Integrating argumentation and sentiment analysis for mining opinions from Twitter

Autores
Grosse, Kathrin; González, María Paula; Chesñevar, Carlos Iván; Maguitman, Ana Gabriela
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Social networks have grown exponentially in use and impact on the society as a whole. In particular, microblogging platforms such as Twitter have become important tools to assess public opinion on different issues. Recently, some approaches for assessing Twitter messages have been developed, identifying sentiments associated with relevant keywords or hashtags. However, such approaches have an important limitation, as they do not take into account contradictory and potentially inconsistent information which might emerge from relevant messages. We contend that the information made available in Twitter can be useful to extract a particular version of arguments (called “opinions” in our formalization) which emerge bottom-up from the social interaction associated with such messages. In this paper we present a novel framework which allows to mine opinions from Twitter based on incrementally generated queries. As a result, we will be able to obtain an “opinion tree”, rooted in the first original query. Distinguished, conflicting elements in an opinion tree lead to so-called “conflict trees”, which resemble dialectical trees as those used traditionally in defeasible argumentation.
Fil: Grosse, Kathrin. Universität Osnabrück. Institut für Kognitionswissenschaft; Alemania
Fil: González, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
Fil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
Fil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
Materia
Artificial Intelligence
Argumentation
Opinion Mining
Social Media
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/45644

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spelling Integrating argumentation and sentiment analysis for mining opinions from TwitterGrosse, KathrinGonzález, María PaulaChesñevar, Carlos IvánMaguitman, Ana GabrielaArtificial IntelligenceArgumentationOpinion MiningSocial Mediahttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Social networks have grown exponentially in use and impact on the society as a whole. In particular, microblogging platforms such as Twitter have become important tools to assess public opinion on different issues. Recently, some approaches for assessing Twitter messages have been developed, identifying sentiments associated with relevant keywords or hashtags. However, such approaches have an important limitation, as they do not take into account contradictory and potentially inconsistent information which might emerge from relevant messages. We contend that the information made available in Twitter can be useful to extract a particular version of arguments (called “opinions” in our formalization) which emerge bottom-up from the social interaction associated with such messages. In this paper we present a novel framework which allows to mine opinions from Twitter based on incrementally generated queries. As a result, we will be able to obtain an “opinion tree”, rooted in the first original query. Distinguished, conflicting elements in an opinion tree lead to so-called “conflict trees”, which resemble dialectical trees as those used traditionally in defeasible argumentation.Fil: Grosse, Kathrin. Universität Osnabrück. Institut für Kognitionswissenschaft; AlemaniaFil: González, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; ArgentinaFil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; ArgentinaIOS Press2015-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/45644Grosse, Kathrin; González, María Paula; Chesñevar, Carlos Iván; Maguitman, Ana Gabriela; Integrating argumentation and sentiment analysis for mining opinions from Twitter; IOS Press; AI Communications; 28; 3; 7-2015; 387-4010921-71261875-8452CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://content.iospress.com/articles/ai-communications/aic627info:eu-repo/semantics/altIdentifier/doi/10.3233/AIC-140627info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:21:42Zoai:ri.conicet.gov.ar:11336/45644instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:21:43.225CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Integrating argumentation and sentiment analysis for mining opinions from Twitter
title Integrating argumentation and sentiment analysis for mining opinions from Twitter
spellingShingle Integrating argumentation and sentiment analysis for mining opinions from Twitter
Grosse, Kathrin
Artificial Intelligence
Argumentation
Opinion Mining
Social Media
title_short Integrating argumentation and sentiment analysis for mining opinions from Twitter
title_full Integrating argumentation and sentiment analysis for mining opinions from Twitter
title_fullStr Integrating argumentation and sentiment analysis for mining opinions from Twitter
title_full_unstemmed Integrating argumentation and sentiment analysis for mining opinions from Twitter
title_sort Integrating argumentation and sentiment analysis for mining opinions from Twitter
dc.creator.none.fl_str_mv Grosse, Kathrin
González, María Paula
Chesñevar, Carlos Iván
Maguitman, Ana Gabriela
author Grosse, Kathrin
author_facet Grosse, Kathrin
González, María Paula
Chesñevar, Carlos Iván
Maguitman, Ana Gabriela
author_role author
author2 González, María Paula
Chesñevar, Carlos Iván
Maguitman, Ana Gabriela
author2_role author
author
author
dc.subject.none.fl_str_mv Artificial Intelligence
Argumentation
Opinion Mining
Social Media
topic Artificial Intelligence
Argumentation
Opinion Mining
Social Media
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Social networks have grown exponentially in use and impact on the society as a whole. In particular, microblogging platforms such as Twitter have become important tools to assess public opinion on different issues. Recently, some approaches for assessing Twitter messages have been developed, identifying sentiments associated with relevant keywords or hashtags. However, such approaches have an important limitation, as they do not take into account contradictory and potentially inconsistent information which might emerge from relevant messages. We contend that the information made available in Twitter can be useful to extract a particular version of arguments (called “opinions” in our formalization) which emerge bottom-up from the social interaction associated with such messages. In this paper we present a novel framework which allows to mine opinions from Twitter based on incrementally generated queries. As a result, we will be able to obtain an “opinion tree”, rooted in the first original query. Distinguished, conflicting elements in an opinion tree lead to so-called “conflict trees”, which resemble dialectical trees as those used traditionally in defeasible argumentation.
Fil: Grosse, Kathrin. Universität Osnabrück. Institut für Kognitionswissenschaft; Alemania
Fil: González, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
Fil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
Fil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
description Social networks have grown exponentially in use and impact on the society as a whole. In particular, microblogging platforms such as Twitter have become important tools to assess public opinion on different issues. Recently, some approaches for assessing Twitter messages have been developed, identifying sentiments associated with relevant keywords or hashtags. However, such approaches have an important limitation, as they do not take into account contradictory and potentially inconsistent information which might emerge from relevant messages. We contend that the information made available in Twitter can be useful to extract a particular version of arguments (called “opinions” in our formalization) which emerge bottom-up from the social interaction associated with such messages. In this paper we present a novel framework which allows to mine opinions from Twitter based on incrementally generated queries. As a result, we will be able to obtain an “opinion tree”, rooted in the first original query. Distinguished, conflicting elements in an opinion tree lead to so-called “conflict trees”, which resemble dialectical trees as those used traditionally in defeasible argumentation.
publishDate 2015
dc.date.none.fl_str_mv 2015-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/45644
Grosse, Kathrin; González, María Paula; Chesñevar, Carlos Iván; Maguitman, Ana Gabriela; Integrating argumentation and sentiment analysis for mining opinions from Twitter; IOS Press; AI Communications; 28; 3; 7-2015; 387-401
0921-7126
1875-8452
CONICET Digital
CONICET
url http://hdl.handle.net/11336/45644
identifier_str_mv Grosse, Kathrin; González, María Paula; Chesñevar, Carlos Iván; Maguitman, Ana Gabriela; Integrating argumentation and sentiment analysis for mining opinions from Twitter; IOS Press; AI Communications; 28; 3; 7-2015; 387-401
0921-7126
1875-8452
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://content.iospress.com/articles/ai-communications/aic627
info:eu-repo/semantics/altIdentifier/doi/10.3233/AIC-140627
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv IOS Press
publisher.none.fl_str_mv IOS Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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