Social Media Analysis using Sentiment Analysis on COVID-19 from Twitter

The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter. © 2022 IEEE.

Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

CONCEPT OF GAMIFICATION IN ADAPTATION OF SNAKE LADDER ONLINE REPRESENTATION EDUCATION COVID-19

Education can be implemented through various concepts, one of which is the concept of gamification which is then adopted as a learning medium. Gamification to make a change in the use of instructional media, namely of the"conventional" to the more"innovative". This study aims to adopt a concept of gamification into a game of snakes and ladders to support learning about the understanding of Covid-19. The research method with the waterfall model, which is a simple classic model with a linear system flow, where the output of each stage is input for the next stage, this is done by manipulating program scripts and algorithms to provide novelty to the rules of the snakes and ladders game concept. will attract users and provide an impact of understanding created through the pleasure of playing in solving challenges. The learning of snakes and ladders itself is designed in such a way that the board interface changes randomly every time you play until the base board game becomes a digital game that can be applied n offline and online. The game is developed to the stages of the"Online Play" mode, which allows matches to be played from various devices via the internet. This game uses a box system that functions as a"lobby"in the game, so online players can choose to join or host the game and invite other players through the game code.

An Outlook of Rarely Used Feature Functions on Zoom Video Conference Technology in Higher Educations

Common online learning systems have functions with collaborative methods to accomplish all academic activities such as deploying curricula, sharing documents, managing and tracking the results. Since Covid-19 pandemics, the need of a sophisticated learning management system is in high growth to benefit academic communities, such as greater collaboration among teachers and a more consistent measurement of students' progress. Survey was conducted to investigate what and how they operate on some functions at Zoom Video Conference technology. Using statistics to support quantitative data processing, and analysis results triggers some ideas to initiate developing more friendly functions for much greater benefits to gain. © 2020 IEEE.