Análisis de sentimientos en Twitter

Un estudio comparativo

Autores/as

DOI:

https://doi.org/10.51252/rcsi.v3i1.418

Palabras clave:

análisis de sentimiento, aprendizaje, clasificación, twitter

Resumen

El análisis de sentimientos ayuda a determinar la percepción de usuarios en diferentes aspectos de la vida cotidiana, como preferencias de productos en el mercado, nivel de confianza de los usuarios en ambientes de trabajo, o preferencias políticas. La idea es predecir tendencias o preferencias basados en sentimientos. En este artículo evaluamos las técnicas más comunes usadas para este tipo de análisis, considerando técnicas de aprendizaje de máquina y aprendizaje de máquina profundo. Nuestra contribución principal se basa en una propuesta de una estrategia metodológica que abarca las fases de preprocesamiento de datos, construcción de modelos predictivos y su evaluación. De los resultados, el mejor modelo clásico fue SVM, con 78% de precisión, y 79% de métrica F1 (F1 score). Para los modelos de Deep Learning, con mejores resultados fueron los modelos clásicos. El modelo con mejor desempeño fue el de Deep Learning Long Short Term Memory (LSTM), alcanzando un 88% de precisión y 89% de métrica F1. El peor de los modelos de Deep Learning fue el CNN, con 77% de precisión como de métrica F1. Concluyendo que, el algoritmo Long Short Term Memory (LSTM) demostró ser el mejor rendimiento, alcanzando hasta un 89% de precisión. 

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Publicado

2023-01-20

Cómo citar

Lovera, F. A., & Cardinale, Y. (2023). Análisis de sentimientos en Twitter: Un estudio comparativo. Revista Científica De Sistemas E Informática, 3(1), e418. https://doi.org/10.51252/rcsi.v3i1.418

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