Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers

Alex Mirugwe
* ORCID logo ,
Clare Ashaba
,
Alice Namale
,
Evelyn Akello
,
Edward Bichetero
,
Edgar Kansiime
,
Juwa Nyirenda
Version 1 : Received: 8 April 2024 / Approved: 8 April 2024 / Online: 9 April 2024 (07:20:36 CEST)

How to cite: Mirugwe, A.; Ashaba, C.; Namale, A.; Akello, E.; Bichetero, E.; Kansiime, E.; Nyirenda, J. Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers. Preprints 2024, 2024040604. https://doi.org/10.20944/preprints202404.0604.v1 Mirugwe, A.; Ashaba, C.; Namale, A.; Akello, E.; Bichetero, E.; Kansiime, E.; Nyirenda, J. Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers. Preprints 2024, 2024040604. https://doi.org/10.20944/preprints202404.0604.v1

Abstract

The Ebola virus disease (EVD) is an extremely contagious and fatal illness caused by the Ebola virus. Recently, Uganda witnessed an outbreak of EVD, which generated a lot of attention on various social media platforms. To ensure effective communication and implementation of targeted health interventions, it is crucial for stakeholders to comprehend the sentiments expressed in the posts and discussions on these online platforms. In this study, we used deep learning techniques to analyze the sentiments expressed in Ebola-related tweets during the outbreak. We explored the application of three deep learning techniques to classify the sentiments in 8,395 tweets as positive, neutral, or negative. The techniques examined included a 6-layer convolutional neural network (CNN), a 6-layer long-short-term memory model (LSTM), and an 8-layer Bidirectional Encoder Representations from Transformers (BERT) model. The study found that the BERT model outperformed both the CNN and LSTM-based models across all the evaluation metrics, achieving a remarkable classification accuracy of 95\%. These findings confirm the reported effectiveness of transformer-based architectures in tasks related to natural language processing, such as sentiment analysis.

Keywords

Ebola; Deep learning; Sentiment Analysis; Natural Language Processing

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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