Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models

Frontiers in Public Health

Public Health

Ioannis Dokas¹  Yiannis Kourkoutas²  Theodoros Constantinidis³  Christos Stefanis³  Athanasios Tselemponis³  Christos Kontogiorgis³  Elpida Giorgi³  Konstantinos Kalentzis³  Eugenia Bezirtzoglou³  Christina Tsigalou⁴  Ekaterini Chatzak⁵  Evangelia Nena⁶ 

[1] Department of Civil Engineering, Democritus University of Thrace, Komotini, Greece;Laboratory of Applied Microbiology, Department of Molecular Biology and Genetics, Democritus University of Thrace, Alexandroupolis, Greece;Laboratory of Hygiene and Environmental Protection, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece;Laboratory of Microbiology, Medical School, Democritus University of Thrace, Alexandroupolis, Greece;Laboratory of Pharmacology, Medical School, Democritus University of Thrace, Alexandroupolis, Greece;Pre-Clinical Education, Laboratory of Social Medicine, Medical School, Democritus University of Thrace, Alexandroupolis, Greece;

关键词: sentiment;    classification;    COVID-19;    Facebook;    public health;    machine learning;    natural language processing;   

DOI  :  10.3389/fpubh.2023.1191730

 received in 2023-03-22, accepted in 2023-06-30,  发布年份 2023

来源: Frontiers

【 摘 要 】

The present research deals with sentiment analysis performed with Microsoft Azure Machine Learning Studio to classify Facebook posts on the Greek National Public Health Organization (EODY) from November 2021 to January 2022 during the pandemic. Positive, negative and neutral sentiments were included after processing 300 reviews. This approach involved analyzing the words appearing in the comments and exploring the sentiments related to daily surveillance reports of COVID-19 published on the EODY Facebook page. Moreover, machine learning algorithms were implemented to predict the classification of sentiments. This research assesses the efficiency of a few popular machine learning models, which is one of the initial efforts in Greece in this domain. People have negative sentiments toward COVID surveillance reports. Words with the highest frequency of occurrence include government, vaccinated people, unvaccinated, telephone communication, health measures, virus, COVID-19 rapid/molecular tests, and of course, COVID-19. The experimental results disclose additionally that two classifiers, namely two class Neural Network and two class Bayes Point Machine, achieved high sentiment analysis accuracy and F1 score, particularly 87% and over 35%. A significant limitation of this study may be the need for more comparison with other research attempts that identified the sentiments of the EODY surveillance reports of COVID in Greece. Machine learning models can provide critical information combating public health hazards and enrich communication strategies and proactive actions in public health issues and opinion management during the COVID-19 pandemic.

【 授权许可】

Unknown   
Copyright © 2023 Stefanis, Giorgi, Kalentzis, Tselemponis, Nena, Tsigalou, Kontogiorgis, Kourkoutas, Chatzak, Dokas, Constantinidis and Bezirtzoglou.