Monitoring Environmental Quality by Sniffing Social MediaReport as inadecuate


Monitoring Environmental Quality by Sniffing Social Media


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1

International School of Software, Wuhan University, Wuhan 430079, China

2

School of Software, East China University of Technology, Nanchang 330013, China





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Academic Editors: Yichun Xie, Xinyue Ye and Clio Andris

Abstract Nowadays, the environmental pollution and degradation in China has become a serious problem with the rapid development of Chinese heavy industry and increased energy generation. With sustainable development being the key to solving these problems, it is necessary to develop proper techniques for monitoring environmental quality. Compared to traditional environment monitoring methods utilizing expensive and complex instruments, we recognized that social media analysis is an efficient and feasible alternative to achieve this goal with the phenomenon that a growing number of people post their comments and feelings about their living environment on social media, such as blogs and personal websites. In this paper, we self-defined a term called the Environmental Quality Index EQI to measure and represent people’s overall attitude and sentiment towards an area’s environmental quality at a specific time; it includes not only metrics for water and food quality but also people’s feelings about air pollution. In the experiment, a high sentiment analysis and classification precision of 85.67% was obtained utilizing the support vector machine algorithm, and we calculated and analyzed the EQI for 27 provinces in China using the text data related to the environment from the Chinese Sina micro-blog and Baidu Tieba collected from January 2015 to June 2016. By comparing our results to with the data from the Chinese Academy of Sciences CAS, we showed that the environment evaluation model we constructed and the method we proposed are feasible and effective. View Full-Text

Keywords: social media; environmental quality; environment monitoring; Support Vector Machine SVM social media; environmental quality; environment monitoring; Support Vector Machine SVM





Author: Zhibo Wang 1,2, Lei Ke 1, Xiaohui Cui 1,* , Qi Yin 1, Longfei Liao 1, Lu Gao 1 and Zhenyu Wang 1

Source: http://mdpi.com/



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