Education system has been gravely affected due to widespread of Covid-19 across the globe. In this paper
we present a thorough sentiment analysis of tweets related to education available on twitter platform and
deduce conclusions about its impact on people’s emotions as the pandemic advanced over the months.
Through twitter over ninety thousand tweets have been gathered related to the circumstances involving the
change in education system over the world. Using Natural language tool kit (NLTK) functionalities and
Naive Bayes Classifier a sentiment analysis has been performed on the gathered dataset. Based on the
results of this analysis we infer to exhibit the impact of covid-19 on education and how people’s sentiment
altered due to the changes with regard to the education system. Thus, we would like to present a better
understanding of people’s sentiment on education while trying to cope with the pandemic in such
unprecedented times.
Global Pulse: Mining Indonesian Tweets to Understand Food Price Crises copyUN Global Pulse
Sudden increases in the price of staple foodstuffs like rice can push whole families below the poverty line and cause regional economic instability; these changes can happen rapidly but food price statistics are generally published only monthly or even less frequently.
This project, in collaboration with the Indonesian Ministry of Development Planning, UNICEF and WFP in Indonesia seeks to use social media analysis to provide real-time information from the population that could enable faster responses to food price increases in the form of social protection policies. Global Pulse analysed tweet volumes relevant to food and fuel between March 2011 and April 2013 and found a significant correlation, suggesting that even potential (rather than realised) fuel price rises affect people’s perceptions of food security. Researchers also found a relationship between retrospective official food inflation statistics and the number of tweets referencing food price increases.
http://www.unglobalpulse.org/social-media-social-protection-indonesia
Analysing Social Media Conversations to Understand Public Perceptions of Sani...UN Global Pulse
The United Nations Millennium Campaign and the Water Supply and Sanitation Collaborative Council partnered to deliver a comprehensive advocacy and communication drive on sanitation. Their efforts were in support of the UN Deputy Secretary General’s Call to Action on Sanitation to increase the number of people with access to better sanitation. Global Pulse provided an analysis of social media in order to provide insight on the baseline of public engagement, and explore ways to monitor a new sanitation campaign. Using a custom keyword taxonomy, English language tweets from January 2011 to December 2013 were extracted, sorted into categories and analysed.
Cite as: UN Global Pulse, 'Analysing Social Media Conversations to Understand Public Perceptions of Sanitation', Global Pulse Project Series, no.5, 2014.
In this contribution, we develop an accurate and effective event detection method to detect events from a
Twitter stream, which uses visual and textual information to improve the performance of the mining
process. The method monitors a Twitter stream to pick up tweets having texts and images and stores them
into a database. This is followed by applying a mining algorithm to detect an event. The procedure starts
with detecting events based on text only by using the feature of the bag-of-words which is calculated using
the term frequency-inverse document frequency (TF-IDF) method. Then it detects the event based on image
only by using visual features including histogram of oriented gradients (HOG) descriptors, grey-level cooccurrence
matrix (GLCM), and color histogram. K nearest neighbours (Knn) classification is used in the
detection. The final decision of the event detection is made based on the reliabilities of text only detection
and image only detection. The experiment result showed that the proposed method achieved high accuracy
of 0.94, comparing with 0.89 with texts only, and 0.86 with images only.
Event detection in twitter using text and image fusioncsandit
In this paper, we describe an accurate and effective event detection method to detect events from
Twitter stream. It detects events using visual information as well as textual information to improve
the performance of the mining. It monitors Twitter stream to pick up tweets having texts and photos
and stores them into database. Then it applies mining algorithm to detect the event. Firstly, it detects
event based on text only by using the feature of the bag-of-words which is calculated using the term
frequency-inverse document frequency (TF-IDF) method. Secondly, it detects the event based on
image only by using visual features including histogram of oriented gradients (HOG) descriptors,
grey-level co-occurrence matrix (GLCM), and color histogram. K nearest neighbours (Knn)
classification is used in the detection. Finally, the final decision of the event detection is made based
on the reliabilities of text only detection and image only detection. The experiment result showed that
the proposed method achieved high accuracy of 0.93, comparing with 0.89 with texts only, and 0.86
with images only.
This collaborative research-project between Global Pulse (www.unglobalpulse.org) and SAS (www.sas.com) investigates how social media and online user-generated content can be used to enrich the understanding of the changing job conditions in the US and Ireland by analyzing the moods and topics present in unemployment-related conversations from the open social web and relating them to official unemployment statistics. For more information on this project or the other projects in this series, please visit: http://www.unglobalpulse.org/research.
The document provides an overview and user guide for the Open Drug Discovery Teams (ODDT) mobile app. ODDT is a free iOS app that aggregates open science data from various online sources on topics like rare diseases and chemistry. It allows users to browse topics, endorse or disapprove documents, and view molecule structures. The guide describes how to use the app, contribute data through Twitter hashtags, and get involved in rare disease communities through the app. The goal is to facilitate collaboration and data sharing in open science.
Please Retweet #SocialWorkEducation: A Content Analysis of Social Work Progra...Jimmy Young
This study analyzed over 2,600 tweets from social work programs in the US to understand how they use Twitter. It found that most schools joined Twitter in 2012 or later and tweet primarily in the afternoon. Tweets focused on sharing information, building community, and encouraging action. Larger schools with more programs engaged more users and received more likes/retweets. While correlations between engagement and school size were weak, social media can help schools share information and connect with students/alumni if policies and strategies are developed.
Global Pulse: Mining Indonesian Tweets to Understand Food Price Crises copyUN Global Pulse
Sudden increases in the price of staple foodstuffs like rice can push whole families below the poverty line and cause regional economic instability; these changes can happen rapidly but food price statistics are generally published only monthly or even less frequently.
This project, in collaboration with the Indonesian Ministry of Development Planning, UNICEF and WFP in Indonesia seeks to use social media analysis to provide real-time information from the population that could enable faster responses to food price increases in the form of social protection policies. Global Pulse analysed tweet volumes relevant to food and fuel between March 2011 and April 2013 and found a significant correlation, suggesting that even potential (rather than realised) fuel price rises affect people’s perceptions of food security. Researchers also found a relationship between retrospective official food inflation statistics and the number of tweets referencing food price increases.
http://www.unglobalpulse.org/social-media-social-protection-indonesia
Analysing Social Media Conversations to Understand Public Perceptions of Sani...UN Global Pulse
The United Nations Millennium Campaign and the Water Supply and Sanitation Collaborative Council partnered to deliver a comprehensive advocacy and communication drive on sanitation. Their efforts were in support of the UN Deputy Secretary General’s Call to Action on Sanitation to increase the number of people with access to better sanitation. Global Pulse provided an analysis of social media in order to provide insight on the baseline of public engagement, and explore ways to monitor a new sanitation campaign. Using a custom keyword taxonomy, English language tweets from January 2011 to December 2013 were extracted, sorted into categories and analysed.
Cite as: UN Global Pulse, 'Analysing Social Media Conversations to Understand Public Perceptions of Sanitation', Global Pulse Project Series, no.5, 2014.
