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

• Autores: Villegas, María Paula; Garciarena Ucelay, María José; Fernández, Juan Pablo; Álvarez Carmona, Miguel A.; Errecalde, Marcelo Luis; Cagnina, Leticia
• Año de publicación: 2016
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; text mining; word-based representations; text categorization; movie reviews; sentiment analysis
• Institución: Universidad Nacional de La Plata

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.
XIII Workshop Bases de datos y Minería de Datos (WBDMD).
Red de Universidades con Carreras en Informática (RedUNCI)