Deep sentiment analysis with data augmentation in distance education during the pandemic

During the global Covid-19 pandemic, the shutdown of educational institutes has resulted in a phenomenal surge in online learning. Academic activities were shifted to online learning platforms to restrict the influence of COVID-19 and block its spread. For both students and parents, the efficiency of online learning is a major concern, particularly in terms of its suitability for students and teachers, as well as its technological applicability in various social situations. Before the online learning approach can be employed on such a big scale, such challenges must be viewed from different aspects. This study aims to assess the efficiency of online learning by examining individuals' sentiments toward it. Due to social media becoming such an essential form of communication, people's opinions can be observed on platforms like Twitter. The main motivation is to use a Twitter dataset featuring online learning-related tweets. Briefly, we focused on specifying the impact of the Covid-19 pandemic on education in many aspects and parameters by using tweets. We utilized natural language processing models for text classification with a gathered dataset that includes fetching tweets consisting of Covid-19 and education topics. We developed a fine-tuned Long short-term memory (LSTM) model that utilizes data augmentation for classifying the emotional states of individuals. With the deep sentiment analysis model that we proposed, we observed that the negative sentiments were experienced more. © 2022 IEEE.

Global impact of the pandemic on education: a study of natural language processing

School closures due to the Covid-19 pandemic have changed education forever and we have witnessed the rise of online learning platforms. The education units of the countries made great efforts to adapt to this new order. The expanding, quick spread of the virus and careful steps have prompted the quest for reasonable choices for continuing education to guarantee students get appropriate education and are not impacted logically or mentally. Different methods were attempted to understand how students were affected by this big change. In addition to the significance of traditional surveys and consulting services, the utilization of social media analysis is used as a supportive approach. This paper analyzes the feedback of students on social media via tweets. Deep sentiment analysis is employed to identify embedded emotions such as negative, neutral, and positive. We also aimed to classify irrelevant tweets as the fourth category. Our experiments showed that the tweets are mostly biased toward negative emotions. © 2022 IEEE.

A Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System

Due to the global spread of the Covid-19 virus and its variants, new needs and problems have emerged during the pandemic that deeply affects our lives. Wearing masks as the most effective measure to prevent the spread and transmission of the virus has brought various security vulnerabilities. Today we are going through times when wearing a mask is part of our lives, thus, it is very important to identify individuals who violate this rule. Besides, this pandemic makes the traditional biometric authentication systems less effective in many cases such as facial security checks, gated community access control, and facial attendance. So far, in the area of masked face recognition, a small number of contributions have been accomplished. It is definitely imperative to enhance the recognition performance of the traditional face recognition methods on masked faces. Existing masked face recognition approaches are mostly performed based on deep learning models that require plenty of samples. Nevertheless, there are not enough image datasets containing a masked face. As such, the main objective of this study is to identify individuals who do not use masks or use them incorrectly and to verify their identity by building a masked face dataset. On this basis, a novel real-time masked detection service and face recognition mobile application was developed based on an ensemble of fine-tuned lightweight deep Convolutional Neural Networks (CNN). The proposed model achieves 90.40% validation accuracy using 12 individuals' 1849 face samples. Experiments on the five datasets built in this research demonstrate that the proposed system notably enhances the performance of masked face recognition compared to the other state-of-the-art approaches.