scholarly journals Examining Public Opinion Regarding Online Learning during Covid19 Outbreak: Sentiment Analysis

2021 ◽  
Author(s):  
Cansu AYDIN
Keyword(s):  
Online Learning ◽  
Public Opinion ◽  
Sentiment Analysis

scholarly journals Public Opinions about Online Learning during COVID-19: A Sentiment Analysis Approach

2021 ◽  
Vol 13 (6) ◽  
pp. 3346
Author(s):  
Kaushal Kumar Bhagat ◽  
Sanjaya Mishra ◽  
Alakh Dixit ◽  
Chun-Yen Chang
Keyword(s):  
Online Learning ◽  
Public Opinion ◽  
World Health Organization ◽  
Sentiment Analysis ◽  
Online News ◽  
World Health ◽  
Analysis Approach ◽  
Web Scraping ◽  
The World ◽  
Health Organization

The aim of this study was to analyze public opinion about online learning during the COVID-19 (Coronavirus Disease 2019) pandemic. A total of 154 articles from online news and blogging websites related to online learning were extracted from Google and DuckDuckGo. The articles were extracted for 45 days, starting from the day the World Health Organization (WHO) declared COVID-19 a worldwide pandemic, 11 March 2020. For this research, we applied the dictionary-based approach of the lexicon-based method to perform sentiment analysis on the articles extracted through web scraping. We calculated the polarity and subjectivity scores of the extracted article using the TextBlob library. The results showed that over 90% of the articles are positive, and the remaining were mildly negative. In general, the blogs were more positive than the newspaper articles; however, the blogs were more opinionated compared to the news articles.

Covid-19 Vaccine Tweets Sentiment Analysis and Topic Modelling for Public Opinion Mining

2021 ◽  
Author(s):  
Trisha Baldha ◽  
Malvi Mungalpara ◽  
Priyanka Goradia ◽  
Santosh Bharti
Keyword(s):  
Public Opinion ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Topic Modelling

scholarly journals Topic Sentiment Analysis in Online Learning Community from College Students

2020 ◽  
Vol 5 (2) ◽  
pp. 33-61
Author(s):  
Kai Wang ◽  
Yu Zhang
Keyword(s):  
Online Learning ◽  
Sentiment Analysis ◽  
Learning Community ◽  
Teaching Practice ◽  
Topic Detection ◽  
Online Learning Community ◽  
Hybrid Matrix ◽  
Content Recommendation ◽  
Topic Sentiment

AbstractPurposeOpinion mining and sentiment analysis in Online Learning Community can truly reflect the students’ learning situation, which provides the necessary theoretical basis for following revision of teaching plans. To improve the accuracy of topic-sentiment analysis, a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models.Methodology/approachWe aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularity-levels. The proposed method comprised data preprocessing, topic detection, sentiment analysis, and visualization.FindingsThe proposed model can effectively perceive students’ sentiment tendencies on different topics, which provides powerful practical reference for improving the quality of information services in teaching practice.Research limitationsThe model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach, without considering the intensity of students’ sentiments and their evolutionary trends.Practical implicationsThe implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint, which can be utilized as a reference for enhancing the accuracy of learning content recommendation, and evaluating the quality of their services.Originality/valueThe topic-sentiment analysis model can clarify the hierarchical dependencies between different topics, which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.

Crowdsourcing Investigations

2014 ◽  
Vol 6 (4) ◽  
pp. 60-75 ◽  
Author(s):  
Andrea H. Tapia ◽  
Nicolas J. LaLone
Keyword(s):  
Public Opinion ◽  
Sentiment Analysis ◽  
Ethical Dilemmas ◽  
The Internet ◽  
Social Approval ◽  
Online Groups ◽  
The Public ◽  
Large Numbers ◽  
Boston Marathon Bombing ◽  
Boston Marathon

In this paper the authors illustrate the ethical dilemmas that arise when large public investigations in a crisis are crowdsourced. The authors focus the variations in public opinion concerning the actions of two online groups during the immediate aftermath of the Boston Marathon Bombing. These groups collected and organized relief for victims, collected photos and videos taken of the bombing scene and created online mechanisms for the sharing and analysis of images collected online. They also used their large numbers and the affordances of the Internet to produce an answer to the question, “who was the perpetrator, and what kind of bomb was used?” The authors view their actions through public opinion, through sampling Twitter and applying a sentiment analysis to this data. They use this tool to pinpoint moments during the crisis investigation when the public became either more positively or negatively inclined toward the actions of the online publics. The authors use this as a surrogate, or proxy, for social approval or disapproval of their actions, which exposes large swings in public emotion as ethical lines are crossed by online publics.

scholarly journals Sentiment Analysis of Face-To-Face Learning Based on Social Media

2021 ◽  
Vol 4 (3) ◽  
pp. 102-106
Author(s):  
Hendra Saputra Batubara ◽  
Ambiyar Ambiyar ◽  
Syahril Syahril ◽  
Fadhilah Fadhilah ◽  
Ronal Watrianthos
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Health Professionals ◽  
Research Data ◽  
Bayes Method ◽  
Negative Attitudes ◽  
Face To Face ◽  
Education And Health ◽  
Face To Face Learning

The use of restricted face-to-face learning during the epidemic in Indonesia was discussed not just by education and health professionals, but also on social media. The study used the Twitter dataset with the keywords 'school' and 'face-to-face' to examine public opinion about face-to-face learning. The research data was obtained from Twitter utilizing Drone Emprit Academic, and it was then processed using the Naive Bayes method to create sentiment analysis. During that time, research revealed that 32% of people were positive, 54% were negative, and 14% were indifferent. Because of worries about the dangers associated with the use of face-to-face learning, negative attitudes predominate.  

scholarly journals Sentimental Knowledge Graph Analysis of the COVID-19 Pandemic Based on the Official Account of Chinese Universities

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2921
Author(s):  
Xiaolin Li ◽  
Zhiyi Li ◽  
Yahe Tian
Keyword(s):  
Public Opinion ◽  
New Media ◽  
Sentiment Analysis ◽  
Topic Model ◽  
Colleges And Universities ◽  
Mobile Internet ◽  
Knowledge Graph ◽  
Analysis Model ◽  
Governance System ◽  
Network Public Opinion

With the advent of the new media mobile Internet era, the network public opinion in colleges and universities, as an extension of social network public opinion, is also facing a crisis in the prevention, control, and governance system. In this paper, the Fiddler was used to collect the comments and other relevant data of the COVID-19 topic articles on the WeChat Official Accounts of China’s top ten universities in 2020. The BILSTM_LSTM sentiment analysis model was used to analyze the sentiment tendency of the comments, and the LDA topic model was used to mine the topics of the comments with different emotional attributes at different stages of COVID-19. Based on sentiment analysis and text mining, entities and relationships in the theme graph of public opinion events in colleges and universities were identified, and the Neo4j graph database was established to construct the sentimental knowledge graph of the pandemic theme of university public accounts. People’s attitudes in university public opinion are easily influenced by a variety of factors, and the degree of emotional disposition changes over time, with the stage the pandemic is in, and with different commentators; official account opinion topics change with the development of the time stage of the pandemic, and students’ positive and negative comment topics show a diverse trend. By incorporating topic mining into the sentimental knowledge graph, the graph can realize functions such as the emotion retrieval of comments on university public numbers, a source search of security threats in university social networks, and monitoring of comments on public opinion under the theme of the pandemic, which provides new ideas for further exploring the research and governance system of university network public opinion and is conducive to preventing and resolving campus public opinion crises.

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