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 The Happy Level: A New Approach to Measure Happiness at Work Using Mixed Methods

2021 ◽  
Vol 20 ◽  
pp. 160940692110024
Author(s):  
Gisela Sender ◽  
Flavio Carvalho ◽  
Gustavo Guedes
Keyword(s):  
Social Media ◽  
Mixed Methods ◽  
Sentiment Analysis ◽  
Narrative Analysis ◽  
Research Data ◽  
Work Life ◽  
Self Report ◽  
Level Indicator ◽  
New Approach ◽  
Automatic Methods

Happiness at Work is considered the Holy Grail of organizational sciences. The belief that happier workers are more productive leads to a win-win situation for both individuals and organizations. Nevertheless, years of research have not brought a convergent conclusion about the topic, mainly due to the lack of a widely accepted measure. Usually, questionnaires and self-report surveys are used; however, these methods embed shortcomings that allow studies’ results to be questioned. In order to overcome these shortcomings, the present study proposes a different approach to measure Happiness at Work, bringing mixed methods to encompass the complexity of the phenomenon. Based on work-life narratives and following Kahneman’s concepts, the proposed approach puts together Narrative Analysis and Sentiment Analysis. Although increasingly used to assess social media reviews, Sentiment Analysis is not yet applied to narratives related to Happiness at Work. Four methods to calculate the Happy Level indicator were tested on actual research data: one manual, through traditional coding processes, and three automatic methods to provide scalability. An example of the Happy Level application is also provided to illustrate how the indicator could improve analyses. The present study concludes that despite the manual method presents better results at this moment; the automatic ones are promising. The results also indicate paths for improvement of these methods.

scholarly journals Ethnicity matters in perceived impacts and information sources of COVID-19 among mothers with young children in Australia: a cross-sectional study

BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e050557
Author(s):  
Li Ming Wen ◽  
Huilan Xu ◽  
Danielle Jawad ◽  
Limin Buchanan ◽  
Chris Rissel ◽  
...  
Keyword(s):  
Social Media ◽  
Young Children ◽  
Health Professionals ◽  
Information Sources ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Face To Face ◽  
Related Information ◽  
Health Related ◽  
English Speaking

ObjectivesThis study aimed to investigate perceived impacts, ways of communication with professionals and information sources related to COVID-19, and explore whether these impacts or information sources were associated with ethnicity that is, language spoken at home.DesignA cross-sectional study.SettingSydney, Australia during the period from March to October 2020.ParticipantsMothers of young children participating in an existing trial.Outcome measuresMothers were asked to respond to a set of survey questions related to COVID-19 via telephone. The questions included a mental health scale, and how they communicated with health professionals and their information sources related to COVID-19 during the COVID-19 pandemic.ResultsOf 537 mothers who completed the survey (81% response rate), 45% reported they spoke a language other than English at home. Overall, 136 (26%) reported experiencing mental distress. 234 (44%) reported that COVID-19 affected the way they receive and communicate health-related information with health professionals, especially for those from non-English speaking backgrounds with an adjusted odds ratio (AOR) 1.58 (95% CI 1.10 to 2.27). They were less likely to use a face-to-face service (AOR 0.55, 95% CI 0.37 to 0.80) and more likely to use social media (AOR 2.11, 95% CI 1.40 to 3.17) for health-related information. Regarding sources of COVID-19-related information, mothers from non-English-speaking backgrounds were more likely to rely on family members (AOR 1.49, 95% CI 1.01 to 2.19) and social media (AOR 3.34, 95% CI 2.05 to 5.43).ConclusionsCOVID-19 has significantly impacted mothers with young children in regard to their mental health, means of communication with health professionals and sources of health information. Mothers from non-English-speaking communities were less likely to use a face-to-face service, and more likely to seek information from family members and social media. Appropriate health support for non-English-speaking community needs to take these factors into account.Trial registration numberANZCTR:12618001571268.they

scholarly journals Sentiment Analysis of Perpustakaan Nasional Republik Indonesia Through Social Media Twitter

MATICS ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 90
Author(s):  
Fakhris Khusnu Reza Mahfud
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
User Needs ◽  
Twitter Data ◽  
Knn Classification ◽  
National Libraries

