Análise de sentimento para textos curtos
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | http://hdl.handle.net/10438/18177 |
Resumo: | A huge number of short informal messages are posted every day in social network sites, discussion forums and customer surveys. Emotions seem to be frequently important in these texts. The challenge of identifying and understanding an emotion present in this type of communication is important in distinguishing the sentiment in the text and also in identifying anomalous and inappropriate behaviors, eventually offering some kind of risk. This work proposes the implementation of a sentiment analysis solution based on machine learning. Using supervised learning techniques, it is desired to discern whether a message has a positive, neutral, or negative sentiment. The messages to be analyzed are IT service satisfaction surveys. Two models were used in the analysis, the first model where only the ”Comment”, a nonstructured text field was considered and the second model, where besides the ”Comment”field, two objective questions were considered. The results obtained indicate that the techniques of machine learning, are not behind the results produced by human-produced baselines. The accuracy obtained was up to 86.8% accuracy for a three class model: ”praise”, ”neutral”and ”complaint”. Accuracy was significantly higher, reaching up to 94.5 % in an alternative model of only two classes: ”praise”and ”non-praise”. |
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Avila, Gustavo ViannaEscolas::EMApSouza, Renato RochaCafé, Ligia Maria ArrudaCoelho, Flávio Codeço2017-04-12T19:10:52Z2017-04-12T19:10:52Z2017-03-10AVILA, Gustavo Vianna. Análise de sentimento para textos curtos. Dissertação (Mestrado em Matemática Aplicada) - Escola de Matemática Aplicada, Fundação Getúlio Vargas - FGV, Rio de Janeiro, 2017.http://hdl.handle.net/10438/18177A huge number of short informal messages are posted every day in social network sites, discussion forums and customer surveys. Emotions seem to be frequently important in these texts. The challenge of identifying and understanding an emotion present in this type of communication is important in distinguishing the sentiment in the text and also in identifying anomalous and inappropriate behaviors, eventually offering some kind of risk. This work proposes the implementation of a sentiment analysis solution based on machine learning. Using supervised learning techniques, it is desired to discern whether a message has a positive, neutral, or negative sentiment. The messages to be analyzed are IT service satisfaction surveys. Two models were used in the analysis, the first model where only the ”Comment”, a nonstructured text field was considered and the second model, where besides the ”Comment”field, two objective questions were considered. The results obtained indicate that the techniques of machine learning, are not behind the results produced by human-produced baselines. The accuracy obtained was up to 86.8% accuracy for a three class model: ”praise”, ”neutral”and ”complaint”. Accuracy was significantly higher, reaching up to 94.5 % in an alternative model of only two classes: ”praise”and ”non-praise”.Um grande número de mensagens curtas informais são postadas diariamente em redes sociais, fórums de discussão e pesquisas de satisfação. Emoções parecem ser importantes de forma frequente nesses textos. O desafio de identificar e entender a emoção presente nesse tipo de comunicação é importante para distinguir o sentimento presente no texto e também para identificar comportamentos anômalos e inapropriados, eventualmente oferecendo algum tipo de risco. Este trabalho propõe a implementação de uma solução para a análise de sentimento de textos curtos baseada em aprendizado por máquina. Utilizando técnicas de aprendizado supervisionado, é desejado discernir se uma mensagem possui sentimento positivo, neutro ou negativo. As mensagens a serem analisadas serão pesquisas de satisfação de serviços de TI. Foram utilizados nas análises dois modelos, o primeiro modelo onde apenas o campo de texto livre "Comentário" foi considerado e o segundo modelo, onde além do campo de texto livre "Comentário", foram consideradas, adicionalmente, duas perguntas objetivas da pesquisa de satisfação. Os resultados obtidos indicam que as técnicas utilizadas de aprendizado por máquina, não ficam atrás dos resultados produzidos por aprendizado humano. A acurácia obtida foi de até 86,8% de acerto para um modelo de três classes: "elogio", "neutro" e "reclamação". A acurácia foi significativamente superior, alcançando até 94,5% em um modelo alternativo, de apenas duas classes: "elogio" e "não-elogio".porMineração de dadosProcessamento da linguagem naturalAprendizado do computadorAnálise de SentimentosTecnologiaMineração de dados (Computação)Processamento da linguagem natural (Computação)Aprendizado do computadorModelagem de dadosAnálise de sentimento para textos curtosinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessTEXTFGV EMAp - Gustavo Avila - Análise de Sentimento para Textos Curtos.pdf.txtFGV EMAp - Gustavo Avila - Análise de Sentimento para Textos Curtos.pdf.txtExtracted texttext/plain103630https://repositorio.fgv.br/bitstreams/5a0d2173-d82f-40ce-a435-1bd5a863acf4/download3e58efc737e912ab8360423b50361f26MD55ORIGINALFGV EMAp - Gustavo Avila - Análise de Sentimento 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|