In this contribution, we develop an accurate and effective event detection method to detect events from a
Twitter stream, which uses visual and textual information to improve the performance of the mining
process. The method monitors a Twitter stream to pick up tweets having texts and images and stores them
into a database. This is followed by applying a mining algorithm to detect an event. The procedure starts
with detecting events based on text only by using the feature of the bag-of-words which is calculated using
the term frequency-inverse document frequency (TF-IDF) method. Then it detects the event based on image
only by using visual features including histogram of oriented gradients (HOG) descriptors, grey-level cooccurrence
matrix (GLCM), and color histogram. K nearest neighbours (Knn) classification is used in the
detection. The final decision of the event detection is made based on the reliabilities of text only detection
and image only detection. The experiment result showed that the proposed method achieved high accuracy
of 0.94, comparing with 0.89 with texts only, and 0.86 with images only.
Event detection in twitter using text and image fusioncsandit
In this paper, we describe an accurate and effective event detection method to detect events from
Twitter stream. It detects events using visual information as well as textual information to improve
the performance of the mining. It monitors Twitter stream to pick up tweets having texts and photos
and stores them into database. Then it applies mining algorithm to detect the event. Firstly, it detects
event based on text only by using the feature of the bag-of-words which is calculated using the term
frequency-inverse document frequency (TF-IDF) method. Secondly, it detects the event based on
image only by using visual features including histogram of oriented gradients (HOG) descriptors,
grey-level co-occurrence matrix (GLCM), and color histogram. K nearest neighbours (Knn)
classification is used in the detection. Finally, the final decision of the event detection is made based
on the reliabilities of text only detection and image only detection. The experiment result showed that
the proposed method achieved high accuracy of 0.93, comparing with 0.89 with texts only, and 0.86
with images only.
This collaborative research-project between Global Pulse (www.unglobalpulse.org) and SAS (www.sas.com) investigates how social media and online user-generated content can be used to enrich the understanding of the changing job conditions in the US and Ireland by analyzing the moods and topics present in unemployment-related conversations from the open social web and relating them to official unemployment statistics. For more information on this project or the other projects in this series, please visit: http://www.unglobalpulse.org/research.
The document provides an overview and user guide for the Open Drug Discovery Teams (ODDT) mobile app. ODDT is a free iOS app that aggregates open science data from various online sources on topics like rare diseases and chemistry. It allows users to browse topics, endorse or disapprove documents, and view molecule structures. The guide describes how to use the app, contribute data through Twitter hashtags, and get involved in rare disease communities through the app. The goal is to facilitate collaboration and data sharing in open science.
Please Retweet #SocialWorkEducation: A Content Analysis of Social Work Progra...Jimmy Young
This study analyzed over 2,600 tweets from social work programs in the US to understand how they use Twitter. It found that most schools joined Twitter in 2012 or later and tweet primarily in the afternoon. Tweets focused on sharing information, building community, and encouraging action. Larger schools with more programs engaged more users and received more likes/retweets. While correlations between engagement and school size were weak, social media can help schools share information and connect with students/alumni if policies and strategies are developed.
Hindsight Imbalance Online and Offline Life: Qualitative Feedback from Online...Dr Poonsri Vate-U-Lan
The document summarizes the proceedings of the International e-Learning Conference 2015, which was held in Bangkok, Thailand on July 20-21, 2015. The conference was organized by the Thailand Cyber University Project to bring together Thai and international educators and researchers to exchange knowledge and experiences in e-learning.
The conference aimed to be a platform for sharing innovative e-learning practices and expanding e-learning knowledge through publications and other means. It covered various topics related to its theme of "Global Trends in Digital Learning," including MOOCs, educational technology, mobile learning, social media in education, emerging technologies, and learning analytics. Events included keynote speeches and paper presentations. Over 800 participants attended the two-day event
PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CH...gerogepatton
Anxiety and depression can have a significant impact on students’ academic performance, however, these
mental health impacts were increased during the Covid-19 pandemic, and accordingly students and
parents need some people to share their feelings together; however, there are different types of social
media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter is one of the
most popular social application that people prefer to share their emotional states. Interestingly, the
psychologist and computer scientists are inspired to study these emotions. In this paper, we propose a
chatbot for detecting the students feeling by using machine-learning algorithms. The authors used a
dataset of tweets from Kaggle’s paltform, and it includes 41157 tweets that are all related to the COVID19. The tweets are classified into categories based on the feeling: Positive and negative. The authors
applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and
accordingly they compared the accuracy between them. In addition to that, the classifiers were evaluated
and compared after changing the test split ratio. The result shows that the accuracy performance of SVM
algorithm is better than Naïve Bayes algorithm, but the speed is extremely slow compared to Naive Bayes
model. In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic
tweets will be included in the future.
PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CH...gerogepatton
Anxiety and depression can have a significant impact on students’ academic performance, however, these
mental health impacts were increased during the Covid-19 pandemic, and accordingly students and
parents need some people to share their feelings together; however, there are different types of social
media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter is one of the
most popular social application that people prefer to share their emotional states. Interestingly, the
psychologist and computer scientists are inspired to study these emotions. In this paper, we propose a
chatbot for detecting the students feeling by using machine-learning algorithms. The authors used a
dataset of tweets from Kaggle’s paltform, and it includes 41157 tweets that are all related to the COVID19. The tweets are classified into categories based on the feeling: Positive and negative. The authors
applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and
accordingly they compared the accuracy between them. In addition to that, the classifiers were evaluated
and compared after changing the test split ratio. The result shows that the accuracy performance of SVM
algorithm is better than Naïve Bayes algorithm, but the speed is extremely slow compared to Naive Bayes
model. In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic
tweets will be included in the future.
This document discusses classifying students' e-learning experiences shared on social media via text mining. It aims to identify problems students face in their learning by analyzing unstructured social media data like tweets, posts and comments. The proposed method introduces a new label "Good Things" to classify positive student experiences alongside existing labels for problems. A naive Bayes multi-label classifier is used to calculate the probability of words in tweets belonging to each label category. The classified tweets with the new label will then be compared to tweets with existing labels to improve understanding of student experiences and enhance e-learning quality.
Classification of Student’s E-Learning Experiences’ in Social Media via Text ...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS ...IJDKP
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
Extent of social media usage by students for improved learning in Tertiary In...iosrjce
The document discusses a study that was conducted to ascertain students' perceptions of using social media for educational purposes. The study surveyed 200 students from three universities in Rivers State, Nigeria. The results found that social media is frequently used by students for educational activities like communication, sharing ideas, and interacting with others. Students generally have a favorable attitude towards using social media. There were also significant differences found in social media usage and attitudes between male and female students, as well as across the three universities. The study recommends incorporating social media into university curriculums to enhance education.
Multimedia has had a significant impact on society. In education, multimedia has improved e-learning by making it more engaging and effective through the use of video, audio, and other mediums. It has also helped students better understand complex and abstract concepts by providing visual representations. For children and teenagers, multimedia can positively impact cognitive development and learning when it delivers age-appropriate content through multiple channels. However, the content and how it is presented must be responsible, as it can strongly influence personality and behavior, especially during teenage years of character building. Overall, multimedia has enhanced communication and learning by utilizing multiple mediums to more effectively convey information.
