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

Xudong Zhang, Zejun Yan, Qianfeng Wu, Ke Wang (), Kelei Miao, Zhangquan Wang and Yourong Chen
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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

Sustainability, 2023, vol. 15, issue 3, 1-17

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.

Keywords: smart city; sentiment classification; community governance; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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