{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:09:30Z","timestamp":1779397770331,"version":"3.53.1"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,15]],"date-time":"2019-01-15T00:00:00Z","timestamp":1547510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The People's Republic of China ministry of science and technology","award":["2016YFE0122600"],"award-info":[{"award-number":["2016YFE0122600"]}]},{"name":"The People's Republic of China ministry of science and technology","award":["41771476"],"award-info":[{"award-number":["41771476"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya\u2019an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies.<\/jats:p>","DOI":"10.3390\/ijgi8010029","type":"journal-article","created":{"date-parts":[[2019,1,16]],"date-time":"2019-01-16T03:09:13Z","timestamp":1547608153000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7405-6697","authenticated-orcid":false,"given":"Tengfei","family":"Yang","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jibo","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqing","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1700-5943","authenticated-orcid":false,"given":"Naixia","family":"Mou","sequence":"additional","affiliation":[{"name":"College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanzhao","family":"Tian","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7838-9832","authenticated-orcid":false,"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,15]]},"reference":[{"key":"ref_1","first-page":"8","article-title":"Citizens as sensors: Web 2.0 and the volunteering of geographic information","volume":"7","author":"Goodchild","year":"2007","journal-title":"GeoFocus. Rev. Int. Ciencia Tecnol. Inf. Geogr."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Yang, L., Van de Walle, B., and Han, C. (2013, January 7\u201310). Classification of microblogs for support emergency responses: Case study Yushu earthquake in China. Proceedings of the 2013 46th Hawaii International Conference on System Sciences, Wailea, Maui, HI, USA.","DOI":"10.1109\/HICSS.2013.129"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Qu, Y., Huang, C., Zhang, P., and Zhang, J. (2010, January 19\u201323). Microblogging after a major disaster in China: A case study of the 2010 Yushu earthquake. Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, Hangzhou, China.","DOI":"10.1145\/1958824.1958830"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.cag.2013.10.008","article-title":"Special Section on Visual Analytics: Public behavior response analysis in disaster events utilizing visual analytics of microblog data","volume":"38","author":"Chae","year":"2014","journal-title":"Comput. Graph."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, C., Emrich, C.T., and Guo, D. (2017). A novel approach to leveraging social media for rapid flood mapping: A case study of the 2015 South Carolina floods. Cartogr. Geogr. Inf. Sci., 1\u201314.","DOI":"10.1080\/15230406.2016.1271356"},{"key":"ref_6","unstructured":"(2013). Social Media in Disasters and Emergencies, The Drum."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dalgleish, T., and Power, M.J. (1999). Basic Emotions. Handbook of Cognition & Emotion, John Wiley & Sons.","DOI":"10.1002\/0470013494"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gruebner, O., Lowe, S.R., Sykora, M., Shankardass, K., Subramanian, S.V., and Galea, S. (2017). A novel surveillance approach for disaster mental health. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0181233"},{"key":"ref_9","unstructured":"Lindell, M.K., Prater, C.S., Perry, R.W., and Nicholson, W.C. (2006). Fundamentals of Emergency Management, Emond Montgomery Publications."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1207\/s15327078in0603_9","article-title":"An Event\u2014Emotion or Event\u2014Expression Hypothesis? A Comment on the Commentaries on Bennett, Bendersky, and Lewis (2002)","volume":"6","author":"Camras","year":"2010","journal-title":"Infancy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1177\/028072709201000103","article-title":"Initial behavioural response to a rapid onset disaster","volume":"10","author":"Goltz","year":"1992","journal-title":"Int. J. Mass Emerg. Disasters"},{"key":"ref_12","unstructured":"Yi, J., Nasukawa, T., Bunescu, R., and Niblack, W. (2003, January 22). Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques. Proceedings of the IEEE International Conference on Data Mining, Melbourne, FL, USA."},{"key":"ref_13","unstructured":"Xu, R., Wong, K.F., and Xia, Y. (2008, January 16\u201319). Coarse-Fine Opinion Mining\u2014WIA in NTCIR-7 MOAT Task. Proceedings of the NTCIR 2008, Tokyo, Japan."},{"key":"ref_14","unstructured":"Turney, P.D. (2012, January 6). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Proceedings of the Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.jpdc.2017.10.018","article-title":"Using convolution control block for Chinese sentiment analysis","volume":"116","author":"Xiao","year":"2017","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Narayanan, V., Arora, I., and Bhatia, A. (2013). Fast and accurate sentiment classification using an enhanced Naive Bayes model. Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning, Springer.","DOI":"10.1007\/978-3-642-41278-3_24"},{"key":"ref_17","first-page":"79","article-title":"Thumbs up? Sentiment Classification using Machine Learning","volume":"10","author":"Pang","year":"2002","journal-title":"Empir. Methods Nat. Lang. Process."