Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method
With the popularization of social networks, the abundance of unstructured data regarding environmental complaints is rapidly increasing. This study established a text mining framework for Chinese civil environmental complaints and analyzed the characteristics of environmental complaints, including k...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/9/4087 |
| _version_ | 1797535678732435456 |
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| author | Yaran Jiao Chunming Li Yinglun Lin |
| author_facet | Yaran Jiao Chunming Li Yinglun Lin |
| author_sort | Yaran Jiao |
| collection | DOAJ |
| description | With the popularization of social networks, the abundance of unstructured data regarding environmental complaints is rapidly increasing. This study established a text mining framework for Chinese civil environmental complaints and analyzed the characteristics of environmental complaints, including keywords, sentiment, and semantic networks, with two–year environmental complaints records in Guangzhou city, China. The results show that the keywords of environmental complaints can be effectively extracted, providing an accurate entry point for solving environmental problems; light pollution complaints are the most negative, and electromagnetic radiation complaints have the most fluctuating emotions, which may be due to the diversity of citizens’ perceptions of pollution; the nodes of the semantic network reveal that citizens pay the most attention to pollution sources but the least attention to stakeholders; the edges of the semantic network shows that pollution sources and pollution receptors show the most concerning relationship, and the pollution receptors’ relationships with pollution behaviors, sensory features, stakeholders, and individual health are also highlighted by citizens. Thus, environmental pollution management should not only strengthen the control of pollution sources but also pay attention to these characteristics. This study provides an efficient technical method for unstructured data analysis, which may be helpful for precise and smart environmental management. |
| first_indexed | 2024-03-10T11:48:47Z |
| format | Article |
| id | doaj.art-049d08c9a29a4762b48c96305d7e36bf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:48:47Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-049d08c9a29a4762b48c96305d7e36bf2023-11-21T17:51:44ZengMDPI AGApplied Sciences2076-34172021-04-01119408710.3390/app11094087Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining MethodYaran Jiao0Chunming Li1Yinglun Lin2Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, ChinaKey Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, ChinaCollege of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaWith the popularization of social networks, the abundance of unstructured data regarding environmental complaints is rapidly increasing. This study established a text mining framework for Chinese civil environmental complaints and analyzed the characteristics of environmental complaints, including keywords, sentiment, and semantic networks, with two–year environmental complaints records in Guangzhou city, China. The results show that the keywords of environmental complaints can be effectively extracted, providing an accurate entry point for solving environmental problems; light pollution complaints are the most negative, and electromagnetic radiation complaints have the most fluctuating emotions, which may be due to the diversity of citizens’ perceptions of pollution; the nodes of the semantic network reveal that citizens pay the most attention to pollution sources but the least attention to stakeholders; the edges of the semantic network shows that pollution sources and pollution receptors show the most concerning relationship, and the pollution receptors’ relationships with pollution behaviors, sensory features, stakeholders, and individual health are also highlighted by citizens. Thus, environmental pollution management should not only strengthen the control of pollution sources but also pay attention to these characteristics. This study provides an efficient technical method for unstructured data analysis, which may be helpful for precise and smart environmental management.https://www.mdpi.com/2076-3417/11/9/4087environmental complainttext miningsemantic networksentiment analysissustainable cities |
| spellingShingle | Yaran Jiao Chunming Li Yinglun Lin Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method Applied Sciences environmental complaint text mining semantic network sentiment analysis sustainable cities |
| title | Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method |
| title_full | Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method |
| title_fullStr | Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method |
| title_full_unstemmed | Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method |
| title_short | Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method |
| title_sort | can urban environmental problems be accurately identified a complaint text mining method |
| topic | environmental complaint text mining semantic network sentiment analysis sustainable cities |
| url | https://www.mdpi.com/2076-3417/11/9/4087 |
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