Comparing and evaluating tools for sentiment analysis
- Autores
- Borrelli, Franco Martín; Challiol, Cecilia
- Año de publicación
- 2023
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Sentiment analysis is a process of identifying and extracting personal information from textual data. It has become essential for businesses and organizations to understand customers' opinions, emotions, and attitudes toward their products, services, or brands. While creating a custom sentiment analysis model can provide tailored results for specific datasets, it can also be time-consuming, resource-intensive, and require a high level of expertise in machine learning. Some tools offer a faster and more accessible alternative to users without a background in machine learning to create a custom model. However, researchers and practitioners usually do not know how to choose the best tool for each domain. This paper compares and evaluates some sentiment analysis tools' differences, considering how they were built and how suitable they are for analyzing sentiments on some specific topics. In particular, this paper focuses on four popular sentiment analysis tools for Python: TextBlob, Vader, Flair, and HuggingFace Transformers.
Facultad de Informática - Materia
-
Ciencias Informáticas
Sentiment Analysis
TextBlob
Vader
Flair
HuggingFace Transformers
Ruled-based approach
Machine Learning - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/155432
Ver los metadatos del registro completo
id |
SEDICI_d71b8132019547a4cad0c003115b8773 |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/155432 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
network_name_str |
SEDICI (UNLP) |
spelling |
Comparing and evaluating tools for sentiment analysisBorrelli, Franco MartínChalliol, CeciliaCiencias InformáticasSentiment AnalysisTextBlobVaderFlairHuggingFace TransformersRuled-based approachMachine LearningSentiment analysis is a process of identifying and extracting personal information from textual data. It has become essential for businesses and organizations to understand customers' opinions, emotions, and attitudes toward their products, services, or brands. While creating a custom sentiment analysis model can provide tailored results for specific datasets, it can also be time-consuming, resource-intensive, and require a high level of expertise in machine learning. Some tools offer a faster and more accessible alternative to users without a background in machine learning to create a custom model. However, researchers and practitioners usually do not know how to choose the best tool for each domain. This paper compares and evaluates some sentiment analysis tools' differences, considering how they were built and how suitable they are for analyzing sentiments on some specific topics. In particular, this paper focuses on four popular sentiment analysis tools for Python: TextBlob, Vader, Flair, and HuggingFace Transformers.Facultad de Informática2023-06info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf36-40http://sedici.unlp.edu.ar/handle/10915/155432enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2271-7info:eu-repo/semantics/reference/hdl/10915/155281info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:40:21Zoai:sedici.unlp.edu.ar:10915/155432Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:40:21.536SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Comparing and evaluating tools for sentiment analysis |
title |
Comparing and evaluating tools for sentiment analysis |
spellingShingle |
Comparing and evaluating tools for sentiment analysis Borrelli, Franco Martín Ciencias Informáticas Sentiment Analysis TextBlob Vader Flair HuggingFace Transformers Ruled-based approach Machine Learning |
title_short |
Comparing and evaluating tools for sentiment analysis |
title_full |
Comparing and evaluating tools for sentiment analysis |
title_fullStr |
Comparing and evaluating tools for sentiment analysis |
title_full_unstemmed |
Comparing and evaluating tools for sentiment analysis |
title_sort |
Comparing and evaluating tools for sentiment analysis |
dc.creator.none.fl_str_mv |
Borrelli, Franco Martín Challiol, Cecilia |
author |
Borrelli, Franco Martín |
author_facet |
Borrelli, Franco Martín Challiol, Cecilia |
author_role |
author |
author2 |
Challiol, Cecilia |
author2_role |
author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Sentiment Analysis TextBlob Vader Flair HuggingFace Transformers Ruled-based approach Machine Learning |
topic |
Ciencias Informáticas Sentiment Analysis TextBlob Vader Flair HuggingFace Transformers Ruled-based approach Machine Learning |
dc.description.none.fl_txt_mv |
Sentiment analysis is a process of identifying and extracting personal information from textual data. It has become essential for businesses and organizations to understand customers' opinions, emotions, and attitudes toward their products, services, or brands. While creating a custom sentiment analysis model can provide tailored results for specific datasets, it can also be time-consuming, resource-intensive, and require a high level of expertise in machine learning. Some tools offer a faster and more accessible alternative to users without a background in machine learning to create a custom model. However, researchers and practitioners usually do not know how to choose the best tool for each domain. This paper compares and evaluates some sentiment analysis tools' differences, considering how they were built and how suitable they are for analyzing sentiments on some specific topics. In particular, this paper focuses on four popular sentiment analysis tools for Python: TextBlob, Vader, Flair, and HuggingFace Transformers. Facultad de Informática |
description |
Sentiment analysis is a process of identifying and extracting personal information from textual data. It has become essential for businesses and organizations to understand customers' opinions, emotions, and attitudes toward their products, services, or brands. While creating a custom sentiment analysis model can provide tailored results for specific datasets, it can also be time-consuming, resource-intensive, and require a high level of expertise in machine learning. Some tools offer a faster and more accessible alternative to users without a background in machine learning to create a custom model. However, researchers and practitioners usually do not know how to choose the best tool for each domain. This paper compares and evaluates some sentiment analysis tools' differences, considering how they were built and how suitable they are for analyzing sentiments on some specific topics. In particular, this paper focuses on four popular sentiment analysis tools for Python: TextBlob, Vader, Flair, and HuggingFace Transformers. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-06 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/155432 |
url |
http://sedici.unlp.edu.ar/handle/10915/155432 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/isbn/978-950-34-2271-7 info:eu-repo/semantics/reference/hdl/10915/155281 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
dc.format.none.fl_str_mv |
application/pdf 36-40 |
dc.source.none.fl_str_mv |
reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
reponame_str |
SEDICI (UNLP) |
collection |
SEDICI (UNLP) |
instname_str |
Universidad Nacional de La Plata |
instacron_str |
UNLP |
institution |
UNLP |
repository.name.fl_str_mv |
SEDICI (UNLP) - Universidad Nacional de La Plata |
repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
_version_ |
1844616276535672832 |
score |
13.070432 |