COVID-19 Impact Sentiment Analysis on a Topic-based Level

Last December 2019, health officials in Wuhan, a province from China, identified a novel coronavirus called SARS-CoV-2 causing pneumonia. In March 2020, World Health Organization (WHO) declared COVID-19 disease being a pandemic. During quarantine periods, people all over the globe were living under severe and overwhelming circumstances and expressing feelings of loneliness, dread, and anxiety. The pandemic has had a significant impact on the labor markets. As a result, several employees have lost their jobs while others are in grave danger to lose their positions the next day. In this paper, we developed a hybrid approach integrating sentiment analysis combined with topic modeling to analyze the impact of the COVID-19 pandemic on Moroccan citizens. The data used in this study includes comments collected from a well-known news website in Morocco called Hespress. Our approach follows a two-step process. In the first step, we implement a topic modeling method to analyze and extract topics from Arabic comments, and in the second step, we perform topic-based sentiment analysis to classify people’s feedback on extracted topics. The final results revealed that the expressed sentiments regarding all the topics are highly negative. © 2022 River Publishers

Arabic Topic Modeling-Based Sentiment Analysis on COVID-19 Feedback Comments

In March 2020, World Health Organization (WHO) declared COVID-19 disease a pandemic. People worldwide face stressful and overwhelming challenges and express emotions of loneliness, fear, and anxiety during quarantine periods. The pandemic strongly hits labor markets. As a result, many employees lost their jobs, and others are living at a high risk of losing theirs the next day. In this paper, we designed a hybrid approach that combines topic modeling and sentiment analysis methods to mine the impact of the COVID-19 pandemic on Moroccan citizens. The dataset used in this study contains collected comments from Hespress, an online news website. Our approach implements Latent Dirichlet Allocation (LDA) to extract topics from the comments. Consequently, we apply a topic-based sentiment analysis using a pre-trained model to mine people’s feedback regarding the extracted topics. The results showed that expressed sentiments towards all extracted topics are more negative than neutral and positive topic-based sentiments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Forecasting COVID-19 Cases in Morocco: A Deep Learning Approach

The world is severely affected by the COVID-19 pandemic, caused by the SARS-CoV-2 virus. So far, more than 108 million confirmed cases have been recorded, and 2.3 million deaths (according to Statistica data platform). This has created a calamitous situation around the world and fears that the disease will affect everyone in future. Deep learning algorithms could be an effective solution to track COVID-19, predict its growth, and design strategies and policies to manage its spread. Our work applies a mathematical model to analyze and predict the propagation of coronavirus in Morocco by using deep learning techniques applied on time series data. In all tested models, long short-term memory (LSTM) model showed a better performance on predicting daily confirmed cases. The forecasting is based on history of daily confirmed cases recorded from March 2, 2020, the day the first case appeared in Morocco, until February 10, 2020. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.