Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 손동영 | - |
dc.date.accessioned | 2019-10-02T06:16:13Z | - |
dc.date.available | 2019-10-02T06:16:13Z | - |
dc.date.issued | 2019-04 | - |
dc.identifier.citation | Proceedings of the ACM Symposium on Applied Computing, Page. 2120-2123 | en_US |
dc.identifier.isbn | 978-1-4503-5933-7 | - |
dc.identifier.uri | https://dl.acm.org/citation.cfm?doid=3297280.3297619 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/110827 | - |
dc.description.abstract | In this paper, we present a tool for analyzing spatio-temporal distribution of social anxiety. Twitter, one of the most popular social network services, has been chosen as data source for analysis of social anxiety. Tweets (posted on the Twitter) contain various emotions and thus these individual emotions reflect social atmosphere and public opinion, which are often dependent on spatial and temporal factors. The reason why we choose anxiety among various emotions is that anxiety is very important emotion that is useful for observing and understanding social events of communities. We develop a machine learning based tool to analyze the changes of social atmosphere spatially and temporally. Our tool classifies whether each Tweet contains anxious content or not, and also estimates degree of Tweet anxiety. Furthermore, it also visualizes spatio-temporal distribution of anxiety as a form of web application, which is incorporated with physical map, word cloud, search engine and chart viewer. Our tool is applied to a big tweet data in South Korea to illustrate its usefulness for exploring social atmosphere and public opinion spatio-temporally. | en_US |
dc.description.sponsorship | This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2015R1D1A1A01060950) and also supported by the Technology Innovation Programs (No.10077553 and No.10060086) funded by the Ministry of Trade, industry & Energy (MOTIE, Korea) | en_US |
dc.language.iso | en | en_US |
dc.publisher | Association for Computing Machinery | en_US |
dc.subject | SNS (Social Networking Service) | en_US |
dc.subject | Spatio-Temporal Information | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Naïve Bayes Classifier | en_US |
dc.subject | Visualization | en_US |
dc.title | A tool for spatio-temporal analysis of social anxiety with Twitter data | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1145/3297280.3297619 | - |
dc.relation.page | 2120-2123 | - |
dc.contributor.googleauthor | Lee, Joohong | - |
dc.contributor.googleauthor | Sohn, Dongyoung | - |
dc.contributor.googleauthor | Choi, Yong Suk | - |
dc.relation.code | 20190164 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF SOCIAL SCIENCES[S] | - |
dc.sector.department | DEPARTMENT OF MEDIA COMMUNICATION | - |
dc.identifier.pid | dysohn | - |
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