dc.title.por.fl_str_mv |
Análise de sentimento para textos curtos |
title |
Análise de sentimento para textos curtos |
spellingShingle |
Análise de sentimento para textos curtos Avila, Gustavo Vianna Mineração de dados Processamento da linguagem natural Aprendizado do computador Análise de Sentimentos Tecnologia Mineração de dados (Computação) Processamento da linguagem natural (Computação) Aprendizado do computador Modelagem de dados |
title_short |
Análise de sentimento para textos curtos |
title_full |
Análise de sentimento para textos curtos |
title_fullStr |
Análise de sentimento para textos curtos |
title_full_unstemmed |
Análise de sentimento para textos curtos |
title_sort |
Análise de sentimento para textos curtos |
author |
Avila, Gustavo Vianna |
author_facet |
Avila, Gustavo Vianna |
author_role |
author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EMAp |
dc.contributor.member.none.fl_str_mv |
Souza, Renato Rocha Café, Ligia Maria Arruda |
dc.contributor.author.fl_str_mv |
Avila, Gustavo Vianna |
dc.contributor.advisor1.fl_str_mv |
Coelho, Flávio Codeço |
contributor_str_mv |
Coelho, Flávio Codeço |
dc.subject.por.fl_str_mv |
Mineração de dados Processamento da linguagem natural Aprendizado do computador Análise de Sentimentos |
topic |
Mineração de dados Processamento da linguagem natural Aprendizado do computador Análise de Sentimentos Tecnologia Mineração de dados (Computação) Processamento da linguagem natural (Computação) Aprendizado do computador Modelagem de dados |
dc.subject.area.por.fl_str_mv |
Tecnologia |
dc.subject.bibliodata.por.fl_str_mv |
Mineração de dados (Computação) Processamento da linguagem natural (Computação) Aprendizado do computador Modelagem de dados |
description |
A huge number of short informal messages are posted every day in social network sites, discussion forums and customer surveys. Emotions seem to be frequently important in these texts. The challenge of identifying and understanding an emotion present in this type of communication is important in distinguishing the sentiment in the text and also in identifying anomalous and inappropriate behaviors, eventually offering some kind of risk. This work proposes the implementation of a sentiment analysis solution based on machine learning. Using supervised learning techniques, it is desired to discern whether a message has a positive, neutral, or negative sentiment. The messages to be analyzed are IT service satisfaction surveys. Two models were used in the analysis, the first model where only the ”Comment”, a nonstructured text field was considered and the second model, where besides the ”Comment”field, two objective questions were considered. The results obtained indicate that the techniques of machine learning, are not behind the results produced by human-produced baselines. The accuracy obtained was up to 86.8% accuracy for a three class model: ”praise”, ”neutral”and ”complaint”. Accuracy was significantly higher, reaching up to 94.5 % in an alternative model of only two classes: ”praise”and ”non-praise”. |
publishDate |
2017 |
dc.date.accessioned.fl_str_mv |
2017-04-12T19:10:52Z |
dc.date.available.fl_str_mv |
2017-04-12T19:10:52Z |
dc.date.issued.fl_str_mv |
2017-03-10 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
AVILA, Gustavo Vianna. Análise de sentimento para textos curtos. Dissertação (Mestrado em Matemática Aplicada) - Escola de Matemática Aplicada, Fundação Getúlio Vargas - FGV, Rio de Janeiro, 2017. |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10438/18177 |
identifier_str_mv |
AVILA, Gustavo Vianna. Análise de sentimento para textos curtos. Dissertação (Mestrado em Matemática Aplicada) - Escola de Matemática Aplicada, Fundação Getúlio Vargas - FGV, Rio de Janeiro, 2017. |
url |
http://hdl.handle.net/10438/18177 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional do FGV (FGV Repositório Digital) instname:Fundação Getulio Vargas (FGV) instacron:FGV |
instname_str |
Fundação Getulio Vargas (FGV) |
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FGV |
institution |
FGV |
reponame_str |
Repositório Institucional do FGV (FGV Repositório Digital) |
collection |
Repositório Institucional do FGV (FGV Repositório Digital) |
bitstream.url.fl_str_mv |
https://repositorio.fgv.br/bitstreams/5a0d2173-d82f-40ce-a435-1bd5a863acf4/download https://repositorio.fgv.br/bitstreams/2bc9d1cd-98ff-4801-9b8a-a3d5929869e0/download https://repositorio.fgv.br/bitstreams/25e099ac-8816-447a-a8ef-52d9dfd1362f/download https://repositorio.fgv.br/bitstreams/8b3e87ef-2000-4356-bd2a-e7b90a904bfc/download |
bitstream.checksum.fl_str_mv |
3e58efc737e912ab8360423b50361f26 245f39102b78290b281cc9f68239d26d dfb340242cced38a6cca06c627998fa1 d3be5526ce99850937eec4b998bd90a8 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV) |
repository.mail.fl_str_mv |
|
_version_ |
1802749883967340544 |