The library is a gate of science and a heart of civilization. Indonesia already has a Perpustakaan Nasional consisted of 27 floors and is equipped with facilities that are adequate for user needs. Apart from that, we need to see opinions from the community as users. Public opinion about the library is critical for library managers to evaluate services and facilities from the library. One way to find out the views of the community is by using social media twitter. Twitter social media is often used in channelling opinions or expressing opinions about specific topics; besides social media, twitter is commonly used for digital campaign movements. Submission of views and even digital campaigns on Twitter social media greatly influence the opinions and even behaviour of society in various ways. This study analyzes tweets about national libraries by classifying, positive opinions, negative opinions and neutral opinions. In this study, twitter data will go through the preprocessing, weighting, and classification stages. TF-IDF and TF binary are used in weighting in this study. The classification used in this study is Naive Bayes and KNN. Accuracy, precision, and recall values were also used in this study to evaluate classification performance. The highest classification performance using KNN classification with TF-IDF weighting resulted in the value of accuracy, precision, and recall of 83.33%, 79.2%, and 83.3% respectively.

scholarly journals PELAKSANAAN PEMBELAJARAN DARING SENI BUDAYA DI KELAS VII SMP NEGERI 7 PADANG

2020 ◽  
Vol 9 (3) ◽  
pp. 15
Author(s):  
Tyas Widyanti ◽  
Irdhan Epria Darma Putra
Keyword(s):  
Online Learning ◽  
Learning Outcomes ◽  
Theoretical Basis ◽  
Research Data ◽  
Lesson Plans ◽  
Learning Activities ◽  
Face To Face ◽  
Cultural Arts ◽  
Music Ensemble ◽  
Face To Face Learning

Abstract This study discusses the Implementation of Online Learning in SMP Negeri 7 Padang. This research is a qualitative research and it uses the descriptive analytic approach which tends to use theoretical basis analysis used to guide the focus to be in accordance with the facts in the field. The research data were collected through interviews done to informants. Based on the results of the research implemented in two meetings, the author concludes that the implementation of online learning of Cultural Arts in music ensemble material to class VII.1 SMP Negeri 7 Padang is the use of WhatsApp Group media recommended by the assistant principle of Curriculum of SMP Negeri 7 Padang to deal with online learning so that learning activities still can be constructed even in a remote condition. The planning of online learning of music ensemble material is not optimal because the teachers do not create lesson plans specifically for the online learning process. The implementation of online learning in Cultural Arts in class VII is in accordance with the lesson plans implemented during face-to-face learning in the classroom. The online learning of Cultural Arts of the ensemble music material continues to run smoothly despite the simple learning. The learning outcomes of Cultural Arts of music ensemble of class VII are less than optimal because the learning activities are constructed without paying attention to the standards related to the preparation of online learning materials.Key words: Online Learning, Music Ensembless

scholarly journals Public Sentiment Analysis of Online Transportation in Indonesia through Social Media Using Google Machine Learning

2021 ◽  
pp. 153
Author(s):  
Desak Ayu Savita ◽  
I Ketut Gede Darma Putra ◽  
Ni Kadek Dwi Rusjayanthi
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Level Of Service ◽  
Public Sentiment ◽  
Analysis Process ◽  
Transportation Sector ◽  
Negative Sentiment

Public opinion is important to agencies or parties in particular fields, as it may indicate a tendency of public's view towards something (such as an object or process). One of them is in the transportation sector. Transportation has become a necessity for the community, many things more effective and efficient online, so that online transportation becomes important for society. The proliferation of online transportation, caused citizens to express opinions through social media. It is important to know the level of service of online transportation considering the large number of users, so that it can be used as a basis for improvement. One of the methods public opinion in social media is by sentiment analysis. The study used the help of Google Machine Learning for the sentiment analysis process that can produce 82,6% of accuracy number, 82,2% of precision, 83,3% of recall with the most sentiment result indicate to public opinion falls into the negative sentiment category for Gojek companies in media social of Twitter.

scholarly journals Communication mediums used by patients and health professionals during access and provision of healthcare in low resource settings: A cross-sectional study

2019 ◽  
Author(s):  
Mathias Dongyele ◽  
Daniel Ansong ◽  
Francis Osei Adjei ◽  
Evans Xamuzu ◽  
Nicholas Karikari Mensah ◽  
...  
Keyword(s):  
Social Media ◽  
Health Professionals ◽  
Health Workers ◽  
Hospital Staff ◽  
Cross Sectional Study ◽  
Health Providers ◽  
Cross Sectional ◽  
Reminder System ◽  
Face To Face ◽  
Almost All

