Comparative analysis between different automatic learning environments for sentiment analysis

Sentiment Analysis is a branch of Natural Language Processing in which an emotion is identified through a sentence, phrase or written expression on the Internet, allowing the monitoring of opinions on different topics discussed on the Web. The study discussed in this paper analyzed phrases or sentences written in Spanish and English expressing opinions about the service of Restaurants and opinions written in the English language about Laptops. Experiments were carried out using 3 automatic classifiers: Support Vector Machine (SVM), Naïve Bayes and Multinomial Naïve Bayes, each one being tested with the three data sets in the Weka automatic learning software and in Python, in order to make a comparison of results between these two tools

• Autores: amelec, viloria; Varela Izquierdo, Noel; Vargas, Jesús; Pineda, Omar
• Tipo de recurso: http://purl.org/coar/resource_type/c_816b
• Fecha de publicación: 2020
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/7279
• Acceso en línea: https://hdl.handle.net/11323/7279; https://repositorio.cuc.edu.co/
• Palabra clave: Automatic learning; Comparative analysis; Sentiment analysis
• Rights: closedAccess
• License: Attribution-NonCommercial-NoDerivatives 4.0 International