Sentiment analysis and emotion detection of post-COVID educational Tweets: Jordan case

Qaqish, Evon; Aranki, Aseel; Etaiwi, Wael

doi:10.1007/s13278-023-01041-8

Cited by 7 publications

(9 citation statements)

References 43 publications

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“…In contrast, the highest negative sentiment was observed in biology (28%) and chemistry (24.6%). Our findings are different from previous studies that mostly investigated negative attitudes towards distance learning during the pandemic [28,47,48,49,50].…”

Section: Discussioncontrasting

confidence: 99%

Self-Attention-Based Bi-LSTM Model for Sentiment Analysis on Tweets about Distance Learning in Higher Education

Lasri

Riadsolh

ElBelkacemi

2023

Int. J. Emerg. Technol. Learn.

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For limiting the COVID-19 spread, countries around the world have implemented prevention measures such as lockdowns, social distancing, and the closers of educational institutions. Therefore, most academic activities are shifted to distance learning. This study proposes a deep learning approach for analyzing people’s sentiments (positive, negative, and neutral) from Twitter regarding distance learning in higher education. We collected and pre-processed 24642 English tweets about distance learning posted between July 20, 2022, and November 06, 2022. Then, a self-attention-based Bi-LSTM model with GloVe word embedding was used for sentiment classification. The proposed model performance was compared to LSTM (Long Short Term Memory), Bi-LSTM (Bidirectional-LSTM), and CNN-Bi-LSTM (Convolutional Neural Network-Bi-LSTM). Our proposed model obtains the best test accuracy of 95% on a stratified 90:10 split ratio. The results reveal generally neutral sentiments about distance learning for higher education, followed by positive sentiments, particularly in psychology and computer science, and negative sentiments in biology and chemistry. According to the obtained results, the proposed approach outperformed the state-of-art methods.

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Section: Discussioncontrasting

confidence: 99%

Self-Attention-Based Bi-LSTM Model for Sentiment Analysis on Tweets about Distance Learning in Higher Education

Lasri

Riadsolh

ElBelkacemi

2023

Int. J. Emerg. Technol. Learn.

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“…Supervised Machine Learning Algorithms Articles Decision Tree(DT) [1], [2], [5], [6], [8], [12], [13], [15], [16]], [17], [18], [21], [22], [26], [30], [33], [38], [40], [45], [47], [50],[ Support Vector Machine(SVM) [51], [52], [55], [58], [59], [61], [67], [68], [71], [72], [73],…”

Section: Table (7) Analysis Of Supervised and Unsupervised Machine Le...mentioning

confidence: 99%

“…Table (8): Relevant Lexicons Lexicons Related Articles VADER(Valence Aware Dictionary for Sentiment Reasoning) [6], [8], [54], [58], [69] Sentiwordnet [15], [35], [55], [58] Sentistrength [58] TextBlob [15], [58] MPQA (Multi-Perspective Question Answering) [58], [42] Thirdly: What are the most prevalent and effective measuring metrics used by previous researchers in carrying out sentiment analysis? To check for the accuracy of the model, evaluation metrics such as precision, F1-score, and recall, need to be evaluated.…”

Section: Table (7) Analysis Of Supervised and Unsupervised Machine Le...mentioning

confidence: 99%

“…This brought about fresh debates, divided opinions, different sentiments among scholars and learners in the education sectors within that period as result of concept of a students must have 70% class attendance before sitting for any exam. Mining these information from all stakeholders, the new realities for our education sector to move forward at this point in time is for us all to accept the virtual mode of teaching and learning processes, including test and exams to be conducted online which add more impetus to idea of distance learning and massive opened online distance learning (Qaqish et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning

