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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/45644
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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 |
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eng |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
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application/pdf application/pdf application/pdf application/pdf |
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IOS Press |
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IOS Press |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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