ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter
Main Author: | |
---|---|
Publication Date: | 2017 |
Other Authors: | |
Format: | Article |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | http://hdl.handle.net/10174/21321 |
Summary: | This paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic Based Message Polarity Classification according to a two-point scale) of SemEval2017 Task 4 Sentiment Analysis in Twitter. We used several features with a sentiment lexicon and NLP techniques, Maximum Entropy as a classifier for our system. |
id |
RCAP_fdc2aaaab7f61e02b40ba49cd5b3dfff |
---|---|
oai_identifier_str |
oai:dspace.uevora.pt:10174/21321 |
network_acronym_str |
RCAP |
network_name_str |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
repository_id_str |
https://opendoar.ac.uk/repository/7160 |
spelling |
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in TwitterNLPClassificationOpinion MiningSentiment AnalysisThis paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic Based Message Polarity Classification according to a two-point scale) of SemEval2017 Task 4 Sentiment Analysis in Twitter. We used several features with a sentiment lexicon and NLP techniques, Maximum Entropy as a classifier for our system.gLINK project of ”Erasmus Mundus Programme, Action 2 - STRAND 1, Lot 5, Asia (East)”ACL2017-09-11T11:33:01Z2017-09-112017-08-03T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/21321http://hdl.handle.net/10174/21321engE. Dovdon and J. Saias (2017). “ej-sa-2017 at semeval-2017 task 4: Experiments for target oriented sentiment analysis in twitter,” in Proceedings of the 11th International Workshop o n Semantic Evaluation (SemEval-2017), (Vancouver, Canada), pp. 635–638, Association for Computational Linguisticshttp://www.aclweb.org/anthology/S/S17/S17-2106.pdfndjsaias@uevora.pt283Dovdon, EnkhzolSaias, 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:RCAAP2024-01-03T19:11:48Zoai:dspace.uevora.pt:10174/21321Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T12:13:46.649550Repositórios 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 |
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter |
title |
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter |
spellingShingle |
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter Dovdon, Enkhzol NLP Classification Opinion Mining Sentiment Analysis |
title_short |
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter |
title_full |
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter |
title_fullStr |
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter |
title_full_unstemmed |
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter |
title_sort |
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter |
author |
Dovdon, Enkhzol |
author_facet |
Dovdon, Enkhzol Saias, José |
author_role |
author |
author2 |
Saias, José |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Dovdon, Enkhzol Saias, José |
dc.subject.por.fl_str_mv |
NLP Classification Opinion Mining Sentiment Analysis |
topic |
NLP Classification Opinion Mining Sentiment Analysis |
description |
This paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic Based Message Polarity Classification according to a two-point scale) of SemEval2017 Task 4 Sentiment Analysis in Twitter. We used several features with a sentiment lexicon and NLP techniques, Maximum Entropy as a classifier for our system. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-09-11T11:33:01Z 2017-09-11 2017-08-03T00:00:00Z |
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/10174/21321 http://hdl.handle.net/10174/21321 |
url |
http://hdl.handle.net/10174/21321 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
E. Dovdon and J. Saias (2017). “ej-sa-2017 at semeval-2017 task 4: Experiments for target oriented sentiment analysis in twitter,” in Proceedings of the 11th International Workshop o n Semantic Evaluation (SemEval-2017), (Vancouver, Canada), pp. 635–638, Association for Computational Linguistics http://www.aclweb.org/anthology/S/S17/S17-2106.pdf nd 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 |
ACL |
publisher.none.fl_str_mv |
ACL |
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 |
_version_ |
1833592642588377088 |