• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.19-30
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• 2023

An electric scooter(e-scooter), one popularized micro-mobility vehicle has shown rapidly increasing use in many cities. In South Korea, the use of e-scooters has greatly increased, as some companies have launched e-scooter sharing services in a few large cities, starting with Seoul in 2018. However, the use of e-scooters is still controversial because of issues such as parking and safety. Since the perception toward the means of transportation affects the mode choice, it is necessary to track the trends for electric scooters to make the use of e-scooters more active. Hence, this study aimed to analyze the trends related to e-scooters. For this purpose, we analyzed news articles related to e-scooters published from 2014 to 2020 using dynamic topic modeling to extract issues and sentiment analysis to investigate how the degree of positive and negative opinions in news articles had changed. As a result of topic modeling, it was possible to extract three different topics related to micro-mobility technologies, shared e-scooter services, and regulations for micro-mobility, and the proportion of the topic for regulations for micro-mobility increased as shared e-scooter services increased in recent years. In addition, the top positive words included quick, enjoyable, and easy, whereas the top negative words included threat, complaint, and ilegal, which implies that people satisfied with the convenience of e-scooter or e-scooter sharing services, but safety and parking issues should be addressed for micro-mobility services to become more active. In conclusion, this study was able to understand how issues and social trends related to e-scooters have changed, and to determine the issues that need to be addressed. Moreover, it is expected that the research framework using dynamic topic modeling and sentiment analysis will be helpful in determining social trends on various areas.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1167-1173
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• 2015

The development of information and communication technology like SNS, blogs, and bulletin boards, was provided a variety of places where you can express your thoughts and comments and allowing Big Data to grow, many people reveal the opinion of the social issues in SNS such as Twitter. In this study, we would like to pre-built sentimental dictionary about social issues and conduct a sentimental analysis with structured dictionary, to gather opinions on social issues that are created on twitter. The data that I used is "bikini", "nakkomsu" including tweet. As the result of analysis, precision is 61% and F1- score is 74%. This study expect to suggest the standard of dictionary construction allowing you to classify positive/negative opinion on specific social issues.

• 한국체육학회지인문사회과학편
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• v.56 no.4
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• pp.397-413
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• 2017

The purpose of this study is to introduce the case and to provide related information for the physical education major to handle and utilize the social big data through the exploratory study for the application of sports industry in the fourth industrial revolution. For this study, data was collected from the article database, which covers the keyword such as 'Social Big Data', 'Sports' and so on. The analyzed articles were 86 articles. As a results, The research on social big data applied to sports industry are as follows: 1) Analysis of major issues related to sports fans' interests and sports events, 2) A study on media sports engagement, 3) The prediction analysis of sports game based on the sentiment analysis, 4) Development of salary estimation model for professional player in sports, 5) Research trend analysis and so on. In conclusion, the social big data analysis technology in the sports industry and management can be utilized variously. Therefore, the specialists of the sports industry and management field need to learn the techniques, to acquire the know-how for the research project, to convert the convergence thinking.

• Asian Journal for Public Opinion Research
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• v.8 no.3
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• pp.393-406
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• 2020

The discourse on the lockdown in Indonesia is getting stronger due to the increasing number of positive cases of the coronavirus and the death rate. As of August 12, 2020, the confirmed number of COVID-19 cases in Indonesia reached 130,718. There were 85,798 victims who have recovered and 5,903 who have died. Data show a significant increase in cases of COVID-19 every day. For this reason, there needs to be an evaluation of the government policy of the Republic of Indonesia in dealing with the COVID-19 pandemic in Indonesia. An evaluation of policies for handling the pandemic must include public opinion to determine any weaknesses of this policy. The development of public opinion about the lockdown policy can be understood through social media. During the COVID-19 pandemic, measuring public opinion through traditional methods (surveys) was difficult. For this reason, we utilized big data on social media as research data. The main purpose of this study is to understand public opinion on the lockdown policy in overcoming the COVID-19 pandemic in Indonesia. The things observed included: volume of Twitter users, top influencers, top tweets, and communication networks between Twitter users. For the methodological development of future public opinion research, the researchers outline the obstacles faced in researching public opinion based on big data from Twitter. The research results show that the lockdown policy is an interesting issue, as evidenced by the number of active users (79,502) forming 133,209 networks. Posts about the lockdown on Twitter continued to increase after the implementation of the lockdown policy on April 10, 2020. The lockdown policy has caused various reactions, seen from the word analysis showing 14.8% positive sentiment, 17.5% negative, and 67.67% non-categorized words. Sources of information who have played the roles of top influencers regarding the lockdown policy include: Jokowi (the president of the Republic of Indonesia), online media, television media, government departments, and governors. Based on the analysis of the network structure, it shows that Jokowi has a central role in controlling the lockdown policy. Several challenges were found in this study: 1) choosing keywords for downloading data, 2) categorizing words containing public opinion sentiment, and 3) determining the sample size.

