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Culture was an always “overloaded” concept during Korea’s colonial period. Like the ideas of literature and art, it was one of the main routes through which Koreans developed a socio-political sense when they were forbidden to speak about politics. Starting in the 1920s especially, Koreans used culture to establish intellectual foundations of modernity, cultivate the masses’ aesthetic senses, and seriously engage with colonial reality. Furthermore, the idea of culture became more complicated in the late 1930s as the colonial government more aggressively employed the cultural idea to propagate a series of wars while mainlining Japan’s ascendency in East Asia. Reflecting upon such a conceptual tug of war by different socio-political actors, this article uses text-mining to explore the changing meanings of culture in a 1930s popular magazine. Run by the proponents of culture as a forefront of social movements, Samch’ŏlli (“Threethousand ri,” which figuratively refers to Korea) was a monthly magazine that lasted for more than a decade from 1929 to 1941, unlike many short-lived journals under censorship. By examining the frequency of the keywords that composed the theme of culture, and the semantic network of culture’s cooccurring words, we diachronically trace the polyphonic meanings of culture in different timeframes. These quantitative and linguistic methods suggest that culture’s semantic network drawn from a 1930s periodical was far larger, more diverse in composition, and more influential than explained in previous studies, especially in its interplay with the various socio-political actors in launching collective projects by Korean intellectuals and the colonial government.

This study was to analyze research trends in the field of business administration by applying text mining techniques. In particular, the 2008 global financial crisis caused many management difficulties for companies. It was intended to consider how these events were reflected in research trends. This study performed 'frequency analysis', 'TF-IDF analysis', and 'topic analysis' among the text mining techniques. First, before and after the global crisis, "management" and "leadership" were ranked top tier. Also 'leadership' and 'management' belonged to the top tier in the total period. Second, the most noticeable word was 'safety' before 2008. And 'integration' is the top priority after 2008. Thus, it was found that the business administration field dealt with 'integration' and 'decision making' meaningfully after 2008. Third, we found a paper that corresponds to the gamma value by topics. Key words explain these papers well. In this way, the meaning of extracted words was analyzed by applying text mining techniques, and research trends in the field of business administration were examined through this. In addition, words that appear important and words with special meanings were analyzed by comparing around 2008.

This article investigates the public perception of simultaneous interpreters on social media, namely comments on YouTube and QnA from Naver's Knowledge In (KiN), the largest online Q&A community in South Korea. Co-occurrence analysis and LDA topic modeling were performed on the collected and pre-processed datasets from these sources. The co-occurrence analysis of Naver KiN found the word 'professional' in all clusters of the corpus, confirming a degree of public acceptance of simultaneous interpretation as a professional feat. The word 'professional' showed the most frequent co-occurrence with 'certification'. Co-occurrence analysis of YouTube comments found recurring references to interpreters' skill, passion, personality, and appearance. Interpreters were perceived to have 'elegance', 'grace', 'humility', and 'dignity'. Through this study, we found that the public expects simultaneous interpretation to be a professional practice, even as it is understood through a lens of oversimplification and ambiguity.

