Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9

Bibliographic Details
Main Author: Saias, José
Publication Date: 2014
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10174/13868
Summary: This document describes the senti.ue system and how it was used for partici- pation in SemEval-2014 Task 9 challenge. Our system is an evolution of our prior work, also used in last year’s edition of Sentiment Analysis in Twitter. This sys- tem maintains a supervised machine learn- ing approach to classify the tweet overall sentiment, but with a change in the used features and the algorithm. We use a re- stricted set of 47 features in subtask B and 31 features in subtask A. In the constrained mode, and for the five data sources, senti.ue achieved a score between 78,72 and 84,05 in subtask A, and a score between 55,31 and 71,39 in sub- task B. For the unconstrained mode, our score was slightly below, except for one case in subtask A.
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spelling Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9NLPArtificial IntelligenceMachine LeaningSentiment AnalysisThis document describes the senti.ue system and how it was used for partici- pation in SemEval-2014 Task 9 challenge. Our system is an evolution of our prior work, also used in last year’s edition of Sentiment Analysis in Twitter. This sys- tem maintains a supervised machine learn- ing approach to classify the tweet overall sentiment, but with a change in the used features and the algorithm. We use a re- stricted set of 47 features in subtask B and 31 features in subtask A. In the constrained mode, and for the five data sources, senti.ue achieved a score between 78,72 and 84,05 in subtask A, and a score between 55,31 and 71,39 in sub- task B. For the unconstrained mode, our score was slightly below, except for one case in subtask A.Association for Computational Linguistics2015-03-31T10:58:47Z2015-03-312014-08-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/13868http://hdl.handle.net/10174/13868engJ. Saias, “Senti.ue: Tweet overall sentiment classification approach for semeval-2014 task 9,” in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), (Dublin, Ireland), pp. 546–550, Association for Computational Linguistics and Dublin City University, August 2014. ISBN 978-1-941643-24-2.http://www.aclweb.org/anthology/S/S14/S14-2095.pdfjsaias@uevora.pt283Saias, Joséinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-01-03T18:59:46Zoai:dspace.uevora.pt:10174/13868Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T12:05:46.029168Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9
title Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9
spellingShingle Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9
Saias, José
NLP
Artificial Intelligence
Machine Leaning
Sentiment Analysis
title_short Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9
title_full Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9
title_fullStr Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9
title_full_unstemmed Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9
title_sort Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9
author Saias, José
author_facet Saias, José
author_role author
dc.contributor.author.fl_str_mv Saias, José
dc.subject.por.fl_str_mv NLP
Artificial Intelligence
Machine Leaning
Sentiment Analysis
topic NLP
Artificial Intelligence
Machine Leaning
Sentiment Analysis
description This document describes the senti.ue system and how it was used for partici- pation in SemEval-2014 Task 9 challenge. Our system is an evolution of our prior work, also used in last year’s edition of Sentiment Analysis in Twitter. This sys- tem maintains a supervised machine learn- ing approach to classify the tweet overall sentiment, but with a change in the used features and the algorithm. We use a re- stricted set of 47 features in subtask B and 31 features in subtask A. In the constrained mode, and for the five data sources, senti.ue achieved a score between 78,72 and 84,05 in subtask A, and a score between 55,31 and 71,39 in sub- task B. For the unconstrained mode, our score was slightly below, except for one case in subtask A.
publishDate 2014
dc.date.none.fl_str_mv 2014-08-01T00:00:00Z
2015-03-31T10:58:47Z
2015-03-31
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 http://hdl.handle.net/10174/13868
http://hdl.handle.net/10174/13868
url http://hdl.handle.net/10174/13868
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv J. Saias, “Senti.ue: Tweet overall sentiment classification approach for semeval-2014 task 9,” in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), (Dublin, Ireland), pp. 546–550, Association for Computational Linguistics and Dublin City University, August 2014. ISBN 978-1-941643-24-2.
http://www.aclweb.org/anthology/S/S14/S14-2095.pdf
jsaias@uevora.pt
283
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Association for Computational Linguistics
publisher.none.fl_str_mv Association for Computational Linguistics
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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