Effectiveness of New Media as a Tool of Edu-Entertainment among School Childreninventionjournals
In this era of technological revolution and changing patterns of family life, children’s favorite pastime has gone beyond outdoor activities or reading bed time stories. Like any other age group, media and children are dependent mutually for their existence. While a majority of children are found watching Television, surfing internet, playing video games on smart phones or on computer, or watching their favorite cartoon/videos online media, we also have children being seriously considered as their prospective customers. We have an influx of Kids channels on Television, comics, VCD’s/DVD’s and New Media in its various manifestations are available in abundance. Childhood also refers to education and children spend a quality time in schools. Thanks to the concept of globalization, technological revolution has made their presence in many international schools that have mushroomed in many big cities. New Media which has found its niche in all fields has not spared education field also. Bangalore being an IT hub hosts innumerable types of educational franchise catering to the needs of customers. One of the most defining factors as observed in these schools is their extensive usage of new media tools as part of their system. In these schools the children are not only being exposed to new media as an educative medium but also for entertainment purposes, thus providing an impetus for better learning and understanding
Team 1 discussed how a principal in Canada uses Twitter to keep parents informed about school events and share photos. Team 2 described a teacher in New Jersey who tweets classroom activities so parents feel involved. Team 3 explained how a Utah school district uses Twitter to quickly notify followers about emergencies. Team 4 discussed an Illinois elementary school that uses Twitter to teach students skills while sharing classroom news with parents.
With the development in technology, social networking sites are booming day by day which results that students are wasting their precious time scrolling up and down the pages in these social sites. Several types of research have been done to take care of this issue by creating applications for students in which they can post their inquiries and individuals solve them. This method of study demonstrated effectiveness for the understudies. Information was integrated through a narrative approach. This is the primary paper to methodically survey the literature on the utilization of web based media like Twitter in students training. So after doing all these researches we decided to write a review on this topic by adding some points and benefits of our application “Tweedle” . It is an android application build in such a way that a student follows their friends, teachers, scholars, graduates, and post graduates. It is somehow working as Twitter. As we know different people are having different opinions so everyone quotes different examples and different ways to make the students understand. With this sort of examining student will have the option to unmistakably comprehend the issue since he will get the arrangements in different ways Even this is also an interesting way of learning because a student will be eager to know the different answers to his problem. By using this application students use social media by not wasting the time. Pavandeep Kaur | Aliza | Pinky | Nikshep Chatta "Tweedle with Fun: An Educational Tool" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38153.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38153/tweedle-with-fun-an-educational-tool/pavandeep-kaur
Running Head ANNOTATED BIBLIOGRAPHY1ANNOTATED BIBLIOGRAPHY .docxSUBHI7
Running Head: ANNOTATED BIBLIOGRAPHY 1
ANNOTATED BIBLIOGRAPHY 6
Annotated Bibliography
Student’s Name
Institutional Affiliation
Button, D., Harrington, A., & Belan, I. (2014). E-learning & information communication technology (ICT) in nursing education: A review of the literature. Nurse Education Today, 34(10), 1311-1323.
There is need for constant updating of informatics in fields such as nursing so as to help those in the fields to be able to learn and utilize the skills they learn in positive development of children and personal development. This resource is aimed at performing a study spread out across a ten year period identifying the issues which arise and affect both tutors and students in the context of e-learning systems. This study has been significantly the largest change in the field of medicine and nursing education where hospital training was moved to the tertiary sector. Also, the existing differences between computers and systems dealing with informatics depending on their use can prove successful to the users. Technology has also enabled for the creation and use of online data sources and libraries for purposes of referencing and gaining knowledge.
From this reference I will be able to review how technology has been used in developing an online database which can be used by learners and tutors in the field of nursing to gain information.
FernáNdez-LóPez, Á. RodríGuez-FóRtiz, M. J., RodríGuez-Almendros, M. L., & MartíNez-Segura, M. J. (2013). Mobile learning technology based on iOS devices to support students with special education needs. Computers & Education, 61, 77-90.
Recent advancements in technology have assisted in creation of avenues for students with special needs to be able to access equal education opportunities. Mobile applications can be developed for children especially with cognitive disabilities and other difficulties which may arise in the process of learning to help them gain knowledge and education better. These applications can be used as a tool for improvement and betterment of behaviors, helping them interact with the environment and other aspects of holistic growth such as helping in communication. These applications can be designed for Apple iOS devices due to their increased use in the American market however developments can be made to incorporate other platforms. However, there exists a number of applications which have already been implemented and have proven to be successful in helping in positive growth n children.
I intend to use this source to sow how technology can be used to teach children with disabilities and difficulties in learning. Technology can be harnessed and channeled to be used as a tool for education outside the school to help for better growth and development of a child.
Lai, K. W., Khaddage, F., & Knezek, G. (2013). Blending student technology experiences in formal and informal learning. Journal of Computer Assisted Learning, 29(5), 414-425.
There is need for im ...
This document is a thesis submitted by Samuel Ayokunle Adekanmbi in partial fulfillment of a Master of Science degree in computer science from the University of Ibadan in February 2014. It investigates filtering offensive language in online communities using grammatical relations. The thesis acknowledges various individuals who provided support and dedicates the work to promoting a shared vision. It then provides an abstract that overviews developing and implementing a sentence-level semantic filtering system to remove offensive content while maintaining readability. The literature review covers topics like offensive language in online communities, cyberbullying, and using grammatical analysis for semantic filtering.
The document proposes a plan to infuse digital literacy throughout the curriculum at Pontiac Township High School. The goals are to create an interdisciplinary curriculum, integrate digital literacy skills across disciplines, connect with other schools locally and globally, and increase digital literacy to make a positive impact. Key aspects of the plan include assessing students' digital skills, having students take lead roles in collaborative projects that address real-world issues, supporting teachers through resources and training, and documenting projects online to motivate continued involvement.
This study explores the positive and negative impacts of TikTok usage on students at BNHS. It aims to determine how TikTok affects academic performance, mental health, and privacy of BNHS students. The study will utilize a questionnaire to gather data from grade 10 students on their TikTok usage habits and perceptions of effects. Results will provide insight into managing social media usage among youth.
Impact of Social Media of Student’s Academic Performanceinventionjournals
The focus of the study is to determine the effect of growing use of social media sites on the academic performance of the students of universities and colleges. On the basis of random sampling a sample of 300 students was selected. Questionnaire was used as an instrument for data collection.97% questionnaire received back from respondents on which descriptive statistics apply for data analysis. Results indicate that the effect of social media can be positive as in this study closely determined the real effect of social media sites. In recent time itencourage the carrier and future of students’ .The social media sites like Facebook, twitter, Google+ .And Skype capturethe attention of students for study and affecting positively their academic Grade points.
Unit III Research ProposalFollow the directions below for the co.docxmarilucorr
Unit III Research Proposal
Follow the directions below for the completion of the Research Proposal assignment for Unit III. If you have questions, please email your professor for assistance.
Purpose: The purpose of the research proposal is to help you to understand your project, to gain direction and feedback on your project, and to establish a blueprint for your project.
Description: In this assignment, you will create a research proposal consisting of three sections:
Section 1: What is the topic? (100-150 words)
Section 2: What is the controversy? Include paragraphs that detail both sides of the controversy. (300-400 words)
Section 3: Your tentative thesis statement (one to two sentences)
Click here to access the research proposal example.
