Egyptian Student Sentiment Analysis Using Word2vec During the Coronavirus (Covid-19) Pandemic

Mostafa, Lamiaa

doi:10.1007/978-3-030-58669-0_18

Cited by 22 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since then, HEIs around the world were obligated to close their campuses so that students could follow social distancing measures and minimize gatherings in order to decrease the transmission of the virus (Toquero, 2020;Mahdy, 2020). Thus, education at all levels, including both undergraduate and postgraduate levels, had been significantly affected, interrupting the learning of more than one billion students in 129 countries around the globe (Mostafa, 2020;UNESCO, 2020). This situation had challenged the education systems worldwide, causing HEIs to rapidly change their traditional face-to-face classes to online classes (Abelskamp & Santamarinam, 2020).…”

Section: Introductionmentioning

confidence: 99%

You’re Such A Burden, COVID-19! Psychological Impact among Postgraduate Students in Malaysia

Ramli¹,

Nasir²,

Som³

2021

IJARBSS

View full text Add to dashboard Cite

The COVID-19 pandemic has given significant impacts on students around the world. This pandemic had caused the closure of higher education institutions (HEIs), hence affecting the psychological well-being of the students. Currently, little is known about the psychological impact of the pandemic upon tertiary-level students. Therefore, this study aims to identify the psychological impact of the pandemic among postgraduate students in Malaysia, as well as to determine the relationship between psychological impact of the pandemic with postgraduate students' socio-demographic. An online survey through Google Form is conducted and participated by a total of 606 postgraduate students from different backgrounds. The findings indicate that most postgraduate students experienced panic and nervousness when thinking about their studies and feeling dejected with their academic progress. This study also reveals that the postgraduate student's socio-demographic (age, monthly income, source of income, study program, mode of study, and year of study) have significant relationship with psychological impact of the pandemic. This study is urgently vital for HEIs and relevant government agencies to assist them in formulating suitable policies and strategies in order to maintain a healthy psychological environment for postgraduate students amidst the pandemic.

show abstract

Section: Introductionmentioning

confidence: 99%

You’re Such A Burden, COVID-19! Psychological Impact among Postgraduate Students in Malaysia

Ramli¹,

Nasir²,

Som³

2021

IJARBSS

View full text Add to dashboard Cite

show abstract

“…Using W2V technique and Machine Learning techniques, a Sentiment Analysis Model has been proposed to analyze the emotions of Egyptian students in the learning process with the pandemic. The word embedding process was then evaluated by NB, SVM and DT classification, and evaluated for precision, recall and accuracy [8].…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis with ensemble and machine learning methods in multi-domain datasets

Başarslan

Kayaalp

2023

Turkish Journal of Engineering

View full text Add to dashboard Cite

The first place to get ideas on all the activities considered to occur in everyday life was the comments on the websites. This is an area that deals with these interpretations in the natural language processing, which is a sub-branch of artificial intelligence. Sentiment analysis studies, which is a task of natural language processing are carried out to give people an idea and even guide them with such comments. In this study, sentiment analysis was implemented on public user feedback on websites in two different areas. TripAdvisor dataset includes positive or negative user comments about hotels. And Rotten Tomatoes dataset includes positive (fresh) or negative (rotten) user comments about films. Sentiments analysis on datasets have been carried out by using Word2Vec word embedding model, which learns the vector representations of each word containing the positive or negative meaning of the sentences, and the Term Frequency Inverse Document Frequency text representation model with four machine learning methods (Naïve Bayes-NB, Support Vector Machines-SVM, Logistic Regression-LR, K-Nearest Neighbour-kNN) and two ensemble learning methods (Stacking, Majority Voting-MV). Accuracy and F-measure is used as a performance metric experiments. According to the results, Ensemble learning methods have shown better results than single machine learning algorithms. Among the overall approaches, MV outperformed Stacking.

show abstract

“…The main goal for every worker is job satisfaction and for educational institutions, this is correlated with student satisfaction [17]. By mid-2020, roughly 70 countries had incorporated some form of online education system to make up for the academic year disrupted by the pandemic [18].…”

Section: Sentiment Analysis Of the Education Sector In A Pandemicmentioning

confidence: 99%

Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning

2021

View full text Add to dashboard Cite

In this study, we qualitatively and quantitatively examine the effects of COVID-19 on classrooms, students, and educators. Using a new Twitter dataset specific to South Korea during the pandemic, we sample the sentiment and strain on students and educators using applied machine learning techniques in order to identify various topical pain points emerging during the pandemic. Our contributions include a novel and open source geo-fenced dataset on student and educator opinion within South Korea that we are making available to other researchers as well. We also identify trends in sentiment and polarity over the pandemic timeline, as well as key drivers behind the sentiments. Moreover, we provide a comparative analysis of two widely used pre-trained sentiment analysis approaches with TextBlob and VADER using statistical significance tests. Ultimately, we analyze how public opinion shifted on the pandemic in terms of positive sentiments about accessing course materials, online support communities, access to classes, and creativity, to negative sentiments about mental fatigue, job loss, student concerns, and overwhelmed institutions. We also initiate initial discussions about the concept of actionable sentiment analysis by overlapping polarity with the concept of trigger management to assist users in coping with negative emotions. We hope that insights from this preliminary study can promote further utilization of social media datasets to evaluate government messaging, population sentiment, and multi-dimensional analysis of pandemics.

show abstract

Egyptian Student Sentiment Analysis Using Word2vec During the Coronavirus (Covid-19) Pandemic

Cited by 22 publications

References 21 publications

You’re Such A Burden, COVID-19! Psychological Impact among Postgraduate Students in Malaysia

You’re Such A Burden, COVID-19! Psychological Impact among Postgraduate Students in Malaysia

Sentiment analysis with ensemble and machine learning methods in multi-domain datasets

Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning

Contact Info

Product

Resources

About