Twitter based Sentiment Analysis of Impact of Covid-19 on Education Globaly

Cheeti, Swetha Sree; Li, Yanyan; Hadaegh, Ahmad

doi:10.5121/ijaia.2021.12302

Cited by 15 publications

(8 citation statements)

References 4 publications

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“…Greater and more effective publicity about the role played by educational centers in society has been required (Benjamin, 1993). An analysis recently published (Cheeti, 2021), utilised Twitter sentiments analysis to explore the impact of the COVID-19 pandemic on education. However, the geographical dimension was not considered.…”

Section: Motivation and Objectives Of The Studymentioning

confidence: 99%

Analysing the sentiments about the education system trough Twitter

2023

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This paper applies Information and Communication Technologies (ICT) as well as data analysis to gain a better understanding of the existing perception on the education system. 45,278 tweets were downloaded and processed. Using a lexicon-based approach, examining the most frequently used words, and estimating similarities between terms, we detected that a predominantly negative perception of the education system exists in most of the analysed countries. A positive perception is identified in certain low-income nations. Men exhibit a more positive sentiment than women as well as a higher subjectivity in some countries. The countries that exhibit the most positive perceptions India, Canada, Pakistan, Australia, South Africa and Kenya are also those that manifest the highest subjectivity.

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Section: Motivation and Objectives Of The Studymentioning

confidence: 99%

Analysing the sentiments about the education system trough Twitter

2023

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“…They applied their methodology to Tweets originating from eight countries. Cheeti et al [50] used a Naïve Bayes Classifier to perform sentiment analysis of Tweets focusing on COVID-19 with a specific focus on Tweets related to education and learning. In their study, Ridhwan et al [51] performed sentiment analysis of Tweets about COVID-19 posted between February 1, 2020, to August 31, 2020, with a specific focus on Tweets that originated from Singapore.…”

Section: Recent Work That Focused On Sentiment Analysis Of Tweets Abo...mentioning

confidence: 99%

Analysis of Public Discourse on Twitter involving COVID-19 and MPox: Findings from Sentiment Analysis and Text Analysis

Thakur¹

2023

Preprint

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Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

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“…Sentiment analysis in many languages has been studied recently. NLTK and Naive Bayes Classifier analyzed 90,000 informative COVID-19 tweets for sentiment [29]. In another study to evaluate online learning sentiments, pandemic news and blogs were monitored.…”

Section: Related Workmentioning

confidence: 99%

The Impact of COVID-19 on E-Learning: Context-Based Sentiment Analysis Discourse Using Text Mining

Rehman,

Aslam,

Abid

et al. 2023

VAWKUM trans. comput. sci.

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Finding the most dominant and pertinent user opinions on a certain topic is crucial to the sentiment analysis success factor. During the pandemic lockdowns around the world, the suspension of academic institutions leads to an exceptional increase in distance education. Academic institutions closed their campuses immediately to mitigate the effects of COVID-19 and prevent its pervasive spread, and educational activities were shifted to online platforms. The effectiveness of online education is a significant topic of interest for both students and their parents, especially in terms of how students and teachers perceive it and how technologically viable it is in a range of social circumstances. Before such a wide adoption of e-learning is possible, these issues must be analyzed from multiple perspectives. The present research aims to evaluate the efficacy of e-learning by examining individuals' perceptions of it. Opinions can be found on websites such as Instagram, Facebook, Twitter, etc. As social media has recently emerged as a significant means of communication. This study addresses factors connected to a significant change in the educational system. 200,000 tweets were gathered from Twitter to evaluate the opinions of Twitter users who were taking part in online learning. This study adopts VADER to analyze the subjectivity and polarity score of tweets, a topic model was also created using the LDA algorithm to determine the themes that were talked about on Twitter the most. The models have been constructed and evaluated using Word2Vec to capture the semantic relationships between words and LSTM and RNN sequential model for sentiment analysis. This study measured the efficiency of a sentiment analysis model using the accuracy metric, the conducted experiments reveal that the proposed hybrid model achieves an overall accuracy of 96.3%. The results also indicate a significant negative impact of the Covid-19 pandemic on individuals' emotions, with 64.4% of the analyzed tweets displaying negative sentiments. These findings provide valuable insights into the relationship between global events and individual emotions on social media platforms.

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Twitter based Sentiment Analysis of Impact of Covid-19 on Education Globaly

Cited by 15 publications

References 4 publications

Analysing the sentiments about the education system trough Twitter

Analysing the sentiments about the education system trough Twitter

Analysis of Public Discourse on Twitter involving COVID-19 and MPox: Findings from Sentiment Analysis and Text Analysis

The Impact of COVID-19 on E-Learning: Context-Based Sentiment Analysis Discourse Using Text Mining

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