My tentative argumentative thesis statement is, social media access should be limited or prevented for young children. Giving internet access freely to young children without adult supervision/consent can put themselves and family at risk of internet stalkers, child predators, cyber bullying, and identity theft. As stated, “Parental monitoring of children’s media influences children’s sleeping habits, school routine, social and aggressive behaviors, and that these impacts are reconciled through the amount of time spent watching and contact with media violence. Parental monitoring of media has defensive impact on a wide variety of academic, social, and physical child habits.” Doing my research, I learned that a lot of parents give internet access freely to their child and don’t think about the effects it will have on their developmental skills and health.
Running Head: ANNOTATED BIBLIOGRAPHY
Annotated Bibliography
Should social media access be limited or prevented for young children.
O'Keffe, G. e. (2011). The Impact of Social Media on Children, Adolescents, and Families.
Excessive computer use is keep able of affecting children's social growth. At the age of around seven years, the interaction of a child with family, school, friends, community and media all play a central role in the growth of interpersonal skills and social competence of the child. Computers are now part of that stage of development and alarms have been sounded that children who have too much access to computers create electronic friendships and might be mired in building interpersonal skills. To reduce the high risk of obesity, and other harmful effects of prolonged media exposure, the American Academy of Pediatrics has always advised parents to reduce the time spent children spend on video games, computers and other media to not more than one to two hours a day, and to encourage them to explore different activities like sports, cycling or imaginative play.
David D. Luxton, P. a. (2012). Social Media and Suicide: A Public Health Perspective.
Social media may also pose a threat to vulnerable people through the formation and influence of extreme online groups that promote and provide support for beliefs and behavi ...
The researcher has been asked by a school to conduct a survey examining student well-being. The survey aims to identify the nature of bullying, assess school enjoyment, examine technology use and risky online behaviors, provide information on psychological well-being, and identify any relationship between bullying and academic achievement. Students will complete an online questionnaire and identify safe/unsafe areas on a school map. Consent will be obtained from parents and risks will be minimized by providing support materials and ensuring anonymity through use of identification codes matched to student names, which will be destroyed after data collection and screening for protection issues.
A web-based survey and theoretical research focuses mainly on the hazards that children are exposed to while surfing the digital world. It addresses the problem from parents/caregivers perspective and tries to shed light over the best ways of understanding and precautionary means. It is important for families to take all preventive measures to protect their kids from such hazards.
Hindsight Imbalance Online and Offline Life: Qualitative Feedback from Online...Dr Poonsri Vate-U-Lan
The document summarizes the proceedings of the International e-Learning Conference 2015, which was held in Bangkok, Thailand on July 20-21, 2015. The conference was organized by the Thailand Cyber University Project to bring together Thai and international educators and researchers to exchange knowledge and experiences in e-learning.
The conference aimed to be a platform for sharing innovative e-learning practices and expanding e-learning knowledge through publications and other means. It covered various topics related to its theme of "Global Trends in Digital Learning," including MOOCs, educational technology, mobile learning, social media in education, emerging technologies, and learning analytics. Events included keynote speeches and paper presentations. Over 800 participants attended the two-day event
PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CH...gerogepatton
Anxiety and depression can have a significant impact on students’ academic performance, however, these
mental health impacts were increased during the Covid-19 pandemic, and accordingly students and
parents need some people to share their feelings together; however, there are different types of social
media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter is one of the
most popular social application that people prefer to share their emotional states. Interestingly, the
psychologist and computer scientists are inspired to study these emotions. In this paper, we propose a
chatbot for detecting the students feeling by using machine-learning algorithms. The authors used a
dataset of tweets from Kaggle’s paltform, and it includes 41157 tweets that are all related to the COVID19. The tweets are classified into categories based on the feeling: Positive and negative. The authors
applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and
accordingly they compared the accuracy between them. In addition to that, the classifiers were evaluated
and compared after changing the test split ratio. The result shows that the accuracy performance of SVM
algorithm is better than Naïve Bayes algorithm, but the speed is extremely slow compared to Naive Bayes
model. In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic
tweets will be included in the future.
PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CH...gerogepatton
Anxiety and depression can have a significant impact on students’ academic performance, however, these
mental health impacts were increased during the Covid-19 pandemic, and accordingly students and
parents need some people to share their feelings together; however, there are different types of social
media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter is one of the
most popular social application that people prefer to share their emotional states. Interestingly, the
psychologist and computer scientists are inspired to study these emotions. In this paper, we propose a
chatbot for detecting the students feeling by using machine-learning algorithms. The authors used a
dataset of tweets from Kaggle’s paltform, and it includes 41157 tweets that are all related to the COVID19. The tweets are classified into categories based on the feeling: Positive and negative. The authors
applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and
accordingly they compared the accuracy between them. In addition to that, the classifiers were evaluated
and compared after changing the test split ratio. The result shows that the accuracy performance of SVM
algorithm is better than Naïve Bayes algorithm, but the speed is extremely slow compared to Naive Bayes
model. In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic
tweets will be included in the future.
This document discusses classifying students' e-learning experiences shared on social media via text mining. It aims to identify problems students face in their learning by analyzing unstructured social media data like tweets, posts and comments. The proposed method introduces a new label "Good Things" to classify positive student experiences alongside existing labels for problems. A naive Bayes multi-label classifier is used to calculate the probability of words in tweets belonging to each label category. The classified tweets with the new label will then be compared to tweets with existing labels to improve understanding of student experiences and enhance e-learning quality.
Classification of Student’s E-Learning Experiences’ in Social Media via Text ...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS ...IJDKP
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
Extent of social media usage by students for improved learning in Tertiary In...iosrjce
The document discusses a study that was conducted to ascertain students' perceptions of using social media for educational purposes. The study surveyed 200 students from three universities in Rivers State, Nigeria. The results found that social media is frequently used by students for educational activities like communication, sharing ideas, and interacting with others. Students generally have a favorable attitude towards using social media. There were also significant differences found in social media usage and attitudes between male and female students, as well as across the three universities. The study recommends incorporating social media into university curriculums to enhance education.
Multimedia has had a significant impact on society. In education, multimedia has improved e-learning by making it more engaging and effective through the use of video, audio, and other mediums. It has also helped students better understand complex and abstract concepts by providing visual representations. For children and teenagers, multimedia can positively impact cognitive development and learning when it delivers age-appropriate content through multiple channels. However, the content and how it is presented must be responsible, as it can strongly influence personality and behavior, especially during teenage years of character building. Overall, multimedia has enhanced communication and learning by utilizing multiple mediums to more effectively convey information.
Effectiveness of New Media as a Tool of Edu-Entertainment among School Childreninventionjournals
In this era of technological revolution and changing patterns of family life, children’s favorite pastime has gone beyond outdoor activities or reading bed time stories. Like any other age group, media and children are dependent mutually for their existence. While a majority of children are found watching Television, surfing internet, playing video games on smart phones or on computer, or watching their favorite cartoon/videos online media, we also have children being seriously considered as their prospective customers. We have an influx of Kids channels on Television, comics, VCD’s/DVD’s and New Media in its various manifestations are available in abundance. Childhood also refers to education and children spend a quality time in schools. Thanks to the concept of globalization, technological revolution has made their presence in many international schools that have mushroomed in many big cities. New Media which has found its niche in all fields has not spared education field also. Bangalore being an IT hub hosts innumerable types of educational franchise catering to the needs of customers. One of the most defining factors as observed in these schools is their extensive usage of new media tools as part of their system. In these schools the children are not only being exposed to new media as an educative medium but also for entertainment purposes, thus providing an impetus for better learning and understanding
Team 1 discussed how a principal in Canada uses Twitter to keep parents informed about school events and share photos. Team 2 described a teacher in New Jersey who tweets classroom activities so parents feel involved. Team 3 explained how a Utah school district uses Twitter to quickly notify followers about emergencies. Team 4 discussed an Illinois elementary school that uses Twitter to teach students skills while sharing classroom news with parents.