Abstract Background: The issue of mediums to communicate to make enquiries to a hospital in finding out the type of services available, availability of physicians and beds at the receiving hospitals, and a reminder system remains a challenge for patients and providers of the health service in the Sub-Sharan Africa. This present study sought to review the existing healthcare communication mediums from the perspectives of patients and health professionals at the Komfo Anokye Teaching Hospital, KumasiMethod A cross-sectional design was employed with a multilevel sampling method to select a total of 651 participants consisting of 304 patients, 303 health workers and 44 hospital directorate managers for the study. A well-structured survey questionnaire was used to collect data from respondents.Results Most hospital staff (66.4%) used a blend of social media and direct means (face-to-face medium) for communicating among themselves whereas 89.8% only communicates with management through meetings sections. Predominantly, 97.4% of the staff communicated by direct means (face-to-face medium) with patients. Almost all the management of the hospital communicated with the general public using mediums like letters and official memos.Conclusions There is evidence of combination of both traditional mediums (face-to-face) and the technological mediums (social media) for communications by health providers and health consumers. However there is a dissatisfaction with delayed information flow and poor feedback with the use of these available mediums. Therefore, a digital mobile application communication system is recommended to offer efficient communication within and outside the Ghanaian health facilities.

Sentiment Analysis of the Enforcement of PSBB Part II in Jakarta

2020 ◽  
Vol 9 (2) ◽  
pp. 259
Author(s):  
Gede Putra Aditya Brahmantha ◽  
I Wayan Santiyasa
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Crawling ◽  
K Nearest Neighbor ◽  
Bayes Method ◽  
The Public ◽  
Naive Bayes Method

In addition to communicating, Social Media is a place to issue opinions by the public on many things that are currently taking place, Twitter is one of these social medias that is widely used in conveying opinions regardless of whether these opinions are negative, positive, or even neutral. Tweets data about the Enforcement of PSBB Part II in Jakarta were obtained as many as 200 opinions using web crawling then advanced to the preprocessing stage before being classified using the K-Nearest Neighbor and Multinomial Naive Bayes algorithms. In 3 tests, the highest accuracy was 65.00% for K-Nearest Neighbor and the highest accuracy was 85.00% for Multinomial Naive Bayes method.

scholarly journals Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

2020 ◽  
Vol 17 (22) ◽  
pp. 8701 ◽  
Author(s):  
Wen Shi ◽  
Diyi Liu ◽  
Jing Yang ◽  
Jing Zhang ◽  
Sanmei Wen ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Healthcare System ◽  
Health Risks ◽  
Emotional Support ◽  
Social Distancing ◽  
The Us ◽  
Almost All

During the COVID-19 pandemic, when individuals were confronted with social distancing, social media served as a significant platform for expressing feelings and seeking emotional support. However, a group of automated actors known as social bots have been found to coexist with human users in discussions regarding the coronavirus crisis, which may pose threats to public health. To figure out how these actors distorted public opinion and sentiment expressions in the outbreak, this study selected three critical timepoints in the development of the pandemic and conducted a topic-based sentiment analysis for bot-generated and human-generated tweets. The findings show that suspected social bots contributed to as much as 9.27% of COVID-19 discussions on Twitter. Social bots and humans shared a similar trend on sentiment polarity—positive or negative—for almost all topics. For the most negative topics, social bots were even more negative than humans. Their sentiment expressions were weaker than those of humans for most topics, except for COVID-19 in the US and the healthcare system. In most cases, social bots were more likely to actively amplify humans’ emotions, rather than to trigger humans’ amplification. In discussions of COVID-19 in the US, social bots managed to trigger bot-to-human anger transmission. Although these automated accounts expressed more sadness towards health risks, they failed to pass sadness to humans.

Do social media create revolutions? Using Twitter sentiment analysis for predicting the Maidan Revolution in Ukraine

2019 ◽  
Vol 15 (3) ◽  
pp. 275-283 ◽  
Author(s):  
Iana Sabatovych
Keyword(s):  
Social Media ◽  
Social Movements ◽  
Public Opinion ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Social Engagement ◽  
Naive Bayes ◽  
Public Attitudes ◽  
Social Media Platforms ◽  
Online Prediction

A wide variety of social media platforms have become integral to contemporary forms of social engagement, including mass protests. Twitter is considered specifically indicative of public attitudes in this regard. This study attempts to examine the feasibility of using Twitter sentiment analysis to predict the 2014 revolution in Ukraine. Tweets representing public opinion are clustered by means of the ‘StreamKM++’ algorithm into three classes (likely, neutral and unlikely). The resulting prediction model for the three classes (using Naïve Bayes) was 96.75 per cent. As such, this study offers a promising way to perform an online prediction of social movements.

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