Título
A simple approach to multilingual polarity classification in twitter
Autor
Eric Tellez
SABINO MIRANDA JIMENEZ
Mario Graff
Daniela Moctezuma
Ranyart Rodrigo Suarez Ponce de Leon
Oscar Sánchez Siordia
Nivel de Acceso
En Embargo
Identificador alterno
doi: https://doi.org/10.1016/j.patrec.2017.05.024
Materias
Resumen o descripción
Recently, sentiment analysis has received a lot of attention due to the interest in mining opinions of social media users. Sentiment analysis consists in determining the polarity of a given text, i.e., its degree of positiveness or negativeness. Traditionally, Sentiment Analysis algorithms have been tailored to a specific language given the complexity of having a number of lexical variations and errors introduced by the people generating content. In this contribution, our aim is to provide a simple to implement and easy to use multilingual framework, that can serve as a baseline for sentiment analysis contests, and as a starting point to build new sentiment analysis systems. We compare our approach in eight different languages, three of them correspond to important international contests, namely, SemEval (English), TASS (Spanish), and SENTIPOLC (Italian). Within the competitions, our approach reaches from medium to high positions in the rankings; whereas in the remaining languages our approach outperforms the reported results.
Editor
Elsevier
Fecha de publicación
15 de julio de 2017
Tipo de publicación
Artículo
Versión de la publicación
Versión aceptada
Recurso de información
Formato
application/pdf
Fuente
Pattern Recognition Letters Volume 94, 15 July 2017, Pages 68-74
Idioma
Inglés
Audiencia
Estudiantes
Investigadores
Maestros
Repositorio Orígen
Repositorio Institucional de CENTROGEO
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