With the development in technology, social networking sites are booming day by day which results that students are wasting their precious time scrolling up and down the pages in these social sites. Several types of research have been done to take care of this issue by creating applications for students in which they can post their inquiries and individuals solve them. This method of study demonstrated effectiveness for the understudies. Information was integrated through a narrative approach. This is the primary paper to methodically survey the literature on the utilization of web based media like Twitter in students training. So after doing all these researches we decided to write a review on this topic by adding some points and benefits of our application “Tweedle” . It is an android application build in such a way that a student follows their friends, teachers, scholars, graduates, and post graduates. It is somehow working as Twitter. As we know different people are having different opinions so everyone quotes different examples and different ways to make the students understand. With this sort of examining student will have the option to unmistakably comprehend the issue since he will get the arrangements in different ways Even this is also an interesting way of learning because a student will be eager to know the different answers to his problem. By using this application students use social media by not wasting the time. Pavandeep Kaur | Aliza | Pinky | Nikshep Chatta "Tweedle with Fun: An Educational Tool" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38153.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38153/tweedle-with-fun-an-educational-tool/pavandeep-kaur
Running Head ANNOTATED BIBLIOGRAPHY1ANNOTATED BIBLIOGRAPHY .docxSUBHI7
Running Head: ANNOTATED BIBLIOGRAPHY 1
ANNOTATED BIBLIOGRAPHY 6
Annotated Bibliography
Student’s Name
Institutional Affiliation
Button, D., Harrington, A., & Belan, I. (2014). E-learning & information communication technology (ICT) in nursing education: A review of the literature. Nurse Education Today, 34(10), 1311-1323.
There is need for constant updating of informatics in fields such as nursing so as to help those in the fields to be able to learn and utilize the skills they learn in positive development of children and personal development. This resource is aimed at performing a study spread out across a ten year period identifying the issues which arise and affect both tutors and students in the context of e-learning systems. This study has been significantly the largest change in the field of medicine and nursing education where hospital training was moved to the tertiary sector. Also, the existing differences between computers and systems dealing with informatics depending on their use can prove successful to the users. Technology has also enabled for the creation and use of online data sources and libraries for purposes of referencing and gaining knowledge.
From this reference I will be able to review how technology has been used in developing an online database which can be used by learners and tutors in the field of nursing to gain information.
FernáNdez-LóPez, Á. RodríGuez-FóRtiz, M. J., RodríGuez-Almendros, M. L., & MartíNez-Segura, M. J. (2013). Mobile learning technology based on iOS devices to support students with special education needs. Computers & Education, 61, 77-90.
Recent advancements in technology have assisted in creation of avenues for students with special needs to be able to access equal education opportunities. Mobile applications can be developed for children especially with cognitive disabilities and other difficulties which may arise in the process of learning to help them gain knowledge and education better. These applications can be used as a tool for improvement and betterment of behaviors, helping them interact with the environment and other aspects of holistic growth such as helping in communication. These applications can be designed for Apple iOS devices due to their increased use in the American market however developments can be made to incorporate other platforms. However, there exists a number of applications which have already been implemented and have proven to be successful in helping in positive growth n children.
I intend to use this source to sow how technology can be used to teach children with disabilities and difficulties in learning. Technology can be harnessed and channeled to be used as a tool for education outside the school to help for better growth and development of a child.
Lai, K. W., Khaddage, F., & Knezek, G. (2013). Blending student technology experiences in formal and informal learning. Journal of Computer Assisted Learning, 29(5), 414-425.
There is need for im ...
This document is a thesis submitted by Samuel Ayokunle Adekanmbi in partial fulfillment of a Master of Science degree in computer science from the University of Ibadan in February 2014. It investigates filtering offensive language in online communities using grammatical relations. The thesis acknowledges various individuals who provided support and dedicates the work to promoting a shared vision. It then provides an abstract that overviews developing and implementing a sentence-level semantic filtering system to remove offensive content while maintaining readability. The literature review covers topics like offensive language in online communities, cyberbullying, and using grammatical analysis for semantic filtering.
The document proposes a plan to infuse digital literacy throughout the curriculum at Pontiac Township High School. The goals are to create an interdisciplinary curriculum, integrate digital literacy skills across disciplines, connect with other schools locally and globally, and increase digital literacy to make a positive impact. Key aspects of the plan include assessing students' digital skills, having students take lead roles in collaborative projects that address real-world issues, supporting teachers through resources and training, and documenting projects online to motivate continued involvement.
This study explores the positive and negative impacts of TikTok usage on students at BNHS. It aims to determine how TikTok affects academic performance, mental health, and privacy of BNHS students. The study will utilize a questionnaire to gather data from grade 10 students on their TikTok usage habits and perceptions of effects. Results will provide insight into managing social media usage among youth.
Impact of Social Media of Student’s Academic Performanceinventionjournals
The focus of the study is to determine the effect of growing use of social media sites on the academic performance of the students of universities and colleges. On the basis of random sampling a sample of 300 students was selected. Questionnaire was used as an instrument for data collection.97% questionnaire received back from respondents on which descriptive statistics apply for data analysis. Results indicate that the effect of social media can be positive as in this study closely determined the real effect of social media sites. In recent time itencourage the carrier and future of students’ .The social media sites like Facebook, twitter, Google+ .And Skype capturethe attention of students for study and affecting positively their academic Grade points.
Unit III Research ProposalFollow the directions below for the co.docxmarilucorr
Unit III Research Proposal
Follow the directions below for the completion of the Research Proposal assignment for Unit III. If you have questions, please email your professor for assistance.
Purpose: The purpose of the research proposal is to help you to understand your project, to gain direction and feedback on your project, and to establish a blueprint for your project.
Description: In this assignment, you will create a research proposal consisting of three sections:
Section 1: What is the topic? (100-150 words)
Section 2: What is the controversy? Include paragraphs that detail both sides of the controversy. (300-400 words)
Section 3: Your tentative thesis statement (one to two sentences)
Click here to access the research proposal example.
My tentative argumentative thesis statement is, social media access should be limited or prevented for young children. Giving internet access freely to young children without adult supervision/consent can put themselves and family at risk of internet stalkers, child predators, cyber bullying, and identity theft. As stated, “Parental monitoring of children’s media influences children’s sleeping habits, school routine, social and aggressive behaviors, and that these impacts are reconciled through the amount of time spent watching and contact with media violence. Parental monitoring of media has defensive impact on a wide variety of academic, social, and physical child habits.” Doing my research, I learned that a lot of parents give internet access freely to their child and don’t think about the effects it will have on their developmental skills and health.
Running Head: ANNOTATED BIBLIOGRAPHY
Annotated Bibliography
Should social media access be limited or prevented for young children.
O'Keffe, G. e. (2011). The Impact of Social Media on Children, Adolescents, and Families.
