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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/18642
Ver los metadatos del registro completo
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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 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 social web text mining opinion mining sentiment classification sentiment analysis |
topic |
Ciencias Informáticas microblogging Information Search and Retrieval Web-based services NLTK Python 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 |
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2011-10 |
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eng |
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eng |
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