Oghu,

Ogbuju,

Abiodun

et al. 2023

AIMS Research Publication

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The idea of modernization has become a norm in society, and the quality of the education sector that is been delivered to society is worth being assessed, and this will reflect the caliber of graduands these institutions are producing. As the key to this assessment is to x-ray clear-cut metrics used in the assessment of these institutions of higher learning by regulating authorities, parents, and other stakeholders These institutions' academic deliverables in terms of availability of experienced teachers, modern teaching and learning amenities, open access to learning materials, open access to assessment results, and timely responses to transcripts application to mention just a few needs proper evaluation. A lot of institutions' indices of performance are not encouraging, which translates to a high level of students dropping out, poor student performance, and low productivity which affects the standard of living and overall planned change that education is supposed to bring to the generality of the society at large. We must appreciate the efforts of previous researchers who have spent precious time using various sentiment analysis tools to unveil these challenges facing our education institutions and providing far-reaching measures that will make these institutions improve all indexes of measurement approved by regulating authorities globally as a mark of global competitiveness. Literature reviews by researchers using natural language processing and other machine learning techniques to get students and other stakeholders’ feedback by applying sentiment analysis to improve the quality of teaching and learning in the education sector have yielded the required results. Given the above this research effort aimed at reviewing published papers geared towards improving learners' experiences and enhancing quality assurance in the education system from published articles between 2016 to 2023 in referenced databases from high-impact journals. The research study collated 80 useful articles from the pool of 4595 that were earlier surfed using eligibility criteria which entailed employing exclusion and inclusion characteristics. The outcome of the literature reviews showed that 70% of researchers have used sentiment analysis as feedback and improvement analysis tools in enhancing the student learning experience and quality assurance in the higher education sector. The highly used sentiment analysis algorithms are lexicon-based, Support Vector Machine (SVM), Naïve Bayes(NB), Linear Regression(LR), and Artificial Neural Network(ANN). Keywords: Sentiment Analysis, Education Sector, Supervised Machine Learning Algorithms, social media, Questionnaires, Lexicon-based, Natural Language Processing(NLP) Emughedi Oghu, Emeka Ogbuju, Taiwo Abiodun & Francisca Oladipo.. (2023): Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning. Journal of Advances in Mathematical & Computational Science. Vol. 10, No. 4. Pp 1-20 Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V11N3P1

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“…Public sentiment toward education post-COVID-19 was also studied using Twitter data sector [12] safety was identified as a top concern for students, parents, and educators via sentiment analysis and machine learning (ML) techniques. Social media data was also used to study the attitude of the Jordanian community towards online and in-person hybrid learning [13] in the post-pandemic era. The study found that 40% of the samples displayed negative sentiments, 13% were positive, and 24.5% were neutral.…”

Section: Introductionmentioning

confidence: 99%

Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022

Yan,

Liu

2024

PLoS ONE

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Background As the impact of the COVID-19 pandemic winds down, both individuals and society are gradually returning to life and activities before the pandemic. This study aims to explore how people’s emotions have changed from the pre-pandemic period during the pandemic to this post-emergency period and whether the sentiment level nowadays has returned to the pre-pandemic level. Method We collected Reddit social media data in 2019 (pre-pandemic), 2020 (peak period of the pandemic), 2021, and 2022 (late stages of the pandemic, transitioning period to the post-emergency period) from the subreddits communities in 128 universities/colleges in the U.S., and a set of school-level baseline characteristics such as location, enrollment, graduation rate, selectivity, etc. We predicted two sets of sentiments from a pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) and a graph attention network (GAT) that leverages both the rich semantic information and the relational information among posted messages and then applied model stacking to obtain the final sentiment classification. After obtaining the sentiment label for each message, we employed a generalized linear mixed-effects model to estimate the temporal trend in sentiment from 2019 to 2022 and how the school-level factors may affect the sentiment. Results Compared to the year 2019, the odds of negative sentiment in years 2020, 2021, and 2022 are 25%. 7.3%, and 6.3% higher, respectively, which are all statistically significant at the 5% significance level based on the multiplicity-adjusted p-values. Conclusions Our study findings suggest a partial recovery in the sentiment composition (negative vs. non-negative) in the post-pandemic-emergency era. The results align with common expectations and provide a detailed quantification of how sentiments have evolved from 2019 to 2022 in the sub-population represented by the sample examined in this study.

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Sentiment analysis and emotion detection of post-COVID educational Tweets: Jordan case

Cited by 7 publications

References 43 publications

Self-Attention-Based Bi-LSTM Model for Sentiment Analysis on Tweets about Distance Learning in Higher Education

Self-Attention-Based Bi-LSTM Model for Sentiment Analysis on Tweets about Distance Learning in Higher Education

Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning

Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022

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