Mining citizen emotions to estimate the urgency of urban issues |
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| Affiliation: | 1. Advanced Material Science, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Japan;2. Advanced Operando-Measurement Technology Open Innovation Laboratory (OPERANDO-OIL), National Institute of Advanced Industrial Science and Technology (AIST), The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Japan;3. Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University, 2-1-1 Katahira Aoba-ku, Sendai, Miyagi, Japan;4. Bio- and Soft-Materials Group, Research & Utilization Division, Japan Synchrotron Radiation Research Institute, 1-1-1 Kouto Sayo-cho, Sayo-gun, Hyogo, Japan;5. Institute of Materials Structure Science, High Energy Accelerator Research Organization, 1-1 Oho, Tsukuba, Japan;6. Molecular Chemistry, Osaka University, 2-1 Yamada-oka, Osaka, Japan;1. Key Lab. of Machine Learning and Computational Intelligence, College of Mathematics and Information Science, Hebei University, Baoding, 071002, Hebei, China;2. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China;3. College of Computer Science and Software, Shenzhen University, Shenzhen 518060, China;4. College of Computer Science and Technology, Hebei University, Baoding, 071002, Hebei, China |
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| Abstract: | Crowdsourcing technology offers exciting possibilities for local governments. Specifically, citizens are increasingly taking part in reporting and discussing issues related to their neighborhood and problems they encounter on a daily basis, such as overflowing trash-bins, broken footpaths and lifts, illegal graffiti, and potholes. Pervasive citizen participation enables local governments to respond more efficiently to these urban issues. This interaction between citizens and municipalities is largely promoted by civic engagement platforms, such as See-Click-Fix, FixMyStreet, CitySourced, and OpenIDEO, which allow citizens to report urban issues by entering free text describing what needs to be done, fixed or changed. In order to develop appropriate action plans and priorities, government officials need to figure out how urgent are the reported issues. In this paper we propose to estimate the urgency of urban issues by mining different emotions that are implicit in the text describing the issue. More specifically, a reported issue is first categorized according to the emotions expressed in it, and then the corresponding emotion scores are combined in order to produce a final urgency level for the reported issue. Our experiments use the SeeClickFix hackathon data and diverse emotion classification algorithms. They indicate that (i) emotions can be categorized efficiently with supervised learning algorithms, and (ii) the use of citizen emotions leads to accurate urgency estimates. Further, using additional features such as the type of issue or its author leads to no further accuracy gains. |
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| Keywords: | Emotion classification Networked citizenship Crowdsourcing |
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