Stacked Bi-directional Long Short Term Memory model for Multi-Class Arabic Sentiment Analysis on Covid-19

The world has been affected by the Covid-19 epidemic during the last three years. During that period, most people tended to use social networks, where by searching for topics related to Covid-19, information could be provided to manage decisions by organizations or governments about public health. With the importance of the Arabic language, despite the lack of research targeting it, using Arabic language as a source of data and analyzing it due to the large number of users on social networks gives an impetus to understand people's feelings about the Covid-19 pandemic. One of the challenges facing sentiment analysis in Arabic is the use of dialects. The most common and existing methods used have been quite ineffective as they are oblivious to contextual information and cannot handle long-distance word dependencies. The Iraqi Arabic dialect is one of the Arabic dialects that still suffers from a lack of research in sentiment analysis. In this study, the official page of the Iraqi Ministry of Health on Facebook was used to collect and analysis comments. Word2vec model is incorporated to extract words semantic characteristics. To capture contextual features, Stacked Bi-directional Long Short Term Memory model (Stacked Bi-LSTM) utilizes sequential word vectors derived from the Continuous Bag of Words model. When compared to most common and existing approaches, the proposed method performed well. © 2022 IEEE.

Fading Voices: COVID-19, Language Death and the Case of Bergamasco in Italy

The chapter explores the impact of the Coronavirus Disease 2019 (COVID-19) pandemic on language endangerment in Italy. Despite their misleading name, Italian Dialects (IDs) are sister languages of Italian and independently developed from Latin. Since the introduction of compulsory education in the 1960s and concurrent Italy's sharp industrialisation, IDs are in decline, and a clear language shift is detectable towards the dominant national language: Italian. IDs are hence only discretely vital among the aging population with the younger generations having broken their intergenerational transmission. Given the higher mortality rates among the elderly and the strict intermittent national and regional lockdowns enforced by the Italian Government, the COVID-19 pandemic has accelerated the process of language death across Italy. This is particularly noticeable in Bergamo, one of the Italian provinces most affected by COVID-19, where the disease caused a five-fold increase in excess mortality in March 2020. In the same period, COVID-19 was the attributable cause of death in half of those older than 50 and the mean age of those dying for COVID-19 was 80. The death toll of COVID-19 on the elderly population has left Bergamasco, the ancestral language of Bergamo, a step closer to extinction. By taking Bergamasco as a case study, the chapter denounces the vulnerability of IDs and the negative impact of the COVID-19 pandemic on their level of vitality. To this aim, the chapter presents a quantitative sociolinguistic study of the vitality of Bergamasco in relation to COVID-19 incidence and lethality rates in the province of Bergamo. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Sentiment analysis of students' Facebook comments toward university announcements

Students' opinions are among the critical indicators to evaluate the university teaching process. However, due to the absence of an official online system in most universities that provides a mechanism for obtaining students' opinions on several university announcements, most students use various social networks to express their feelings and provide their opinions toward these announcements. We present, through this paper, sentiment analysis of Facebook comments written in the Moroccan Arabic dialect. These comments reflect the opinions of students about university announcements during the COVID-19 pandemic, especially those related to teaching mode and ex-am planning. Then, the comments collected were cleaned, preprocessed, and manually classified into four categories, namely positive, neutral, negative, and bipolar. Further, data dimensionality reduction is applied using TF-IDF and Chi-square test. Finally, we evaluated the performance of three standard classifiers, i.e., Naïve Bayesian (NB), Support Vector Machines (SVM), and Random Forests (RF) using k-fold cross-validation. The results showed that the SVM-based classifier performs as well as the RF-based classifier regarding the classification's accuracy and F1-score, while the NB-based classifier lags behind them. © 2022 IEEE.

Inclusive Education in Times of Crisis

Children are powerful agents of change when given the chance to succeed. However, due to protracted crises and natural disasters, an estimated 33 million children have been forcibly displaced worldwide and denied their childhoods and an opportunity to reach their full potential. Psychosocial support is key to bridging the learning gap that displaced and out-of-school children face. Introducing this support through social emotional learning (SEL) allows children to build the resilience they need to thrive. The lack of psychosocial support and the impact of forced displacement on the mental health of children is an ongoing problem that has been further exacerbated by the COVID-19 pandemic. Working to address the effects of toxic stress and adverse experiences in childhood, can mitigate the vicious cycle of trauma and violence and reduce the rippling effect on future generations.

DZchatbot: A Medical Assistant Chatbot in the Algerian Arabic Dialect using Seq2Seq Model

In light of the global crisis like COVID-19, many people are afraid to leave the house and visit the doctor for fear of these epidemics. On the other side, the amazing development of artificial intelligence has led to chatbots' emergence and use in several fields. Therefore, in this paper, we propose to build an automated chatbot system that interacts with people in the Arabic Algerian dialect and helps patients ask general medical questions. To achieve this purpose, we propose three sequence-to-sequence models based on three Recurrent Neural Networks encoder-decoders: Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Gated Recurrent Unit, to understand the user's request and provide the right useful answer. Experimentally, we have collected medical data of 2150 pairs. The results were very promising, and the proposed chatbot performed excellently in handling user questions. © 2022 IEEE.

New Education Policy: Contents and Sentiment Analysis

The year 2020 has been an extraordinary year for nations across the globe. In India, aside from Covid19, one of the significant changes that occurred was the advancement of the New Education Policy (NEP) 2020. Time by time, different boards of trustees have suggested expanding the allotment of the spending plan for training to 6% of the GDP, this has prompted the interest of specialists. This paper expects to recognize the worries and focal point of NEP 2020. The creators have used subjective information investigation strategies to comprehend basic areas of focal point of strategy records and PC helped subjective information examination programming to resolve the issues. The examination likewise utilizes information from Twitter. The information (strategy archives) was downloaded from the Ministry of Human Resource Development (MHRD) site. The paper recognizes three urgent parts of the arrangement - course, language, and understudies, for the advanced education area. This paper additionally examines the feeling investigation, connected with NEP 2020. It was found that the vast majority think about the strategy a positive and inviting advance.

Confronting the gated community: Towards a decolonial critique of violence beyond the paradigm of war

This article investigates the works of Dussel, Maldonado-Torres, and Mbembe as representatives of a tendency in the field of decolonial thought to assume the templates of warfare and the camp as the archetypal registers of violence in the contemporary world. Identifying this focus as the remnant of a Eurocentric vocabulary (the paradigm of war), the article proposes a shift from the language of warfare predominant in the field to a language of welfare. The article turns to the gated community (GC), instead of the camp, and the imperatives of (re)creation, instead of the logics of elimination, as new templates with which to make sense of modern/colonial violence. Moving beyond militaristic imagery, the analysis shows a form of violence that emerges as a response to the endless search for a life of convenience inside the walls of the GC. To this end, the article advances the concept of the dialect of disarrangement, the enforced but uneasy encounter between two subjectivities that inhabit the GC: the patrons (the homeowners who consume the easy life) and servants (the racialised service staff). In the GC, violence emerges in attempts to respond to this (in)convenient encounter via misrepresentations of both patrons and servants as out of their place.