},{"key":"ref_18","unstructured":"Jozefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., and Wu, Y. (arXiv, 2016). Exploring the Limits of Language Modeling, arXiv."},{"key":"ref_19","first-page":"1137","article-title":"A neural probabilistic language model","volume":"3","author":"Bengio","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_20","first-page":"207","article-title":"60,000 Disaster Victims Speak: Part I. An Empirical Review of the Empirical Literature, 1981\u20132001","volume":"65","author":"Norris","year":"2002","journal-title":"Psychiatry-Interpers. Biol. Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1146\/annurev-publhealth-032013-182435","article-title":"Mental health consequences of disasters","volume":"35","author":"Goldmann","year":"2014","journal-title":"Ann. Rev. Public Health"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2571","DOI":"10.1001\/jama.2012.110700","article-title":"Mental health effects of Hurricane Sandy: Characteristics, potential aftermath, and response","volume":"308","author":"Neria","year":"2012","journal-title":"J. Am. Med. Assoc."},{"key":"ref_23","unstructured":"Coyle, D., and Meier, P. (2009). New technologies in emergencies and conflicts: The role of information and social networks. Washington D, Available online: https:\/\/www.popline.org\/node\/209135."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1177\/0261927X09351676","article-title":"The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods","volume":"29","author":"Tausczik","year":"2009","journal-title":"J. Lang. Soc. Psychol."},{"key":"ref_25","unstructured":"Oh, O., Kwon, K.H., and Rao, H.R. (2010, January 12\u201315). An Exploration of Social Media in Extreme Events: Rumor Theory and Twitter during the Haiti Earthquake 2010. Proceedings of the International Conference on Information Systems, Icis 2010, Saint Louis, MO, USA."},{"key":"ref_26","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","volume":"26","author":"Mikolov","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (arXiv, 2013). Efficient estimation of word representations in vector space, arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mikolov, T., Kopecky, J., Burget, L., Glembek, O., and Cernocky, J. (2009, January 19\u201324). Neural network based language models for highly inflective languages. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan.","DOI":"10.1109\/ICASSP.2009.4960686"},{"key":"ref_29","first-page":"43","article-title":"The Architecture of Word2vec and Its Applications","volume":"1","author":"Xiong","year":"2015","journal-title":"J. Nanjing Norm. Univ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification, Association for Computational Linguistics.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_31","unstructured":"Zhang, D., and Wang, D. (2019, January 12). Relation Classification: CNN or RNN?. Available online: https:\/\/link.springer.com\/chapter\/10.1007\/978-3-319-50496-4_60."},{"key":"ref_32","unstructured":"Pappas, N., and Popescu-Belis, A. (arXiv, 2017). Multilingual Hierarchical Attention Networks for Document Classification, arXiv."},{"key":"ref_33","unstructured":"Yin, W., Kann, K., Yu, M., and Schtze, H. (arXiv, 2017). Comparative Study of CNN and RNN for Natural Language Processing, arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., and Liu, T. (2015, January 17\u201321). Document modeling with gated recurrent neural network for sentiment classification. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1167"},{"key":"ref_35","unstructured":"(2019, January 12). Report on Text Classification Using CNN, RNN & HAN. Available online: https:\/\/medium.com\/jatana\/report-on-text-classification-using-cnn-rnn-han-f0e887214d5f."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Collobert, R., and Weston, J. (2008, January 5\u20139). A unified architecture for natural language processing:deep neural networks with multitask learning. Proceedings of the International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390177"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, Y., Feng, S., Wang, D., Yu, G., and Zhang, Y. (2016, January 23\u201325). Multi-label Chinese Microblog Emotion Classification via Convolutional Neural Network. Proceedings of the Web Technologies and Applications: 18th Asia-Pacific Web Conference, APWeb 2016, Suzhou, China.","DOI":"10.1007\/978-3-319-45814-4_46"},{"key":"ref_38","unstructured":"Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis, Chapman and Hall."},{"key":"ref_39","unstructured":"(2019, January 12). Word2Vec. Available online: https:\/\/code.google.com\/archive\/p\/word2vec\/."},{"key":"ref_40","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (arXiv, 2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, arXiv."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/1\/29\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:25:59Z","timestamp":1760185559000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/1\/29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,15]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["ijgi8010029"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8010029","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,15]]}}}