Expression Of Annoyance 4 Letters

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Expression Of Annoyance 4 Letters

Expression Of Annoyance 4 Letters

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Letter, 1778 September 22, Charleston, S.c., Rawlins Lowndes To Henry Laurens

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Abdul Ghani Ghanem 1, *, Chemi Asad 1, 2, Hakeem Hafidi 1, Yunus Mokafi 1, Basma Gorma 1, Nida Sabihi 1, Mahdi Zakrum 1, Munir Ghousho 1, Maryam Deri Babina 4, Maryam Deri 3

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 social and economic fronts, public health related aspects and human interaction is undeniable. Amidst social distancing protocols and stay-at-home rules enforced in many countries, citizens took to social media to address the emotional crisis of the pandemic and respond to government-issued regulations. To highlight the collective emotional response of Moroccan citizens to this pandemic and its effects, we use topic modeling to identify the most dominant COVID-19 related topics of interest among Moroccan social media users. and sentiment/sentiment analysis to gain insight into their reactions. At various inspiring events. The data collected consists of comments related to COVID-19 posted on Twitter, Facebook and Youtube and on the websites of two popular Moroccan online news outlets (Hespress and Habapress) throughout the year 2020. Modern Standard Arabic (MSA). To perform topic modeling and sentiment classification, we developed the first Universal Language Model for the Moroccan language (MD-ULM) using available corpora, which we fine-tuned using our COVID-19 dataset. We show that our approach outperforms classical machine learning classification methods in topic modeling, sentiment recognition and polar sentiment analysis. To provide real-time information of these sentiments, we have developed an online platform to automate the execution of various processes, and especially regular data collection. This platform is a decision-making support tool for COVID-19 mitigation and management in Morocco.

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COVID19; emotional analysis; machine learning; Analysis of polar impulses; topic modeling; Universal Language Model for Moroccan Language COVID-19; emotional analysis; machine learning; Analysis of polar impulses; topic modeling; Universal language model for the Moroccan language

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

Expression Of Annoyance 4 Letters

Although the outbreak began and was geographically focused on mainland China, the rate of increase in cases in the rest of the world on February 26, 2020 was higher than within China [2]. The geographical expansion of the epidemic reached the rest of the world, which severely affected Italy, Iran, Spain and the United States. The World Health Organization declared COVID-19 a pandemic health emergency on March 11, 2020, as most countries went into states of sanitary emergency and implemented varying levels of lockdowns and social distancing.

Expression Of Emotional–motivational Connotations With A One Word Utterance: The Journal Of The Acoustical Society Of America: Vol 102, No 3

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

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

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In the midst of the COVID-19 pandemic, the Moroccan population, similar to the rest of the world, is going through a turmoil of emotions, which reflects not only the impact of the psychological burden of life during the pandemic [6] but also the emotional one. . The impact of essential mitigation strategies such as quarantines, lockdowns, curfews and social distancing.

The main objective of this study is to investigate how Moroccan social media users are responding to hygiene measures and other regulations, as well as their collective emotional reactions to different aspects of life during the COVID-19 pandemic. More specifically, we aim to assess and analyze the emotional response of Moroccan social media users to COVID-19 and its effects on public health, education, economy and social life. Additionally, we explored the COVID-19-related topics of discussion among Moroccan social media users.

Deep Fake Detection Using A Sparse Auto Encoder With A Graph Capsule Dual Graph Cnn [peerj]

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 COVID-19 by Moroccan social media users in the Moroccan language (MD) and They are written in Modern Standard Arabic (MSA). . In addition, we collected user opinions in response to news articles on popular online news outlets in Morocco such as Hespress (accessed 15 October 2021) and (accessed 15 October 2021). done A collection of key events and government announcements during this period was compiled to investigate their impact on the emotions of social media users.

An aggregate dataset was created from various sources of online comments and put through an interpretive process to label different topics and sentiments expressed by Moroccan social media users regarding COVID-19. Several machine learning algorithms for topic modeling and sentiment analysis were then tested on this dataset.

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The remainder of this paper is organized as follows. Related work is presented in Section 2. Our method for collecting comments from different social media sources and interpreting them for topic modeling and sentiment classification is presented in Section 3.1. The concept of the first Moroccan dialect universal language model is detailed in Section 3.2. The temporal evolution of COVID-19 emotional responses and topics of interest as well as the results of emotion classification, polar emotion classification and topic modeling are described in Section 4. Our online platform is offered to help automate our pipeline and decision-making. In section 5. The results are summarized in Section 6.

Expression Of Annoyance 4 Letters

The increase in COVID-19 cases worldwide and the gravity of its impact on public health have contributed greatly to making COVID-19 the dominant topic of scientific literature this past year. Many studies in different domains have been published and highly cited. A particular area of ​​interest includes 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.

Text‐based Emotion Detection: Advances, Challenges, And Opportunities

Within this framework, a set of studies focused on using topic 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 BTM Topic Model (BTM) was used to discover and describe user-generated conversations that may be related to the symptoms of COVID-19 and disease recovery indicators as well as to testing. Access difficulties. Other researchers used topic modeling to analyze newspaper articles. For example, in [11], the authors developed a topic analysis system of news articles related to COVID-19 in Canada. Furthermore, in [12], the authors used a topic modeling approach to examine news during the early stages of the outbreak in China.

While the aforementioned studies focus on topic modeling, another set of works aims to use sentiment analysis. The authors [13] used classical machine learning algorithms such as

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Halo, Saya adalah penulis artikel dengan judul Expression Of Annoyance 4 Letters yang dipublish pada September 11, 2022 di website Caipm

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