Excessive computer use is keep able of affecting children's social growth. At the age of around seven years, the interaction of a child with family, school, friends, community and media all play a central role in the growth of interpersonal skills and social competence of the child. Computers are now part of that stage of development and alarms have been sounded that children who have too much access to computers create electronic friendships and might be mired in building interpersonal skills. To reduce the high risk of obesity, and other harmful effects of prolonged media exposure, the American Academy of Pediatrics has always advised parents to reduce the time spent children spend on video games, computers and other media to not more than one to two hours a day, and to encourage them to explore different activities like sports, cycling or imaginative play.
David D. Luxton, P. a. (2012). Social Media and Suicide: A Public Health Perspective.
Social media may also pose a threat to vulnerable people through the formation and influence of extreme online groups that promote and provide support for beliefs and behavi ...
The researcher has been asked by a school to conduct a survey examining student well-being. The survey aims to identify the nature of bullying, assess school enjoyment, examine technology use and risky online behaviors, provide information on psychological well-being, and identify any relationship between bullying and academic achievement. Students will complete an online questionnaire and identify safe/unsafe areas on a school map. Consent will be obtained from parents and risks will be minimized by providing support materials and ensuring anonymity through use of identification codes matched to student names, which will be destroyed after data collection and screening for protection issues.
A web-based survey and theoretical research focuses mainly on the hazards that children are exposed to while surfing the digital world. It addresses the problem from parents/caregivers perspective and tries to shed light over the best ways of understanding and precautionary means. It is important for families to take all preventive measures to protect their kids from such hazards.
Similar to TWITTER BASED SENTIMENT ANALYSIS OF IMPACT OF COVID-19 ON EDUCATION GLOBALY (20)
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
2. Operations Strategy in a Global Environment.ppt
TWITTER BASED SENTIMENT ANALYSIS OF IMPACT OF COVID-19 ON EDUCATION GLOBALY
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.3, May 2021
DOI: 10.5121/ijaia.2021.12302 15
TWITTER BASED SENTIMENT ANALYSIS OF
IMPACT OF COVID-19 ON EDUCATION GLOBALY
Swetha Sree Cheeti, Yanyan Li and Ahmad Hadaegh
California State University-San Marcos, San Marcos, California, USA
ABSTRACT
Education system has been gravely affected due to widespread of Covid-19 across the globe. In this paper
we present a thorough sentiment analysis of tweets related to education available on twitter platform and
deduce conclusions about its impact on people’s emotions as the pandemic advanced over the months.
Through twitter over ninety thousand tweets have been gathered related to the circumstances involving the
change in education system over the world. Using Natural language tool kit (NLTK) functionalities and
Naive Bayes Classifier a sentiment analysis has been performed on the gathered dataset. Based on the
results of this analysis we infer to exhibit the impact of covid-19 on education and how people’s sentiment
altered due to the changes with regard to the education system. Thus, we would like to present a better
understanding of people’s sentiment on education while trying to cope with the pandemic in such
unprecedented times.
KEYWORDS
Sentiment Analysis, Education, Covid-19, Tweets, Naïve Bayes Classifier.
1. INTRODUCTION
SARS-CoV-2 virus commonly known as Covid-19 has hit the entire world like never known
before and became a global threat. Education is the most basic and an important tool to improve
one’s life. As the world health organization (WHO) has declared Covid-19 as a pandemic, it
resulted in near total closure of schools, universities and colleges worldwide. These closures have
affected not only students, instructors, staff and, their families but also have a far reaching
economic and social consequences [1]. In such a distinctive time, as the world tries to heal and
cope, almost every educational organization has moved the education process to remote
functioning and virtual learning. Through online classes, using technology students and teachers
have been trying to continue the education through various practices as zoom meetings, sharing
online materials, recording lectures, power point tutorials and so on. Recent study says that about
1.52 billion learners are affected due to closure of schools, colleges and universities which has
shed light on many broader socio-economic issues like student debt, food insecurity, academic
integrity, homelessness, childcare, healthcare, housing, internet, digital learning and disability
services [1].
In this paper, we attempt to shed light on people’s sentiment about education during the global
rise of Covid-19. To do this, we use the data from the most extensively used social media
platform twitter. Data from social media has been used even in the past to monitor public
sentiments and communication during health emergencies [2]. Tweets related to education are
collected from twitter and then analyzed using a custom framework to predict the sentiment of
each tweet which would give insight about the tweeter sentiment. Based on these results we
determine to present a better supportive of people’s sentiment on education during the pandemic and
through this analysis we could observe and differentiate the level of positive and negative
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.3, May 2021
16
implications due to the shift in the education system. Observing which educators and institutions
can come up with better efficient ways which might be helpful to smoothen the process of
learning. We prospect that with the continuing growth of virtual education monitoring people’s
sentiment using our enhanced model could make a difference as there is always a scope for betterment
to provide the best quality of education in such overwhelming times.
2. BACKGROUND
Remote learning has become the new education lifeline and this primary change has an impact on
everyone but has more severe consequences for students and their families. To have a successful
virtual learning experience, a student should be able to access to a computer and reliable internet
which is not the case for every student as some are at a disadvantage of having none. Working
parents are at greater disadvantage of missing work hours in order to take care of children at
home which indirectly would result in wage loss. Many children across the world rely on schools
for free and discounted meals for such children nutrition is especially compromised [1]. Through
all these changes students are expected to pay full tuition fee to institutions which is unfair.
Learning outcomes cannot be the same with virtual learning as many organizations have to
postpone or cancel exams as going ahead with it would lead to violation of social distancing [3].
With many such drawbacks and disadvantages student learning, development and growth are at
stake.
Social media is an imperative part of almost every individual’s life and is widely used tool for
interactions among individuals in which they create, share, and exchange information, ideas and
views on topics. Among the social media platforms used today, Twitter is the broadly utilized
social media platform and recent study suggests that over 500 million tweets are tweeted each
day on twitter. We base our research on tweets collected through such extensively used platform
and hope to acquire an insight of people’s emotions on changes in education as most people do
express their opinions and discuss ongoing issues using social media these days. The data
collected through this platform would be useful to analyze, experiment and deduce results to
support our research.
3. RELATED WORK
Many studies have been conducted earlier using twitter data to predict the sentiment of people in
different expanses but to the best of our knowledge there hasn’t been a study on people
sentiment on education during the pandemic. There are distinctive ways proposed by several
researchers to evaluate the sentiments of people using social media. Many of those ways include
machine learning algorithms like Naïve Bayes, Max Entropy, Support Vector Machine and so on.
Using multinomial Naïve Bayes method with a training set of tweets that contains only
emoticons, Pak and Paroubek [4] presented a model that classifies tweets as positive or negative.
But since the training set knowledge is only based on tweets with emoticons, this approach was
less effective. Go and L. Huang [5] used both Naïve Bayes and SVM models to analyze twitter
data using distant supervision with emoticons in their training data to predict sentiment and
determined that SVM performed better. In Agarwal et al. [6] using a tree kernel model tweets are
represented as trees and are classified into positive, negative and neutral based on polarity of
words with their Part of Speech (POS) tags. A study on sentiment analysis of tweets during covid
in Nepal [7] has used python TextBlob library and classified tweets as positive or negative.
There are many different studies conducted to determine the sentiment of tweets, but we propose
an enhanced approach which doesn’t only rely on emoticons or POS of the tweets to classify a
3. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.3, May 2021
17
tweet as positive or negative sentiment which is explained in detail in the next section. Further
the accuracy attained through our framework also defines the effective performance of our model.
