Sentiment Detection in Economics Texts

Olumide E. Ojo, Alexander Gelbukh, Hiram Calvo, Olaronke O. Adebanji, Grigori Sidorov

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

6 Citas (Scopus)

Resumen

Deriving intelligence from text is important as it can provide valuable information on how events influence public opinion. In this work, a classification task was done in order to obtain the sentiment behind the polarity of an economic text using machine learning and deep learning methods. We analyzed the text for keywords that can be categorized into positive, negative and neutral reviews and found more insights. In the final result of classifying three groups (positive, negative and neutral), the models were unable to perform up to 80% accuracy, where only one variant has the accuracy of 80% as the best on the test dataset.

Idioma originalInglés
Título de la publicación alojadaAdvances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings
EditoresLourdes Martínez-Villaseñor, Hiram Ponce, Oscar Herrera-Alcántara, Félix A. Castro-Espinoza
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas271-281
Número de páginas11
ISBN (versión impresa)9783030608866
DOI
EstadoPublicada - 2020
Evento19th Mexican International Conference on Artificial Intelligence, MICAI 2020 - Mexico City, México
Duración: 12 oct. 202017 oct. 2020

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12469 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia19th Mexican International Conference on Artificial Intelligence, MICAI 2020
País/TerritorioMéxico
CiudadMexico City
Período12/10/2017/10/20

Huella

Profundice en los temas de investigación de 'Sentiment Detection in Economics Texts'. En conjunto forman una huella única.

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