Título

EvoMSA: A Multilingual Evolutionary Approach for Sentiment Analysis

Autor

MARIO GRAFF GUERRERO

SABINO MIRANDA JIMENEZ

Eric Sadit Téllez Avila

Daniela Moctezuma

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by-nc/4.0

Identificador alterno

arxiv: https://arxiv.org/abs/1812.02307v3

Referencia de datos

datasetURL/http://arxiv.org/abs/1812.02307

Materias

Lenguaje de programación - (UNESCO) Máquina de aprendizaje - (UNESCO) INGENIERÍA Y TECNOLOGÍA - (CTI) CIENCIAS TECNOLÓGICAS - (CTI) TECNOLOGÍA DE LOS ORDENADORES - (CTI) LENGUAJES DE PROGRAMACIÓN - (CTI) LENGUAJES DE PROGRAMACIÓN - (CTI)

Resumen o descripción

Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several research competitions in a number of languages, e.g., English, Spanish, and Arabic, among others. In this contribution, we propose an SA system, namely EvoMSA, that our participating systems in various SA competitions, making it domain independent and multilingual by processing text using only language-independent techniques.

EvoMSA is based on Genetic Programming that works by combining the output of text classifers to produce the final prediction. We analyzed EvoMSA on diferent SA competitions to provide a global overview of its performance. The results indicated that EvoMSA is competitive obtaining top rankings in several SA competitions. Furthermore, we performed an analysis of EvoMSA's components to measure their contribution to the performance; the aim was to facilitate a practitioner or newcomer to implement a competitive SA classifer. Finally, it is worth to mention that EvoMSA is available as open source software.

Editor

Cornell University

Fecha de publicación

2019

Tipo de publicación

Artículo

Versión de la publicación

Versión aceptada

Recurso de información

http://infotec.repositorioinstitucional.mx/jspui/handle/1027/344

Formato

application/pdf

Fuente

Computation and Language

Idioma

Inglés

Relación

&

Moctezuma, D. (2018). EvoMSA: A Multilingual Evolutionary Approach for Sentiment Analysis. arXiv:1812.02307 [cs, stat]. Recuperado de http://arxiv.org/abs/1812.02307

Audiencia

Público en general

Sugerencia de citación

Graff, M., Miranda-Jiménez, S., Tellez, E. S.,

Repositorio Orígen

Repositorio Institucional de INFOTEC

Descargas

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