Sentiment Analysis Using Machine Learning

In recent years, sentiment analysis on social media, including Facebook, Twitter and blogs, has grown in popularity. Social media generate large amounts of information, and this has contributed to the growth of sentiment analysis as a field of research. This study demonstrates that sentiment analysi...

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Detalles Bibliográficos
Autores: Singh, Neha, Jaiswal, Umesh Chandra
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/160195
Acceso en línea:http://hdl.handle.net/10366/160195
Access Level:acceso abierto
Palabra clave:sentiment analysis
machine learning
social media
logistic regression
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spelling Sentiment Analysis Using Machine LearningSingh, NehaJaiswal, Umesh Chandrasentiment analysismachine learningsocial medialogistic regressionIn recent years, sentiment analysis on social media, including Facebook, Twitter and blogs, has grown in popularity. Social media generate large amounts of information, and this has contributed to the growth of sentiment analysis as a field of research. This study demonstrates that sentiment analysis has been thoroughly researched in previous years, and numerous methods have been designed and evaluated. Nevertheless, there is still much room for improvement. This paper reviews the state of art in sentiment analysis. Various machine learning procedures for sentiment analysis are discussed, their potential to increase the level of the analysis accuracy is underscored. This paper introduces sentiment analysis types, methodologies, applications, challenges, and a comparative study of machine learning and sentiment analysis approaches. Performance evaluation parameters, for sentiment analysis, have also been tested and compared using different machine learning classifiers. Performance evaluation points to logistic regression as the model that achieves the best result. In the future, a method that is easy, versatile, and practicable, should be offered as opposed to existing machine learning methods, and more work should be put into improving the algorithms' performance.Ediciones Universidad de Salamanca (España)202420242023info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10366/160195reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1601952026-06-07T06:28:51Z
dc.title.none.fl_str_mv Sentiment Analysis Using Machine Learning
title Sentiment Analysis Using Machine Learning
spellingShingle Sentiment Analysis Using Machine Learning
Singh, Neha
sentiment analysis
machine learning
social media
logistic regression
title_short Sentiment Analysis Using Machine Learning
title_full Sentiment Analysis Using Machine Learning
title_fullStr Sentiment Analysis Using Machine Learning
title_full_unstemmed Sentiment Analysis Using Machine Learning
title_sort Sentiment Analysis Using Machine Learning
dc.creator.none.fl_str_mv Singh, Neha
Jaiswal, Umesh Chandra
author Singh, Neha
author_facet Singh, Neha
Jaiswal, Umesh Chandra
author_role author
author2 Jaiswal, Umesh Chandra
author2_role author
dc.subject.none.fl_str_mv sentiment analysis
machine learning
social media
logistic regression
topic sentiment analysis
machine learning
social media
logistic regression
description In recent years, sentiment analysis on social media, including Facebook, Twitter and blogs, has grown in popularity. Social media generate large amounts of information, and this has contributed to the growth of sentiment analysis as a field of research. This study demonstrates that sentiment analysis has been thoroughly researched in previous years, and numerous methods have been designed and evaluated. Nevertheless, there is still much room for improvement. This paper reviews the state of art in sentiment analysis. Various machine learning procedures for sentiment analysis are discussed, their potential to increase the level of the analysis accuracy is underscored. This paper introduces sentiment analysis types, methodologies, applications, challenges, and a comparative study of machine learning and sentiment analysis approaches. Performance evaluation parameters, for sentiment analysis, have also been tested and compared using different machine learning classifiers. Performance evaluation points to logistic regression as the model that achieves the best result. In the future, a method that is easy, versatile, and practicable, should be offered as opposed to existing machine learning methods, and more work should be put into improving the algorithms' performance.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/160195
url http://hdl.handle.net/10366/160195
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Ediciones Universidad de Salamanca (España)
publisher.none.fl_str_mv Ediciones Universidad de Salamanca (España)
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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