• Informatization Policy
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• v.23 no.3
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• pp.26-42
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• 2016

Recently, with the rapid growth of the O2O market, Fintech combining the finance and ICT technology is drawing attention as innovation to lead "O2O of finance", along with Fintech-based payment, authentication, security technology and related services. For new technology industries such as Fintech, technical sources, related systems and regulations are important but previous studies on Fintech lack in-depth research about systems and technological trends of the domestic Fintech industry. Therefore, this study aims to analyze domestic Fintech trends and find the insights for the direction of technology and systems of the future domestic Fintech industry by comparing Kakao Pay and Samsung Pay, the two domestic representative mobile payment services. By conducting a complete enumeration survey about the tweets mentioning Fintech until June 2016, this study visualized topics extraction, sensitivity analysis and keyword analyses. According to the analysis results, it was found that various topics have been created in the technologies and systems between 2014 and 2016 and different keywords and reactions were extracted between topics of Samsung Pay based on "devices" such as Galaxy and Kakao Pay based on "service" such as KakaoTalk. This study contributes to analyzing the unstructured data of social media by period by using social media mining and quantifying the expectations and reactions of consumers to services through the sentiment analysis. It is expected to be the foundation of Fintech industry development by presenting a strategic direction to Fintech related practitioners.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.285-294
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• 2017

In this study, change of the view of love was analyzed by big data analysis in TV drama of married person's love. Two dramas were selected for analysis with opposite theme of love story. The sympathy of audience for the one month period from the end of the drama was analyzed by text mining and sentiment analysis. In particular, changes in the meaning of home meaning are identified. Home is not 'a place where a husband and wife play a social role', but 'a place where they can share real sympathy and one can be happy'. If individuals are not happy, they need to break their homes. In this study, the current divorce rate and the question regarding the matter should be considered. But based on Google Trends, in Korean society, interest in marriage were still higher than romance. It means that people prefer to 'a love to get marriage' in Korean modern society, than 'love for love affair'. It seems to be reflection of cognition change, marriage should be based on true love. This study is expected to be applied to the study of trend change through social media.

• Korean Journal of Legislative Studies
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• v.23 no.1
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• pp.113-149
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• 2017

This study aims to identify the micro-level, social psychological foundation of regionalism and analyze its change and continuity by comparing 1988, 2003, and 2016 survey results. Drawing on the theory of prejudice and social identity, it clarifies the concept of regionalism and examines its affective, behavioral, cognitive implications. In the empirical analysis, where it takes advantage of relevant questions of the same or similar wording in three nationally representative surveys, the study identifies the changes in regionalism at the individual level focusing on anti-Honam prejudice and discrimination and attribution of regional conflict. First, anti-Honam prejudice has been in decline nationally as well as regardless of where one has grown up, except for Daegu/Kyungpook area. Second, anti-Honam prejudice has been weakened among younger generations while regional party identification now affects the sentiment in the direction of regional cleavage overlapped with ideological leanings. Third, while most respondents do not experience explicit discrimination, Honam natives are still more likely to experience discrimination, especially identity and self-esteem related, due to his or her home town. Fourth, Honam natives have been more likely to attribute regional conflict to an external, structural factor like government economic policy and less likely to a subjective one like regional sentiment, which seems to be consistent with attributional attitudes of the victims of prejudice. The study ends with the discussion of how to reduce further anti-Honam prejudice, which includes contact hypothesis, recategorization, cross-categorization, and de-categorization.

• Journal of Digital Convergence
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• v.13 no.3
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• pp.283-294
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• 2015

Through the film 'Miss Granny', we analyzed a variety of structures. Social awareness of the cultural content of the film shown in the form of elderly social psychology, combination of approaches, role structural analysis of the people around centered the main character, acceleration structure analysis of the people around, the atmosphere structural analysis according to the environment of the people around. By these analysis, the elderly mentality was noticed by sociological theory related to the elderly. The formation of negative stereotypes and perceptions in what young people have a bias and unconscious, conscious discrimination against the elderly was mentioned in conclusion. Because the positive changes in recognition of the elderly is necessary, collective prejudice should be eliminated. The social atmosphere composition that can empathize with the sentiment of the elderly in the pursuit of youth and the necessity to develop generation integrated social programs with that younger generation and older generation sympathize together were suggested. were heartfelt suggested.

• Journal of Contemporary Eastern Asia
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• v.23 no.1
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• pp.18-34
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• 2024

In what was labeled the "nut rage" incident, the vice president of Korean Air, Hyun-Ah Cho (Heather Cho), demonstrated behavior that exemplifies corporate transgression and deviation from societal moral standards toward a flight attendant aboard a flight. Such behavior instigated the public to express negative sentiment on various social media platforms. This study investigates word-of-mouth network on YouTube in response to the crisis, patterns of co-commenting activities across selected YouTube videos, as well as public responses to the incident by employing social and semantic network analysis. A total of 512 YouTube videos featuring the crisis from December 8, 2014 through November 11, 2018, and 52,772 public comments to the videos were collected. The central videos in the network successfully attracted the public's attention and engagements. The results suggest that the video network was decentralized, with multiple videos acting as hubs in the network. The public commented on various videos instead of focusing on a few. The contents of influential videos uploaded by popular news organizations revealed not only Cho's behaviors related to the nut rage crisis but also unrelated illegal behaviors and the moral violations committed by the family members of Korean Air. The public attached derogatory remarks to Cho and her family, and the comments also addressed ethical concerns, management issues of the company, and boycott intentions. The results imply that adverse public reaction was related to the long-standing problem caused by family ownership and governance in large Korean corporations. This Korean Air scandal illustrates backlash toward a leadership breakdown by the family business conglomerate prevalent in the Korean society. This study provides insights for effective handling of similar crises.