Sentiment Analysis of COVID-19 using Multimodal Fusion Neural Networks
Sentiment Analysis of COVID-19 using Multimodal Fusion Neural Networks
Author(s): Ermatita Ermatita, Abdiansah Abdiansah, Dian Palupi Rini, Fatmalina FebrySubject(s): ICT Information and Communications Technologies
Published by: UIKTEN - Association for Information Communication Technology Education and Science
Keywords: Multimodal Fusion Neural Networks; COVID-19; Sentiment Analysis
Summary/Abstract: The purpose of this study creates a Sentiment Analysis model of COVID-19 using Multimodal Fusion Neural Networks in real time to model and visualize COVID-19 in Indonesia. This study obtained 87 percent accuracy using the Multimodal Fusion Neural Networks model, a higher 5 percent than the benchmarking model Convolutional Neural Networks. This study proves that the sentiment model built is quite promising and relevant to be implemented.
Journal: TEM Journal
- Issue Year: 11/2022
- Issue No: 3
- Page Range: 1316-1321
- Page Count: 6
- Language: English