# Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method
## 1 Introduction  
Environmental quality has become a critical factor for improving urban sustainability [1]. In the era of big data, smart cities provide citizens with a better living environment, which has become an emerging model of world city development. Its essence lies in the high integration of informatization and urbanization. With the rapid development of information technology and the increase in citizens' environmental awareness [2], it is more convenient to make environmental pollution complaints with the help of mobile phones and social networks. Citizens are more active in expressing their subjective feelings about environmental pollution. For example, in 2019, China's \"12369\" environmental protection reporting network management platform received more than 530,000 environmental complaints records from the public, of which Guangdong Province ranked second. Environmental complaint data are unstructured text data, which have different data analysis methods from traditional environmental sensor networks (such as the airquality monitoring network or water pollution monitoring network); furthermore, the density of environmental complaints is much higher than that of any of the current environmental sensor network sites. Massive environmental complaints have produced huge text data containing rich information, such as the characteristics of the pollution source, the information of stakeholders, and the perception regarding the complainants. However, previous studies of environmental complaints mostly focused on correlating environmental complaints with socio-economic factors or individual features, including economic development, geographical location, household income, literacy rate, environmental management, age, gender, education quality, perception, which played significant roles in determining civil environmental complaints. For example, [PERSON] and [PERSON] [3] evaluated the influencing factors of civil environmental complaints based on an econometric model, which proved basic education has a significant effect on complaint behaviors. [PERSON] and [PERSON] [4] further demonstrated the influence of education and income on local environmental complaints. [PERSON] et al. [5] demonstrated that exposure to harmful pollutants and household income significantly influence people's complaint behaviors at the provincial level based on economic willingness-to-pay models. [PERSON] [6] verified that the perception of environmental information significantly determined citizens' environmental complaints by questionnaire survey and various multivariate regressions. [PERSON] and [PERSON] [1] explored the relationships between noise complaints and socio-economic factors at the city/region level. Some works indicated social psychological factors that impact environmental complaint behavior on the individual level based on the norm activation model and revealed that the personal norm is the most immediate and powerful predictor of environmental complaint intention [7; 8]. Few scholars have discussed the relationship between environmental monitoring data and environmental complaints. [PERSON] et al. [9] revealed that hydrocarbons have the highest correlation with the total number of citizen complaints by analyzing the correlation between air measurement results and public complaints. The environmental complaint is one of the most important channels that allows a deeper understanding of the local environment; provides a useful instrument for developing suitable environmental policies; and positively impacts pollution control [10; 11; 12]. [PERSON] et al. [13] constructed an approach to the field of environmental governance by considering youth complaints as an important source of information for the management authorities and verified the effectiveness of the complaint information on environmental governance. [PERSON] et al. [14] showed that public participation policy plays a significant role in improving environmental governance.