Dictionary-based sentiment analysis applied to a specific domain
The web and social media have been growing exponentially in recent years. We now have access to documents bearing opinions expressed on a broad range of topics. This constitutes a rich resource for natural language processing tasks, particularly for sentiment analysis. Nevertheless, sentiment analysis is usually difficult because expressed sentiments are usually topic-oriented. In this paper, we propose to automatically construct a sentiment dictionary using relevant terms obtained from web pages for a specific domain. This dictionary is initially built by querying the web with a combination of opinion terms, as well as terms of the domain. In order to select only relevant terms we apply two measures AcroDefMI3 and TrueSkill. Experiments conducted on different domains highlight that our automatic approach performs better for specific cases.
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dig-cirad-fr-5838382022-03-30T11:53:33Z http://agritrop.cirad.fr/583838/ http://agritrop.cirad.fr/583838/ Dictionary-based sentiment analysis applied to a specific domain. Cruz Laura, Ochoa José, Roche Mathieu, Poncelet Pascal. 2017. In : Information management and big data. Lossio-Ventura Juan Antonio (ed.), Alatrista-Salas Hugo (ed.). Cham : Springer International Publishing, 57-68. (Communications in Computer and Information Science, 656) ISBN 978-3-319-55208-8 Annual International Symposium on Information Management and Big Data. 2, Cusco, Pérou, 2 Septembre 2015/4 Septembre 2015.https://doi.org/10.1007/978-3-319-55209-5_5 <https://doi.org/10.1007/978-3-319-55209-5_5> Researchers Dictionary-based sentiment analysis applied to a specific domain Cruz, Laura Ochoa, José Roche, Mathieu Poncelet, Pascal eng 2017 Springer International Publishing Information management and big data C30 - Documentation et information U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques 000 - Autres thèmes The web and social media have been growing exponentially in recent years. We now have access to documents bearing opinions expressed on a broad range of topics. This constitutes a rich resource for natural language processing tasks, particularly for sentiment analysis. Nevertheless, sentiment analysis is usually difficult because expressed sentiments are usually topic-oriented. In this paper, we propose to automatically construct a sentiment dictionary using relevant terms obtained from web pages for a specific domain. This dictionary is initially built by querying the web with a combination of opinion terms, as well as terms of the domain. In order to select only relevant terms we apply two measures AcroDefMI3 and TrueSkill. Experiments conducted on different domains highlight that our automatic approach performs better for specific cases. conference_item info:eu-repo/semantics/conferenceObject Conference info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/583838/7/ID583838.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1007/978-3-319-55209-5_5 10.1007/978-3-319-55209-5_5 http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=219270 info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-55209-5_5 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/978-3-319-55209-5_5 |
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description |
The web and social media have been growing exponentially in recent years. We now have access to documents bearing opinions expressed on a broad range of topics. This constitutes a rich resource for natural language processing tasks, particularly for sentiment analysis. Nevertheless, sentiment analysis is usually difficult because expressed sentiments are usually topic-oriented. In this paper, we propose to automatically construct a sentiment dictionary using relevant terms obtained from web pages for a specific domain. This dictionary is initially built by querying the web with a combination of opinion terms, as well as terms of the domain. In order to select only relevant terms we apply two measures AcroDefMI3 and TrueSkill. Experiments conducted on different domains highlight that our automatic approach performs better for specific cases. |
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conference_item |
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C30 - Documentation et information U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques 000 - Autres thèmes |
author |
Cruz, Laura Ochoa, José Roche, Mathieu Poncelet, Pascal |
author_facet |
Cruz, Laura Ochoa, José Roche, Mathieu Poncelet, Pascal |
author_sort |
Cruz, Laura |
title |
Dictionary-based sentiment analysis applied to a specific domain |
title_short |
Dictionary-based sentiment analysis applied to a specific domain |
title_full |
Dictionary-based sentiment analysis applied to a specific domain |
title_fullStr |
Dictionary-based sentiment analysis applied to a specific domain |
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Dictionary-based sentiment analysis applied to a specific domain |
title_sort |
dictionary-based sentiment analysis applied to a specific domain |
publisher |
Springer International Publishing |
url |
http://agritrop.cirad.fr/583838/ http://agritrop.cirad.fr/583838/7/ID583838.pdf |
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AT cruzlaura dictionarybasedsentimentanalysisappliedtoaspecificdomain AT ochoajose dictionarybasedsentimentanalysisappliedtoaspecificdomain AT rochemathieu dictionarybasedsentimentanalysisappliedtoaspecificdomain AT ponceletpascal dictionarybasedsentimentanalysisappliedtoaspecificdomain |
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1758025312014172160 |