Expression Of Annoyance 3 Letters Starts With O

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Expression Of Annoyance 3 Letters Starts With O

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By Abdelghani Ghanem 1, * , Chaimae Asaad 1, 2 , Hakim Hafidi 1 , Youness Moukafih 1 , Bassma Guermah 1 , Nada Sbihi 1 , Mehdi Zakroum 1 , Mounir Ghogho 1 , Meriem Dairi 3 , Mariam Cherqaoui 4 and Karim Baina 2

Received: 30 August 2021 / Revised: 13 October 2021 / Accepted: 16 October 2021 / Published: 19 November 2021

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The impact of COVID-19 on socio-economic fronts, public health aspects and human interaction cannot be denied. Amid social distancing protocols and stay-at-home orders imposed in several countries, citizens have taken to social media to cope with the emotional turmoil of the pandemic and respond to government regulations. To discover the collective emotional response of Moroccan citizens to this pandemic and its effects, we use topic modeling to identify the most dominant topics related to COVID-19 of interest to Moroccan social media users and sentiment/emotion analysis to gain insight into their reactions to various high-profile events. The collected data consists of comments related to COVID-19 posted on Twitter, Facebook and Youtube and on the websites of two popular online news outlets in Morocco (Hespress and Hibapress) throughout the year 2020. The comments are expressed in the Moroccan dialect (MD) or Modern Standard Arabic (MSA). To perform thematic modeling and sentiment classification, we built the first universal language model for the Moroccan dialect (MD-ULM) using available corpora, which we fine-tuned using our COVID-19 dataset. We have shown that our method significantly outperforms classic machine learning classification methods in topic modeling, emotion recognition, and polar emotion analysis. To ensure real-time information control of these emotions, we have developed an online platform for automating the implementation of various processes and, in particular, regular data collection. This platform is intended to be a decision support tool for the mitigation and management of COVID-19 in Morocco.

COVID-19; sentiment analysis; machine learning; polar sentiment analysis; thematic modeling; universal language model for Moroccan dialect COVID-19; sentiment analysis; machine learning; polar sentiment analysis; thematic modeling; a universal language model for the Moroccan dialect

In late December 2019, the first cases of pneumonia of unknown origin were reported in Wuhan, the capital of Hubei province in central China. The causative agent was identified as a novel RNA-enveloped betacoronavirus. Based on the phylogenetic similarity to the previously isolated severe acute respiratory syndrome coronavirus (SARS-CoV), the new virus was named SARS-CoV-2 [1].

Although the epidemic started and was initially geographically concentrated in Mainland China, on 26 February 2020, the rate of increase in cases became greater in the rest of the world than in China [2]. The geographic spread of the epidemic has reached the rest of the world, with Italy, Iran, Spain and the United States the most affected. On March 11, 2020, the World Health Organization declared COVID-19 a pandemic health emergency, with most countries entering sanitary emergencies and implementing varying degrees of lockdowns and social distancing protocols.

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By June 25, 2021, 179 million cases of COVID-19 had been reported worldwide, with the resulting number of deaths reaching 3.88 million [3]. In Morocco, the number of confirmed cases is reported to be around 527,147 with 9247 deaths [3].

Since its emergence, COVID-19 has had a dramatic socioeconomic impact on most countries, as well as global public health, food systems, and employment [4]. During this time, social media sites have become an essential tool for sharing information, communication, entertainment and a free platform for sharing opinions. By becoming an increasingly reliable emotional outlet for communities around the world [5], social media has played a key role in influencing people’s perceptions of the COVID-19 outbreak and the crisis response strategies put in place by governments.

In the midst of the COVID-19 pandemic, the Moroccan population, like the rest of the world, has experienced a turmoil of emotions that reflect the impact of not only the psychological strain of living during the pandemic [6] but also the emotional impact of the necessary mitigation strategies such as quarantine, confinement, curfews and social distance.

The main objective of this study is to investigate how Moroccan social media users respond to sanitary measures and other regulations and their overall emotional response to different aspects of life during the COVID-19 pandemic. Specifically, we aim to measure and analyze the emotional response of Moroccan social media users to COVID-19 and its impact on public health, education, economy and social life. In addition, we discover the topics related to the COVID-19 virus that Moroccan social media users are discussing.

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To this end, we conducted a year-long data collection campaign on social media platforms such as Facebook and Twitter to collect comments related to the disease COVID-19 written by Moroccan social media users and written in the Moroccan dialect (MD) and Modern Standard Arabic (MSA). . In addition, we collected user comments in response to news on popular online news sites in Morocco, such as Hespress www.hespress.com (accessed on October 15, 2021) and www.ar.hibapress.com (accessed on October 15, 2021). A collection of major events and government announcements during this time period was compiled to explore their impact on the emotions of social media users.

A combined dataset was compiled from different sources of online comments that were subjected to an annotation process to indicate different topics and emotions conveyed by Moroccan social media users regarding COVID-19. Several machine learning algorithms for topic modeling and sentiment analysis were then tested on this dataset.

The remainder of this document is organized as follows. Related work is presented in Section 2. Our methodology for collecting comments from various social media sources and their annotations for topic modeling and sentiment classification is presented in Section 3.1. The conceptualization of the first universal language model of the Moroccan dialect is detailed in Section 3.2. The temporal evolution of emotional responses and topics of interest caused by COVID-19, and the results of emotion classification, polar emotion classification, and topic modeling are shown in Section 4. Our online platform for automating our pipeline and decision support is presented in Section 5. The conclusions are summarized in Section 6.

The increase in cases of COVID-19 worldwide and the severity of its impact on public health have greatly contributed to COVID-19 being a dominant topic in the scientific literature over the past year. Many studies in various fields have been published and highly cited. One particular area of ​​interest involves using artificial intelligence (AI) and natural language processing (NLP) to analyze content related to COVID-19, such as scientific articles, social media posts and news headlines.

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Within this scope, a range of studies focused on the use of thematic modeling to better understand different aspects of the pandemic. In [7], the authors analyzed the reactions of Twitter users using Sequential Pattern Mining (SPM) techniques. To study the main topics of interest on Twitter using Latent Dirichlet Allocation (LDA), the authors of [8] focused on the Italian Twitter community, while the authors of [9] analyzed tweets written in English.

In [10], the Biterm Topic Model (BTM) was used to detect and describe user-generated interactions that could be related to COVID-19 symptoms and signs of disease recovery and difficulties in accessing testing. Other researchers have used thematic modeling to analyze newspaper articles. For example, in [11], the authors developed a thematic analysis system for articles on COVID-19 in Canada. In addition, the authors in [12] used a topic modeling approach to study the news in the early stages of the outbreak in China.

While the aforementioned studies focused on thematic modeling, another set of works is devoted to the application of sentiment analysis. The authors [13] used classic machine learning algorithms, such as e.g