3D 프린팅은 4차 산업혁명에서 주목받는 주요 기술로 다양한 분야에서 활용되고 있다. 패션 분야도 3D 프린터를 활용하여 하이엔드 패션부터 웨어러블 패션 제품까지 폭넓게 시도되고 있으며 관련 연구들도 진행 되고 있다. 자유로운 제작과 맞춤형 제품이 가능한 3D 프린팅 패션의 장점을 활용하기 위해서 이에 대한 소비자 인식과 태도의 이해는 중요하나 이를 실증적으로 검증한 연구는 미비하다. 본 연구는 3D 프린팅 패션 에 대한 소비자 인식을 살펴보기 위해 빅데이터 분석 중 텍스트마이닝을 실시하였다. 소셜 빅데이터 프로그 램 텍스톰을 활용하여 국내·외 대표적인 포털사이트 네이버, 다음, 구글에서 ‘3D프린팅+ 패션’으로 키워드를 설정하여 3D 프린팅 패션에 대한 온라인 텍스트를 수집하고 빈도분석, TF-IDF 분석, 오피니언 마이닝 분석을 실시하며 이를 바탕으로 LDA 토픽 모델링 분석을 수행하여 소비자의 긍정 및 부정의 감성을 알아보았으며 주제를 도출하고 소비자의 인식을 살펴보았다. 토픽 모델링과 오피니언 마이닝은 온라인 텍스트 속에서 소수 로 나타나는 소비자의 인식이나 경험에 관한 내용을 추출할 수 있어서 홍보자료 위주 분석 결과의 미비한 부분을 보완할 수 있다. 빈도분석 결과, ‘3D프린팅’, ‘패션’, ‘산업’, ‘디자인’, ‘기술’, ‘패션쇼’, ‘과정’, ‘3D프린 터’, ‘3D’, ‘제작’ 등의 순서로 빈도가 높게 나타났고, TF-IDF는 ‘과정’, ‘디자인’, ‘산업’, ‘패션’, ‘기술’의 순으 로 높았다. 토픽 모델링 분석 결과는 ‘디자인’, ‘패션쇼’, ‘체험’, ‘수상’, ‘산업’, ‘전문기술’, ‘엑스포’, ‘제작’, ‘걸스데이행사’의 9개 토픽이 도출되었으며 오피니언 마이닝 분석 결과, 긍정이 90.2%로 부정 9.8%에 비해 높은 비율로 나타났다. 분석 결과를 토대로 3D 프린팅 패션이 교육 및 대중화를 위한 도입 성장 단계로서 우리 사회에서 긍정적으로 인식됨을 알 수 있으며 이를 통해 향후 3D 프린팅 패션의 디자인 개발과 마케팅 전략 방향으로 도출할 수 있는 토대가 될 것이다.

3D printing is a major technology attracting attention in the fourth industrial revolution and is being used in various fields. In the fashion field, a wide range of attempts are being made from high-end fashion to wearable fashion products using 3D printers, and related research is also being conducted. In order to take advantages of 3D printing fashion, which allows for free production and customized products, it is important to understand consumer perceptions and attitudes towards them, but studies that empirically verify them are lacking. In this study, text-mining was conducted during bog data analysis to examine consumer perception of 3D printing fashion. Using the social big data program Textom, the study set keywords as “3D printing + fashion” in domestic and foreign major portal sites Naver, Daum, and Google to collect online texts about 3D printing fashion, and frequency analysis, TF-IDF analysis, and opinion mining. Based on this above, LDA topic modeling analysis was performed to find out the positive and negative emotions of consumers, and the topics were derived and consumers’ perceptions were examined. As a result of the frequency analysis, the frequency increased in the order of ‘3D printing’, ‘fashion’, ‘industry’, ‘design’, ‘technology’, ‘fashion show’, ‘process’, ‘3D printer’, ‘3D’, ‘production’, etc. TF-IDF was highest in the order of ‘process’, ‘design’, ‘industry’, ‘fashion’, ‘technology’, etc. As a result of the topic modeling analysis, nine topics were derived: ‘design’, ‘fashion show’, ‘experience’, ‘award’, ‘industry’, ‘professional technology’, ‘expo’, ‘production’, and ‘Girl’s day event’. In addition, as a result of opinion mining analysis, the positive rate was 90.2%, which was higher than the negative 9.8%. Based on the analysis results, it shows that 3D printing fashion is positively recognized in our society as a stage of introduction and growth for education and popularization.

Social media has become an important online field in which professional identity construction occurs(Gal et al., 2016). This study aims to understand how interpreters are perceived and represented on the video-sharing website YouTube (www.youtube.com). Using Selenium, a Python library, the title and description of all 298 videos searched with the keyword “tongyeoksa”(meaning “interpreter” in Korean) were scraped. Keyword analysis, co-occurrence network analysis and LDA topic modeling analysis were employed to derive insights from the scraped data set. A qualitative content analysis was performed to complement the result of analysis. Through the analysis, the study reveals that the main topics of ‘interpreter-related’ YouTube videos are “Occupation and English Learning”, “Interpreters under the Media Spotlight”, “Sign Language Interpretation and Certificate”, “Female Sports Interpreter and Appearance”, and “Lectures and Awareness-raising”. Perception of interpreters in general showed an association with ‘foreign language skills’, ‘foreign language learning’, and ‘the growing popularity of Korean cultural content overseas’. On the other hand, ‘certificate’ and ‘awareness-raising about human rights of the socially-underprivileged’ appeared as main topics in the online discourse of sign language interpreters while ‘femininity’ and ‘appearance’ were highlighted in sports interpreters.

