scholarly journals Social Emotional Opinion Decision with Newly Coined Words and Emoticon Polarity of Social Networks Services

2019 ◽  
Vol 11 (8) ◽  
pp. 165 ◽  
Author(s):  
Jin Sol Yang ◽  
Myung-Sook Ko ◽  
Kwang Sik Chung
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Threshold Value ◽  
Big Data Analysis ◽  
Network Services ◽  
Social Big Data ◽  
The Social ◽  
Social Network Services ◽  
The Government ◽  
Social Opinions

Nowadays, based on mobile devices and internet, social network services (SNS) are common trends to everyone. Social opinions as public opinions are very important to the government, company, and a person. Analysis and decision of social polarity of SNS about social happenings, political issues and government policies, or commercial products is very critical to the government, company, and a person. Newly coined words and emoticons on SNS are created every day. Specifically, emoticons are made and sold by a person or companies. Newly coined words are mostly made and used by various kinds of communities. The SNS big data mainly consist of normal text with newly coined words and emoticons so that newly coined words and emoticons analysis is very important to understand the social and public opinions. Social big data is informally made and unstructured, and on social network services, many kinds of newly coined words and various emoticons are made anonymously and unintentionally by people and companies. In the analysis of social data, newly coined words and emoticons limit the guarantee the accuracy of analysis. The newly coined words implicitly contain the social opinions and trends of people. The emotional states of people significantly are expressed by emoticons. Although the newly coined words and emoticons are an important part of the social opinion analysis, they are excluded from the emotional dictionary and social big data analysis. In this research, newly coined words and emoticons are extracted from the raw Twitter’s twit messages and analyzed and included in a pre-built dictionary with the polarity and weight of the newly coined words and emoticons. The polarity and weight are calculated for emotional classification. The proposed emotional classification algorithm calculates the weight of polarity (positive or negative) and results in total polarity weight of social opinion. If the total polarity weight of social opinion is more than the pre-fixed threshold value, the twit message is decided as positive. If it is less than the pre-fixed threshold value, the twit message is decided as negative and the other values mean neutral opinion. The accuracy of the social big data analysis result is improved by quantifying and analyzing emoticons and newly coined words.

A Social Big Data Analysis on Sport Participation

2020 ◽  
Vol 25 (2) ◽  
pp. 18-30
Author(s):  
Seung Wook Oh ◽  
Jin-Wook Han ◽  
Min Soo Kim
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Sport Participation ◽  
Big Data Analysis ◽  
Social Big Data

scholarly journals A News Big Data Analysis of Issues in Higher Education in Korea amid the COVID-19 Pandemic

2021 ◽  
Vol 13 (13) ◽  
pp. 7347
Author(s):  
Jangwan Ko ◽  
Seungsu Paek ◽  
Seoyoon Park ◽  
Jiwoo Park
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analysis ◽  
College Education ◽  
Big Data Analysis ◽  
Second Phase ◽  
Public Attention ◽  
Job Market ◽  
Media Reports ◽  
The Government

This paper examines the main issues regarding higher education in Korea—where college education experienced minimal interruptions—during the COVID-19 pandemic through a big data analysis of news articles. By analyzing policy responses from the government and colleges and examining prominent discourses on higher education, it provides a context for discussing the implications of COVID-19 on education policy and what the post-pandemic era would bring. To this end, we utilized BIgKinds, a big data research solution for news articles offered by the Korea Press Foundation, to select a total of 2636 media reports and conducted Topic Modelling based on LDA algorithms using NetMiner. The analyses are split into three distinct periods of COVID-19 spread in the country. Some notable topics from the first phase are remote class, tuition refund, returning Chinese international students, and normalization of college education. Preparations for the College Scholastic Ability Test (CSAT), contact and contactless classes, preparations for early admissions, and supporting job market candidates are extracted for the second phase. For the third phase, the extracted topics include CSAT and college-specific exams, quarantine on campus, social relations on campus, and support for job market candidates. The results confirmed widespread public attention to the relevant issues but also showed empirically that the measures taken by the government and college administrations to combat COVID-19 had limited visibility among media reports. It is important to note that timely and appropriate responses from the government and colleges have enabled continuation of higher education in some capacity during the pandemic. In addition to the media’s role in reporting issues of public interest, there is also a need for continued research and discussion on higher education amid COVID-19 to help effect actual results from various policy efforts.

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