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Sustainability 2017, 9(2), 85;

Monitoring Environmental Quality by Sniffing Social Media

International School of Software, Wuhan University, Wuhan 430079, China
School of Software, East China University of Technology, Nanchang 330013, China
Author to whom correspondence should be addressed.
Academic Editors: Yichun Xie, Xinyue Ye and Clio Andris
Received: 9 November 2016 / Revised: 22 December 2016 / Accepted: 5 January 2017 / Published: 10 February 2017
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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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)

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Wang, Z.; Ke, L.; Cui, X.; Yin, Q.; Liao, L.; Gao, L.; Wang, Z. Monitoring Environmental Quality by Sniffing Social Media. Sustainability 2017, 9, 85.

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