4. METHODOLOGY
The process to study the impact of covid-19 on education through sentiment analysis of twitter
data involves fetching tweets related to education from twitter and then applying Natural
language tool kit (NLTK) functionalities to clean the data and split the data into tokens for
accurate assessment. Later this modified data is passed through a designed framework for
analysis and is trained using Naive Bayes classifier to study each tweet and predict the sentiment
of the tweeter. A detailed explanation of steps involved in this research is given in the following
sections.
Figure 1. Shows the steps involved in the methodology
4.1. Fetching Twitter Data
The primary step is to gather the required data through extensively used social media platform
Twitter. From mid-March almost every educational organization moved to remote functioning.
So, based on that change, we gathered tweets from the months of March to July of the year 2020
for this study. To gather the data of this timeline, we used a customized get old tweets (GOT3)
API from GitHub repository which uses URLlib to retrieve tweets from the twitter search engine
as twitter API doesn’t give free access to its data which is older than a week. We used certain
specific keywords to query the twitter dataset which are observed as being generally used by
people in their tweets to express their views about education. The keywords used are Online-
Classes, Virtual-Learning, Remote-Learning, Zoom-Classes and Distance-Learning. To avoid
fetching tweets with text, which is not in English language, we have used Google’s language
detection library in Python named as langdetect and specified the preferred language as English
[8]. Through the months of March to July, we have collected tweets from non-consecutive
randomly chosen six dates of each month so that we could certainly identify the difference in
public sentiments through each month from time to time. For every six days of a month, we have
collected about minimum of 15 thousand tweets. Thus, we have a dataset of 91 thousand tweets
from the months of March 2020 to July 2020. Having such a large dataset would help us gain
better analysis.
4.2. Pre-Processing the Data
The data gathered in the above step contains raw tweets from twitter in which tweeters use
different symbols, images, emoticons and links attached to their text message to express and
support their context. Normally these raw tweets would have user handles, URL links, image
Fetching
Twitter Data
• Collecting
Tweets
related to
education
from Twitter
platform
during Covid-
19 pandemic
Pre-
Processing
Data
• Data Cleaning
using NLTK
library
functionalities
• Data Filtering
and
Tokenization
Training
• Using Naïve
Bayes
Classifier
data is
trained to do
sentiment
analysis on
gathered
dataset
Testing
• Using 80%
data as
training set
and 20% data
as testing set
accuracy of
the model is
predicted
4. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.3, May 2021
18
links, punctuation marks, alpha numerals, numbers, emojis and other unnecessary symbols
included with the text message [9]. Since tweets with such unnecessary links and symbols could
bother the analysis procedure and affect the accuracy of the model, the data should be thoroughly
cleaned. Using the “re” library of python we have performed data cleaning. After cleaning the
data utilizing the functionalities of NLTK library data filtering and tokenization is done. Data
filtering process involves removal of stop words such as prepositions and conjunctions from a
tweet and tokenization is a process of splitting a tweet into tokens i.e., individual words [10].
Hence, raw tweets gathered are refined and each tweet is transformed into tokens which could be
further passed for training procedure.
Table 1: Shows pre-processing of data with sample tweets
Raw tweets Clean tweets Tokenized tweets
Yes indeed. A4: Moreover,
we should be in contact with
our students' parents to
supply them with the
necessary tools for remote
learning like comfortable
atmosphere, powerful access
to the internet, sufficient
devices. #RemoteLearning
#MSFTEduChat
#MicrosoftEDU
#TweetMeetEN https://
twitter.com/AbhilashaTochi
/status/125265743464199782
6 xe2x80xa6"""
Yes indeed Moreover we should be in
contact with our students parents to supply
them with the necessary tools for remote
learning like comfortable atmosphere
powerful access to the internet sufficient
devices
RemoteLearningMSFTEduChatMicrosoftED
UTweetMeetEN
'indeed', 'Moreover',
'contact', 'students',
'parents', 'supply',
'necessary', 'tools',
'remote', 'learning',
'like', 'comfortable',
'atmosphere',
'powerful', 'access',
'internet', 'sufficient',
'devices',
'RemoteLearning',
'MSFTEduChat',
'MicrosoftEDU',
'TweetMeetEN'
So important to accept that
this is a difficult time for all
and very difficult for some.
As such keep things simple
and make sure expectations
are reasonable; This requires
discussing with students.
#MicrosoftEdu #MIEExpert
#RemoteLearning
#TweetMeetEn https://
twitter.com/robdunlopEDU/s
tatus/1252656849377046528
xe2x80xa6'
So important to accept that this is a difficult
time for all and very difficult for some As
such keep things simple and make sure
expectations are reasonable This requires
discussing with students MicrosoftEdu
MIEExpert
RemoteLearning
TweetMeetEn
'important', 'accept',
'difficult', 'time',
'difficult', 'keep',
'things', 'simple',
'make', 'sure',
'expectations',
'reasonable',
'requires',
'discussing',
'students',
'MicrosoftEdu',
'MIEExpert',
'RemoteLearning',
'TweetMeetEn'
To me this is what
#remotelearning has felt like!
Students plug in and out
where needed and suited their
needs. Redefining to me what
engagement looks, feels and
sounds like. Essentially this is
what my practice should have
looked like prior to
#COVID19nz I guess #nzhpe
pic.twitter.com/R81RTkTUoz
'"
To me this is what remotelearning has felt like
Students plug in and out where needed and
suited their needs Redefining to me what
engagement looks feels and sounds like
Essentially this is what my practice should
have looked like prior COVIDnz I guess
nzhpe
‘remotelearning',
'felt', 'like', 'Students',
'plug', 'needed',
'suited', 'needs',
'Redefining',
'engagement', 'looks',
'feels', 'sounds', 'like',
'Essentially',
'practice', 'looked',
'like', 'prior',
'COVIDnz', 'guess',
'nzhpe'
5. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.3, May 2021
19
4.3. Training
In our model to classify a tweet as positive or negative or as neutral we use a training dataset. A
huge set of generic positive words and negative words files have been created. Using a bag of
words function [11] we convert both the positive and negative word files into word dictionary
and label each word of a positive word dictionary as positive and each word of a negative word
dictionary as negative. Both the labelled positive and negative word dictionaries combined would
serve as training data for our model.
Next, we use the generated training data with the Naïve Bayes classifier [12] in our model to
generate a classifier. The tokenized tweets would be passed through this classifier and each token
of tweet would be compared with training data and each token of tweet would be identified as
positive or negative token. Then based on the possibility of positive or negative tokens in a tweet
each tweet would have a positive probability and negative probability. Hence, a tweet with higher
positive probability would be labelled as a positive tweet and a tweet with higher negative
probability would be labelled as a negative tweet. For a tweet to be labelled as neutral tweet we
have specified a neutral range of probability i.e. from 0.4 to 0.6. When a tweet that has positive
and negative probability of that specified range (0.4 – 0.6) is found then the classifier would label
that tweet as neutral.
Through this combination of word based training and Naïve Bayes classifier, we acquire the
sentiment of each tweet amongst the gathered data. Thus, all the tweets are now labelled as either
positive, negative or neutral.
