Sanchez & Antonio Grilo, 2019. "Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining," Sustainability, MDPI, vol. 11(3), pages 1-14, February.
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    Cited by:

    1. Zahra Ahanin & Maizatul Akmar Ismail & Narinderjit Singh Sawaran Singh & Ammar AL-Ashmori, 2023. "Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages," Sustainability, MDPI, vol. 15(16), pages 1-24, August.

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