# Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method
## 4 Conclusions  
In this study, a framework for the textual analysis of Chinese environmental protection complaints was established, and the two-year civil environmental complaint records in Guangzhou city were analyzed using this framework. The conclusions show the following: (1) Civil environmental complaint characteristics can be identified. Keywords of various types of environmental complaints can be automatically and effectively extracted by TF-IDF, such as \"lampblack\" and \"exhaust gas\" in air pollution and \"LED lights\" in light pollution, which provides an accurate entry point for solving urban environmental problems. It also provides technical support for smart city environmental management. (2) The overall sentiment of environmental complaints is negative. Light pollution complaints are the most negative, and EM radiation complaints have the most fluctuating emotions, which may be caused by differences in citizen perception of EM radiation. (3) The semantic network nodes of the six types of environmental complaints reveal that the public pays the most attention to the pollution sources when complaining but the least attention to stakeholders, which may reduce the efficiency of environmental managers in handling complaints. (4) Besides the Ecology Environment Bureau, stakeholders in environmental complaints involve multiple government departments, including water affairs departments, urban management departments, and other departments. This not only reflects the complexity of environmental pollution but also shows that the issue of environmental complaints is deemed urgent by multiple departments. (5) The citizen semantic network indicates that pollution sources and pollution receptors are paid the most attention. Simultaneously, among different types of complaints, the pollution receptor's relationship with pollution behaviors (site selection, overnight construction), sensory features (stench, dazzle), stakeholders, and individual health are also highlighted by citizens. These relationships suggest that the pollution behavior of pollution sources, sensory features, environmental knowledge of pollution sources, and other details may become a crucial part of pollution management, which will provide more accurate management measures and be beneficial to smart urban environmental governance.  
For accurate text mining in further research, a rich corpus of environmental complaints must be established, and adaptable Chinese grammar for complaints needs to be summarized. Named-entity recognition could be considered, which will provide assistance in extracting detailed information about pollution incidents in semantic network analysis. Urban environmental management departments must establish a big data analysis system for environmental complaints based on text mining technology. Only in this way can urban environmental issues be effectively managed.  
[PERSON] developed the framework for textual analysis and performed the experiments, derived the models, and analyzed the data. [PERSON] was involved in part of the code work. [PERSON] wrote the manuscript in consultation with [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences [Grant No. XDA23030401] and the Youth Innovation Promotion Association, Chinese Academy of Sciences [Grant No. 2017351].  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.