Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.17645/up.v1i2.617 |
Resumo: | Traditional urban planning processes typically happen in offices and behind desks. Modern types of civic participation can enhance those processes by acquiring citizens’ ideas and feedback in participatory sensing approaches like “People as Sensors”. As such, citizen-centric planning can be achieved by analysing Volunteered Geographic Information (VGI) data such as Twitter tweets and posts from other social media channels. These user-generated data comprise several information dimensions, such as spatial and temporal information, and textual content. However, in previous research, these dimensions were generally examined separately in single-disciplinary approaches, which does not allow for holistic conclusions in urban planning. This paper introduces TwEmLab, an interdisciplinary approach towards extracting citizens’ emotions in different locations within a city. More concretely, we analyse tweets in three dimensions (space, time, and linguistics), based on similarities between each pair of tweets as defined by a specific set of functional relationships in each dimension. We use a graph-based semi-supervised learning algorithm to classify the data into discrete emotions (happiness, sadness, fear, anger/disgust, none). Our proposed solution allows tweets to be classified into emotion classes in a multi-parametric approach. Additionally, we created a manually annotated gold standard that can be used to evaluate TwEmLab’s performance. Our experimental results show that we are able to identify tweets carrying emotions and that our approach bears extensive potential to reveal new insights into citizens’ perceptions of the city. |
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Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithmintegrated space-time-linguistics methodology; participatory planning; semi-supervised learning; Twitter emotionsTraditional urban planning processes typically happen in offices and behind desks. Modern types of civic participation can enhance those processes by acquiring citizens’ ideas and feedback in participatory sensing approaches like “People as Sensors”. As such, citizen-centric planning can be achieved by analysing Volunteered Geographic Information (VGI) data such as Twitter tweets and posts from other social media channels. These user-generated data comprise several information dimensions, such as spatial and temporal information, and textual content. However, in previous research, these dimensions were generally examined separately in single-disciplinary approaches, which does not allow for holistic conclusions in urban planning. This paper introduces TwEmLab, an interdisciplinary approach towards extracting citizens’ emotions in different locations within a city. More concretely, we analyse tweets in three dimensions (space, time, and linguistics), based on similarities between each pair of tweets as defined by a specific set of functional relationships in each dimension. We use a graph-based semi-supervised learning algorithm to classify the data into discrete emotions (happiness, sadness, fear, anger/disgust, none). Our proposed solution allows tweets to be classified into emotion classes in a multi-parametric approach. Additionally, we created a manually annotated gold standard that can be used to evaluate TwEmLab’s performance. Our experimental results show that we are able to identify tweets carrying emotions and that our approach bears extensive potential to reveal new insights into citizens’ perceptions of the city.Cogitatio2016-07-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.17645/up.v1i2.617oai:ojs.cogitatiopress.com:article/617Urban Planning; Vol 1, No 2 (2016): Volunteered Geographic Information and the City; 114-1272183-7635reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttps://www.cogitatiopress.com/urbanplanning/article/view/617https://doi.org/10.17645/up.v1i2.617https://www.cogitatiopress.com/urbanplanning/article/view/617/617Copyright (c) 2016 Bernd Resch, Anja Summa, Peter Zeile, Michael Strubehttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessResch, BerndSumma, AnjaZeile, PeterStrube, Michael2022-12-20T11:00:07Zoai:ojs.cogitatiopress.com:article/617Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:22:03.533223Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm |
title |
Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm |
spellingShingle |
Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm Resch, Bernd integrated space-time-linguistics methodology; participatory planning; semi-supervised learning; Twitter emotions |
title_short |
Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm |
title_full |
Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm |
title_fullStr |
Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm |
title_full_unstemmed |
Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm |
title_sort |
Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm |
author |
Resch, Bernd |
author_facet |
Resch, Bernd Summa, Anja Zeile, Peter Strube, Michael |
author_role |
author |
author2 |
Summa, Anja Zeile, Peter Strube, Michael |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Resch, Bernd Summa, Anja Zeile, Peter Strube, Michael |
dc.subject.por.fl_str_mv |
integrated space-time-linguistics methodology; participatory planning; semi-supervised learning; Twitter emotions |
topic |
integrated space-time-linguistics methodology; participatory planning; semi-supervised learning; Twitter emotions |
description |
Traditional urban planning processes typically happen in offices and behind desks. Modern types of civic participation can enhance those processes by acquiring citizens’ ideas and feedback in participatory sensing approaches like “People as Sensors”. As such, citizen-centric planning can be achieved by analysing Volunteered Geographic Information (VGI) data such as Twitter tweets and posts from other social media channels. These user-generated data comprise several information dimensions, such as spatial and temporal information, and textual content. However, in previous research, these dimensions were generally examined separately in single-disciplinary approaches, which does not allow for holistic conclusions in urban planning. This paper introduces TwEmLab, an interdisciplinary approach towards extracting citizens’ emotions in different locations within a city. More concretely, we analyse tweets in three dimensions (space, time, and linguistics), based on similarities between each pair of tweets as defined by a specific set of functional relationships in each dimension. We use a graph-based semi-supervised learning algorithm to classify the data into discrete emotions (happiness, sadness, fear, anger/disgust, none). Our proposed solution allows tweets to be classified into emotion classes in a multi-parametric approach. Additionally, we created a manually annotated gold standard that can be used to evaluate TwEmLab’s performance. Our experimental results show that we are able to identify tweets carrying emotions and that our approach bears extensive potential to reveal new insights into citizens’ perceptions of the city. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-07-05 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.17645/up.v1i2.617 oai:ojs.cogitatiopress.com:article/617 |
url |
https://doi.org/10.17645/up.v1i2.617 |
identifier_str_mv |
oai:ojs.cogitatiopress.com:article/617 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://www.cogitatiopress.com/urbanplanning/article/view/617 https://doi.org/10.17645/up.v1i2.617 https://www.cogitatiopress.com/urbanplanning/article/view/617/617 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 Bernd Resch, Anja Summa, Peter Zeile, Michael Strube http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 Bernd Resch, Anja Summa, Peter Zeile, Michael Strube http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Cogitatio |
publisher.none.fl_str_mv |
Cogitatio |
dc.source.none.fl_str_mv |
Urban Planning; Vol 1, No 2 (2016): Volunteered Geographic Information and the City; 114-127 2183-7635 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799130666610196480 |