Sentiment analysis and topic classification based on binary maximum entropy classifiers

Detalhes bibliográficos
Autor(a) principal: Batista, F.
Data de Publicação: 2013
Outros Autores: Ribeiro, R.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://ciencia.iscte-iul.pt/public/pub/id/13632
http://hdl.handle.net/10071/10036
Resumo: This paper presents a strategy based on binary maximum entropy classifiers for automatic sentiment analysis and topic classification over Spanish Twitter data. The developed system achieved the best results for topic classification, and the second place for sentiment analysis in a joint evaluation effort — the TASS challenge. Different configurations have been explored for both tasks, leading to the use of a cascade of binary classifiers for sentiment analysis and a one-vs-all strategy for topic classification, where the most probable topics for each tweet were selected.
id RCAP_3955478f8c491b3aa38e7ce5d7be1955
oai_identifier_str oai:repositorio.iscte-iul.pt:10071/10036
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Sentiment analysis and topic classification based on binary maximum entropy classifiersSentiment analysisTopic detectionSocial mediaLogistic regressionMaximum entropyThis paper presents a strategy based on binary maximum entropy classifiers for automatic sentiment analysis and topic classification over Spanish Twitter data. The developed system achieved the best results for topic classification, and the second place for sentiment analysis in a joint evaluation effort — the TASS challenge. Different configurations have been explored for both tasks, leading to the use of a cascade of binary classifiers for sentiment analysis and a one-vs-all strategy for topic classification, where the most probable topics for each tweet were selected.Sociedad Española para el Procesamiento del Lenguaje Natural2015-10-28T13:21:51Z2013-01-01T00:00:00Z20132015-10-28T13:18:03Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://ciencia.iscte-iul.pt/public/pub/id/13632http://hdl.handle.net/10071/10036eng1135-5948Batista, F.Ribeiro, R.info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-07-07T03:05:03Zoai:repositorio.iscte-iul.pt:10071/10036Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-07-07T03:05:03Repositório 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 Sentiment analysis and topic classification based on binary maximum entropy classifiers
title Sentiment analysis and topic classification based on binary maximum entropy classifiers
spellingShingle Sentiment analysis and topic classification based on binary maximum entropy classifiers
Batista, F.
Sentiment analysis
Topic detection
Social media
Logistic regression
Maximum entropy
title_short Sentiment analysis and topic classification based on binary maximum entropy classifiers
title_full Sentiment analysis and topic classification based on binary maximum entropy classifiers
title_fullStr Sentiment analysis and topic classification based on binary maximum entropy classifiers
title_full_unstemmed Sentiment analysis and topic classification based on binary maximum entropy classifiers
title_sort Sentiment analysis and topic classification based on binary maximum entropy classifiers
author Batista, F.
author_facet Batista, F.
Ribeiro, R.
author_role author
author2 Ribeiro, R.
author2_role author
dc.contributor.author.fl_str_mv Batista, F.
Ribeiro, R.
dc.subject.por.fl_str_mv Sentiment analysis
Topic detection
Social media
Logistic regression
Maximum entropy
topic Sentiment analysis
Topic detection
Social media
Logistic regression
Maximum entropy
description This paper presents a strategy based on binary maximum entropy classifiers for automatic sentiment analysis and topic classification over Spanish Twitter data. The developed system achieved the best results for topic classification, and the second place for sentiment analysis in a joint evaluation effort — the TASS challenge. Different configurations have been explored for both tasks, leading to the use of a cascade of binary classifiers for sentiment analysis and a one-vs-all strategy for topic classification, where the most probable topics for each tweet were selected.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01T00:00:00Z
2013
2015-10-28T13:21:51Z
2015-10-28T13:18:03Z
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://ciencia.iscte-iul.pt/public/pub/id/13632
http://hdl.handle.net/10071/10036
url https://ciencia.iscte-iul.pt/public/pub/id/13632
http://hdl.handle.net/10071/10036
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1135-5948
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Sociedad Española para el Procesamiento del Lenguaje Natural
publisher.none.fl_str_mv Sociedad Española para el Procesamiento del Lenguaje Natural
dc.source.none.fl_str_mv 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 mluisa.alvim@gmail.com
_version_ 1817546393704726528

Registros relacionados