Expression Of Annoyance 3 Letters

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Expression Of Annoyance 3 Letters

Expression Of Annoyance 3 Letters

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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 Baina Karqaim 3 , 4 and Cheraim Baina , 4

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

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The impact of COVID-19 on socio-economic fronts, public health aspects and human interaction is undeniable. In the midst of social distancing protocols and stay-at-home rules imposed in many countries, citizens have taken to social media to cope with the emotional turmoil of the pandemic and respond to the regulations issued by the government. To explore the collective emotional response of Moroccan citizens to this pandemic and its effects, we use topic modeling to identify the most dominant topics of interest related to COVID-19 among Moroccan social media users and sentiment/emotion analysis to gain insight into their reactions. to various impact events. The data collected consists of comments related to COVID-19 published on Twitter, Facebook and Youtube and on the websites of two popular online newspapers in Morocco (Hespress and Hibapress) throughout the year 2020. The comments are expressed in Moroccan Dialect (MD) or Modern Standard Arabic (MSA). To perform topic modeling and sentiment classification, we built a first universal language model for the Moroccan dialect (MD-ULM) using available corpora, which we added to our COVID-19 dataset. We demonstrate that our method significantly outperforms classical machine learning classification methods in Topic Modeling, Emotion Recognition, and Polar Sentiment Analysis. To provide real-time infoveillance of these feelings, we have developed an online platform to automate the execution of the various processes, and in particular the collection of regular data. This platform is intended to be a decision support tool for the mitigation and management of COVID-19 in Morocco.

COVID19; emotion analysis; machine learning; polar sentiment analysis; theme template; universal language model for Moroccan dialect COVID-19; emotion analysis; machine learning; polar sentiment analysis; theme template; universal language model for the Moroccan dialect

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

Expression Of Annoyance 3 Letters

Although the epidemic began and was geographically concentrated in mainland China at first, the rate of increase in cases became greater in the rest of the world than in China on February 26, 2020 [2]. The geographic expansion of the epidemic has reached the rest of the world, hitting Italy, Iran, Spain and the United States the hardest. The World Health Organization declared COVID-19 a pandemic health emergency on March 11, 2020, as most countries went into states of health emergency and implemented various levels of lockdown and distancing protocols. social.

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As of June 25, 2021, 179 million cases of COVID-19 have been recorded worldwide and the resulting number of deaths has reached 3.88 million [3]. In Morocco, reports put the number of confirmed cases at around 527, 147 with 9247 resulting deaths [3].

Since its emergence, COVID-19 has had a dramatic socio-economic impact in most countries as well as global public health, food systems and employment [4]. During this time, social media sites have become a necessary tool for information sharing, communication, entertainment and as a free opinion sharing platform for everyone. Becoming an increasingly reliable emotional outlet for communities around the world [5], social media has played a crucial role in influencing people’s perception 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 reflects the effect not only of the psychological burden of life during the pandemic [6] , but also of emotions. impact of necessary mitigation strategies such as quarantine, lockdown, curfew and social distancing.

The main objective of this study is to investigate how Moroccan social media users respond to health measures and other regulations, as well as their collective emotional reaction to different aspects of life during the COVID-19 pandemic. More specifically, we want to gauge and analyze the emotional response of Moroccan social media users to COVID-19 and its impact on public health, education, economy and social life. Additionally, we explore topics of discussion related to COVID-19 among Moroccan social media users.

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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 gather comments related to COVID-19 made by Moroccan social media users and written in Moroccan dialect (MD) and in Modern Standard Arabic (MSA). . In addition, we collected user comments in response to news articles on popular online media in Morocco such as Hespress www.hespress.com (accessed October 15, 2021) and www.ar.hibapress. com (accessed October 15, 2021). A collection of major events and government announcements during this time period was compiled to investigate their impact on the emotions of social media users.

A dataset aggregated from different sources of online comments was constructed and​​​​​​​​​​​​​​through an annotation process to label the different themes and emotions conveyed by Moroccan social media users in relation to COVID-19. Various Machine Learning algorithms for Topic Modeling and Sentiment Analysis were then tested on this data set.

The rest of this paper is organized as follows. Related work is presented in Section 2. Our methodology for collecting comments from different social media sources and their annotation for topic modeling and emotion classification is presented in Section 3.1. The conceptualization of the first Moroccan dialect universal language model is detailed in Section 3.2. The temporal evolution of emotional responses induced by COVID-19 and topics of interest, as well as the results of emotion classification, polar sentiment classification and topic modeling are illustrated in Section 4. Our platform online for our pipeline automation and decision support is presented. in Section 5. Conclusions are summarized in Section 6.

Expression Of Annoyance 3 Letters

The increase in COVID-19 cases around the world and the severity of its impact on public health have greatly contributed to making COVID-19 the dominant topic of scientific literature in the past year. Numerous studies in various domains have been published and widely cited. A particular area of ​​interest involves the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) to analyze COVID-19 content, such as scientific articles, social media posts and news headlines .

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To this end, a set of studies focused on the use of issue models for a better understanding of 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 the Latent Dirichlet Allocation (LDA), the authors of [8] focused on the Italian Twitter community, while the authors of [9] analyzed the tweets written in English.

In [10], Biterm Topic Model (BTM) was used to discover and describe user-generated interactions that could be related to the symptoms of COVID-19 and indications of recovery from the disease, as well as difficulties in access to the test. Other researchers have used topic models to analyze newspaper articles. For example, in [11], the authors developed a thematic analysis system of news articles related to COVID-19 in Canada. Also, in [12], the authors employed a topic model approach to examine the news during the early stages of the outbreak in China.

While the aforementioned studies focused on the theme model, another set of works aimed at using sentiment analysis. The authors of [13] used classical machine learning algorithms such as

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