4.4. Testing
The accuracy of this model depends on the positive and negative word files used as they are used
to train the classifier and predict the tweet sentiment. Thus, the higher the number of words in the
positive and negative word files, the better the training data and it would result in accurate
labelling of tweets. To test the accuracy of the model described above to predict the sentiment of
tweets, we split the labelled tweets into two data sets. 80% of the labelled tweets are used as
training data and 20% of the labelled tweets are used as testing data. Applying the training and
testing data sets to the multinomial Naïve Bayes classifier, we have an accuracy of 83.5% for our
model.
5. RESULTS AND ANALYSIS
The research of performing sentiment analysis on twitter dataset of a total 91,701 tweets related
to education during the Covid-19 phase from the months of March to July of the year 2020 has
given an analysis result as follows
Table2: Shows percentage of the positive, negative and neutral tweets after
performing sentiment analysis on the gathered dataset.
Sentiment of tweet Number of tweets Percentage
Positive tweets 42,161 45.97
Negative tweets 49,507 53.98
Neutral tweets 33 0.03
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Figure 2: Bar graph representation of the classified tweets
Thus, the above analysis of dataset clearly indicates that the tweets collected include a very
minimal number of neutral range tweets and the percentage of negative tweets is clearly higher
than the positive tweets collected. To analyze the variation of tweets sentiments through each
month, we have used matplotlib library of python to show a graphical visualization of positive,
negative and neutral tweets collected per each of the six days from the months of March to July
of the year 2020 as shown below
Figure 3: March 2020 tweets analysis
Figure 4: April 2020 tweets analysis
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Figure 5: May 2020 tweets analysis
Figure 6: June 2020 tweets analysis
Figure 7: July 2020 tweets analysis
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From the graphs shown above, (Figures 3 – 7), x-axis shows the dates on which tweets were
collected and y-axis shows the number of tweets. Green bar in the picture indicates positive
tweets and red bar indicates negative tweets collected on that day of each month. As the analysis
of tweets progressed from the months of March to July, we could clearly see that there is a visible
growth in negative tweets bar per day in comparison to the positive tweets bar. Neutral tweets bar
isn’t visible as they are very minimal but the space between bars representing positive and
negative tweets indicate that each day there has been at least few neutral tweets tweeted.
Figure 8: Shows the frequency distribution graph of top 25 most commonly used words in the dataset
Figure 9: Shows Wordcloud of most commonly used words in the dataset
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Using features of word cloud library in python [13], we have plotted the frequency of the
occurrence of most commonly used words in the gathered tweet dataset (Figure 8) and using
word cloud picturization, the words that occurred most commonly in the tweets, are shown in
Figure 9.
6. CONCLUSION
In this paper we have given an overview of the educational situation globally and effect of its
shift to virtual learning and remote functioning during the Covid-19 pandemic. To study the
effect of these dire changes in the education system and people’s sentiment towards it, we have
gathered related tweets and used a combination of word-based training and Naïve Bayes model
on these tweets, we performed a sentiment analysis. Through this model, we have acquired
sentiment of each tweet which reflects tweeter sentiment.
From the study results, we have seen that the number of tweets with negative sentiment is higher
than the tweets with positive sentiment. Even though it seems safe to stay home and continue the
education, there are clearly several other concerns being battled on daily basis to keep up with
these changes in education system. There are many inconveniencies such as internet and tech
issues, financial situation, and quality of education which have steered people to have negative
sentiment towards the remote learning education being practiced now.
Further, we could implement this model and study people’s sentiment across different countries
independently and compare the difference in sentiments of the people about education. This
could provide a perspective on this issue based on the geographical dimension. Also, we could try
and include tweets in other languages as well for training and improve our model as a
multilingual sentiment classifier.
REFERENCES
[1] “Impact of Covid-19 pandemic on education”, from Wikipedia, the free encyclopedia. 2021.
[2] L. MO, L. J, Sheldenkar, S. PJ, S. W, Gupta R, and Y. Yang. Global Sentiments Surrounding the
COVID-19 Pandemic on Twitter: Analysis of Twitter Trends. JMIR Public Health Surveillance.
2020.
[3] S. Burges and H. Henrik Sievertsen, “Schools, skills, and learning: The impact of COVID-19 on
education”VoxEU & CEPR. 2020.
[4] A. Pak and P. Paroubek. “Twitter as a Corpus for Sentiment Analysis and Opinion Mining". In
Proceedings of the Seventh Conference on International Language Resources and Evaluation,
pp.1320-1326. 2020.
[5] R. Go, B. Huang. “Twitter Sentiment Classification Using Distant Supervision" Stanford University,
Technical Paper. 2009.
[6] A. Xie, I. Vovsha, O. Rambow, and R. Passonneau, “Sentiment Analysis of Twitter Data", In
Proceedings of the ACL 2011Workshop on Languages in social media, pp. 30-38. 2011
[7] B. Pokharel, “Twitter Sentiment analysis during COVID-19 Outbreak in Nepal”,SSRN Electronic
Journal. January 2020.
[8] Jenny Lee, “Benchmarking Language Detection for NLP” Towards Data Science, 2020.
[9] A. Brahmananda Reddy, D.N. Vasundhara, P. Subhash, “Sentiment Research on Twitter Data”
International Journal of recent Technology and Engineering (IJRTE). September 2019.
[10] Martin Pellarolo, “Naïve Bayes for Sentiment Analysis” Medium 2018.
[11] Yin Zhang, Rong Jin and Zhou, “Understanding bag-of-words model: a stastical framework”
International Journal of Machine Learning and Cybernetics volume 1, 2010.
[12] Phyu Thwee, Yi Yi Aung, Cho Cho Lwin, “Naïve Bayes Classifier for sentiment analysis”
International Journal of Creative and Innovative Research in All studies. January 2021.
[13] Florian Heimerl, Steffen Lohmann, Thomas Ertl “Word Cloud explorer: Text analytics based on word
cloud” IEEE, 2014.
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APPENDIX
In this appendix we provide the links of libraries that we used.
A1. https://docs.python.org/3/library/
A2. https://github.com/Jefferson-Henrique/GetOldTweets-python
ACKNOWLEDGEMENTS
I would like to thank my advisors Dr. Ahmad Hadaegh and Dr. Yanyan Li who advised me and
provided valuable comments in this work. I also like to thank my friends and my family for their
support and encouragement during this time.
AUTHORS
Swetha Sree Cheeti was born in September 1994 in India. She completed her bachelor’s in
Computer Science from Jawaharlal Nehru Technological University (JNTU), Hyderabad,
INDIA in 2016. She then moved to US in 2019 to pursue her Master of Science in
computer science at California State University at San Marcos, California. She recently
earned her graduation in Spring 2021 from CSUSM.
Yanyan Li received his Ph.D. degree from the University of Arkansas, Little Rock, in
2018. He is currently an Assistant Professor in the Department of Computer Science and
Information Systems at California State University San Marcos. His research interests are
in the areas of cybersecurity, machine learning, and mobile computing.
Dr. Hadaegh was born in Shiraz Iran. He moved to Canada in July 1983 and did his
undergraduate work in computer science at University of Lethbridge in Alberta Canada. He
moved to Winnipeg, Canada in 1988 to do his master and PhD at University of Manitoba in
Computer Science. Dr. Hadaegh was hired by California State University San Marcos
(CSUSM) in Fall 2002 and he has been working at CSUSM since then. His expertise is in
Databases and Data Mining.