Using text-mining technology, this article traces diachronic changes of narrativity in the genre of the British and American novel from the 18th-century to the mid-twentieth-century. Recently introduced to the various fields of the humanities, text-mining is a viable way in which quantitative analyses can be used in literary studies. To open wide the possibility of quantitative anlayses of the genre of novel, this article shows how text-mining is used and innovates the study of novels through sample analyses of Henry Fielding, Daniel Defoe, James Joyce, and Ernest Hemingway. The results show the distinctive differences in narratorial control (first person pronoun), syntactic simplicity, and other grammatical and stylistic features. For instance, in the samples, Hemingway enhanced narrativity by increasing word concreteness and James Joyce by syntactic simplicity, whereas Defoe enhanced it by increasing referential cohesion and Fielding by deep cohesion.

COVID-19로 인한 사회적 거리두기로 외식배달주문플랫폼, 일반적으로 '배달앱'이라 부르는 어플리케이션을 통한 음식배달이 활성화되었 다. 하지만 빠르게 성장한 민간배달앱의 높은 수수료 인상으로 인해 소상공인들의 피해가 발생하게 되면서 각 지방자치단체에서는 이러한 문제들을 해결하기 위하여 '공공배달앱'이라 불리는 공공 배달 애플리케이션을 출시하였다. 그러나 일부 공공배달앱에 대한 여러 불만이 나오고 있어 소비자들로부터 외면을 받고 있으며 낮은 시장 점유율과 인지도로 인해 고전하고 있다. 이에 본 연구는 텍스트 마이닝 기법을 사용하여 지난 1년간의 공공배달앱에 대한 소셜네트워크상의 키워드를 파악하고 소비자의 인식을 분석하고자 하였다. 이를 위해 온라인 네트워크상 존재하는 빅데이터의 수집과 데이터 분석을 실행하는 프로그램인 텍스톰을 활용하여 2021년 8월에서 2022년 8월까지 약 1년간의 데이터를 수집하였고 소셜네트워크 분석 소프트웨어인 Ucinet 6을 이용하여 시각화하였다. 수집된 데이터를 바탕으로 도출된 키워드를 분석한 결과, 배달특급과 먹깨비가 가장 높은 인지도를 가지고 있으며 각각 경기도, 경상북도, 공공배달앱과 연관성이 높은 것으로 드러났다. 또한 배달특급, 먹깨비, 경기도, 소상공인, 이벤트가 소셜네트워크상에서 공공배달앱과 연결되어 많이 등장하고 중요하게 다루어지고 있음을 파악할 수 있었다. CONCOR 분석결과 주요 키워드는 4개 그룹을 형성하였으며 각각 ‘공공배달앱의 마케팅 속성’, ‘공공배달앱의 운영사례’, ‘공공배달앱의 속성’, ‘지역별 공공배달앱’으로 정의하였다. 본 연구의 시사점은 다음과 같다. 텍스트마이닝을 통하여 소비자들이 인식하고 있는 공공배달앱에 대한 인식과 소셜네트워크상에서 공공배달앱과 관련되어 다루어지고 있는 키워드를 도출 하였다. 본 연구결과를 토대로 공공배달앱이 가지는 문제점과 개선방안을 모색하는데 이바지할 수 있을 것으로 사료되며 선행 연구가 부족한 공공배달앱의 후속 연구에 도움을 줄 수 있을 것이다.

Due to social distancing caused by COVID-19, food delivery through a restaurant delivery order platform, an application generally called a "delivery app," has been thrived. local government has launched a public food delivery application called 'public delivery app'. However, many complaints about the quality of service in some public delivery apps have begun to be shunned by consumers. This study attempted to grasp the perception on social networks of public delivery apps over the past year using big data analysis. "Delivery Express" and "Mukkkaebi" have the highest recognition. As a result of the CONCOR analysis, four major factors group were formed, and each was defined as 'Marketing attributes of public delivery apps', 'Operational cases of public delivery apps', 'Properties of public delivery apps', and 'Public delivery apps by region'. The implications of this study are as follows. Through big data analysis, consumers' perception of public delivery apps and related factors were derived. Based on the results of this study, it is expected to help find problems and improvement plans for public delivery apps.

