Vector-based word representations for sentiment analysis: a comparative study

New applications of text categorization methods like opinion mining and sentiment analysis, author profiling and plagiarism detection requires more elaborated and effective document representation models than classical Information Retrieval approaches like the Bag of Words representation. In this co...

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Detalles Bibliográficos
Autores principales:	Villegas, María Paula, Garciarena Ucelay, María José, Fernández, Juan Pablo, Álvarez Carmona, Miguel A., Errecalde, Marcelo Luis, Cagnina, Leticia
Formato:	Objeto de conferencia
Lenguaje:	Inglés
Publicado:	2016
Materias:	Ciencias Informáticas text mining word-based representations text categorization movie reviews sentiment analysis
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/56763
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-56763
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas text mining word-based representations text categorization movie reviews sentiment analysis
spellingShingle	Ciencias Informáticas text mining word-based representations text categorization movie reviews sentiment analysis Villegas, María Paula Garciarena Ucelay, María José Fernández, Juan Pablo Álvarez Carmona, Miguel A. Errecalde, Marcelo Luis Cagnina, Leticia Vector-based word representations for sentiment analysis: a comparative study
topic_facet	Ciencias Informáticas text mining word-based representations text categorization movie reviews sentiment analysis
description	New applications of text categorization methods like opinion mining and sentiment analysis, author profiling and plagiarism detection requires more elaborated and effective document representation models than classical Information Retrieval approaches like the Bag of Words representation. In this context, word representation models in general and vector-based word representations in particular have gained increasing interest to overcome or alleviate some of the limitations that Bag of Words-based representations exhibit. In this article, we analyze the use of several vector-based word representations in a sentiment analysis task with movie reviews. Experimental results show the effectiveness of some vector-based word representations in comparison to standard Bag of Words representations. In particular, the Second Order Attributes representation seems to be very robust and effective because independently the classifier used with, the results are good.
format	Objeto de conferencia Objeto de conferencia
author	Villegas, María Paula Garciarena Ucelay, María José Fernández, Juan Pablo Álvarez Carmona, Miguel A. Errecalde, Marcelo Luis Cagnina, Leticia
author_facet	Villegas, María Paula Garciarena Ucelay, María José Fernández, Juan Pablo Álvarez Carmona, Miguel A. Errecalde, Marcelo Luis Cagnina, Leticia
author_sort	Villegas, María Paula
title	Vector-based word representations for sentiment analysis: a comparative study
title_short	Vector-based word representations for sentiment analysis: a comparative study
title_full	Vector-based word representations for sentiment analysis: a comparative study
title_fullStr	Vector-based word representations for sentiment analysis: a comparative study
title_full_unstemmed	Vector-based word representations for sentiment analysis: a comparative study
title_sort	vector-based word representations for sentiment analysis: a comparative study
publishDate	2016
url	http://sedici.unlp.edu.ar/handle/10915/56763
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Vector-based word representations for sentiment analysis: a comparative study

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