Sentiment analysis in microblogging: a practical implementation

Autores
Cohen, Mauro; Damiani, Pablo; Durandeu, Sebastián; Navas, Renzo; Merlino, Hernán; Fernández, Enrique
Año de publicación
2011
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This paper presents a system that can take short messages relevant to a particular topic from a microblogging service such as Twitter or Facebook, analyze the messages for the sentiments they carry on, and classify them. In particular, the system addresses this problem by retrieving raw data from Twitter - one of the most popular microblogging platforms - pre-processing on that raw data, and finally analyzing it using machine learning techniques to classify them by sentiment as either positive or negative
Presentado en el XII Workshop Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
microblogging
Information Search and Retrieval
Web-based services
NLTK
Python
Twitter
social web
text mining
opinion mining
sentiment classification
sentiment analysis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/18642

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spelling Sentiment analysis in microblogging: a practical implementationCohen, MauroDamiani, PabloDurandeu, SebastiánNavas, RenzoMerlino, HernánFernández, EnriqueCiencias InformáticasmicrobloggingInformation Search and RetrievalWeb-based servicesNLTKPythonTwittersocial webtext miningopinion miningsentiment classificationsentiment analysisThis paper presents a system that can take short messages relevant to a particular topic from a microblogging service such as Twitter or Facebook, analyze the messages for the sentiments they carry on, and classify them. In particular, the system addresses this problem by retrieving raw data from Twitter - one of the most popular microblogging platforms - pre-processing on that raw data, and finally analyzing it using machine learning techniques to classify them by sentiment as either positive or negativePresentado en el XII Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2011-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf191-200http://sedici.unlp.edu.ar/handle/10915/18642enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:53:36Zoai:sedici.unlp.edu.ar:10915/18642Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:53:36.562SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Sentiment analysis in microblogging: a practical implementation
title Sentiment analysis in microblogging: a practical implementation
spellingShingle Sentiment analysis in microblogging: a practical implementation
Cohen, Mauro
Ciencias Informáticas
microblogging
Information Search and Retrieval
Web-based services
NLTK
Python
Twitter
social web
text mining
opinion mining
sentiment classification
sentiment analysis
title_short Sentiment analysis in microblogging: a practical implementation
title_full Sentiment analysis in microblogging: a practical implementation
title_fullStr Sentiment analysis in microblogging: a practical implementation
title_full_unstemmed Sentiment analysis in microblogging: a practical implementation
title_sort Sentiment analysis in microblogging: a practical implementation
dc.creator.none.fl_str_mv Cohen, Mauro
Damiani, Pablo
Durandeu, Sebastián
Navas, Renzo
Merlino, Hernán
Fernández, Enrique
author Cohen, Mauro
author_facet Cohen, Mauro
Damiani, Pablo
Durandeu, Sebastián
Navas, Renzo
Merlino, Hernán
Fernández, Enrique
author_role author
author2 Damiani, Pablo
Durandeu, Sebastián
Navas, Renzo
Merlino, Hernán
Fernández, Enrique
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
microblogging
Information Search and Retrieval
Web-based services
NLTK
Python
Twitter
social web
text mining
opinion mining
sentiment classification
sentiment analysis
topic Ciencias Informáticas
microblogging
Information Search and Retrieval
Web-based services
NLTK
Python
Twitter
social web
text mining
opinion mining
sentiment classification
sentiment analysis
dc.description.none.fl_txt_mv This paper presents a system that can take short messages relevant to a particular topic from a microblogging service such as Twitter or Facebook, analyze the messages for the sentiments they carry on, and classify them. In particular, the system addresses this problem by retrieving raw data from Twitter - one of the most popular microblogging platforms - pre-processing on that raw data, and finally analyzing it using machine learning techniques to classify them by sentiment as either positive or negative
Presentado en el XII Workshop Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description This paper presents a system that can take short messages relevant to a particular topic from a microblogging service such as Twitter or Facebook, analyze the messages for the sentiments they carry on, and classify them. In particular, the system addresses this problem by retrieving raw data from Twitter - one of the most popular microblogging platforms - pre-processing on that raw data, and finally analyzing it using machine learning techniques to classify them by sentiment as either positive or negative
publishDate 2011
dc.date.none.fl_str_mv 2011-10
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info:eu-repo/semantics/publishedVersion
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dc.language.none.fl_str_mv eng
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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