| Title |
Detecting Changes in Perceptions towards Smart City on Chinese Social Media: A Text Mining and Sentiment Analysis |
| ID_Doc |
36106 |
| Authors |
Yue, AB; Mao, C; Chen, LY; Liu, ZB; Zhang, CJ; Li, ZQ; Li, ZA |
| Title |
Detecting Changes in Perceptions towards Smart City on Chinese Social Media: A Text Mining and Sentiment Analysis |
| Year |
2022 |
| Published |
Buildings, 12, 8 |
| DOI |
10.3390/buildings12081182 |
| Abstract |
Examining the public's attention and comments on smart city topics in social media can help enable a full understanding of the development characteristics of smart cities, and provide a realistic reference for improving the level of public participation and citizens' sense of acquisition in smart city construction. Based on Sina Weibo, a well-known social media platform in China, over 230,000 public comments related to smart cities were extracted to analyze. Using LDA (Latent Dirichlet Assignment) and CNN-BiLSTM (Convolutional Neural Network and Bi-directional long and short memory) models, a topic mining and sentiment analysis model for user comments was constructed to study the current state of public perception of smart city concepts. The results demonstrate that public discussions on smart cities were macro-oriented, focusing on strategic layout and technical applications. As public awareness of smart cities deepens, topics about application scenarios and social services are gradually emphasized. The public's positive sentiment toward smart cities dominates and varies in sentiment intensity across years; the positive sentiment intensity of individual users on smart city ideas is significantly lower than that of official certified Weibo users, such as government departments and corporate organizations, which reveals the identity and temporal characteristics of public participation in cyberspace. |
| Author Keywords |
smart city; public perception; topics detection; sentiment change |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000845092100001 |
| WoS Category |
Construction & Building Technology; Engineering, Civil |
| Research Area |
Construction & Building Technology; Engineering |
| PDF |
https://www.mdpi.com/2075-5309/12/8/1182/pdf?version=1659955353
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