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
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/155432

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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
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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/
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