This study analyzed 2,071 job postings related to interpretation from prominent job search platforms, namely Saramin and JobKorea, utilizing a text-mining approach. The study delved into employer preferences, particularly focusing on 'preferred qualifications' and 'job duties.' Python was used to crawl data from online sources and prompt engineering with the Gemini Pro AI model facilitated the extraction of relevant information. Preprocessed data underwent keyword frequency analysis and Latent Dirichlet Allocation (LDA) topic modeling. An association analysis of co-occurring word pairs further enhanced the understanding of employer demands in the interpretation job market. Employers showed a preference for candidates with industry experience, technical skills, foreign language or job-related majors, and possession of relevant certifications other than language and interpretation skills. Notably, emerging industries such as IT and content creation exhibited a heightened emphasis on industry experience and software utilization skills. As for job duties, ‘interpretation and translation’ accounted for only approximately 9.8% of the total job duties described in all postings.

가습기살균제특별법 제정으로 피해구제가 본격화됐지만, 아직 많은 피해자가 인과관계 입증에 어려움을 겪어 피해에 상응하는 보상을 받지 못하고 있다. 이에 본 연구는 특별법 이후 나타난 가습기살균제 피해구제의 문제점 을 검토하고 향후 개선점을 모색하고자 법이 시행된 2017년 8월부터 2023년 11월까지의 뉴스를 대상으로 텍스 트마이닝을 실시하였다. 시간의 흐름에 따른 주요 보도 이슈를 살펴봤을 때, 2017~2020년은 특별법 제정 후 나타난 피해구제의 실효 성에 대한 비판과 법령 개정을 통한 해결과정이 주로 조명됐으며, 2022~2023년은 가습기살균제 피해구제를 위 한 조정위원회의 결렬이 주된 이슈로 나타났다. 뉴스 본문에 대한 LDA 토픽모델링 결과, 협소한 피해구제 지적, 집단 피해구제의 한계, 피해구제 범위의 확대 노력, 민․형사 재판의 인과관계 문제 등의 쟁점 구조가 확인되었다. 상기 연구 결과를 바탕으로 본 연구는 가해기업과 정부가 책임을 인정하고 피해구제 사각지대를 좁히기 위해 각자의 배․보상액 몫을 확장할 것을, 신속하게 역학관계 입증을 완수하고 건강피해 인정기준을 유연하게 적용할 것을, 그리고 소송에서 엄격한 인과관계 입증보다 실질적 피해구제에 집중할 것을 제언하였다.

Despite the Special Act on Remedy for Damage caused by Humidifer Disinfectants, many victims are still unable to get redress for their damages due to difficulties in proving causation. In order to examine the problems in humidifier disinfectant redress that have emerged since the Special Act and to seek solutions, we conducted text-mining on the news from August 2017 to November 2023. When examining the main news issues in chronological order, the years 2017 to 2020 were primarily characterized by criticisms regarding the effectiveness of damage redress after the enactment of the Special Act, and the subsequent process of resolution through legislative amendments. Meanwhile, the years 2022 to 2023 were highlighted by the failure of the mediation committee for humidifier disinfectant damages redress, emerging as the primary issue during this period. As a result of the news topic modeling, the following topic structure was identified: the criticism of narrow damage redress, limitations of collective damage redress, efforts to expand the scope of damage redress, and causation problems in civil and criminal suits. Drawing on the above findings, we recommend that offending companies and governments acknowledge their responsibility and expand their respective shares of damage redress to narrow the gaps in compensation; expedite epidemiological proof and apply flexible criteria for recognizing health damages; and focus on substantive damages rather than strict causation proof in litigation.

Purpose: This study aimed to identify the main keywords, network structures, and topical themes in patient safety incident reports using text network analysis. Methods: The study analyzed patient safety incident reports from a general hospital in Seoul, covering a total of 3,576 cases reported over five years, from 2019 to 2023. Unstructured data were extracted from the text of the incident reports, detailing how the patient safety incidents occurred and how they were managed according to the six-part principles. The analysis was conducted in four steps: 1) word extraction and refinement, 2) keyword extraction and word network generation, 3) network connectivity and centrality analysis, and 4) topic modeling analysis. The NetMiner program was used for data analysis. Results: The analysis of degree, betweenness, and closeness centrality revealed that the most common keywords among the top five were "confirmation," "medication," "inpatient room," "caregiver," and "condition." Topic modeling analysis identified three main topic groups: 1) incidents caused by a lack of awareness of fall risk, 2) incidents of non-compliance with basic medication principles, and 3) incidents due to inaccurate patient identification. Conclusion: To prevent patient safety incidents, it is necessary to promote a culture of safety in hospitals, standardize patient identification procedures, and provide basic training in medication safety and fall prevention to healthcare staff. Furthermore, empirical research on patient safety practices is necessary to encourage active participation in patient safety activities by patients and family caregivers.

Purpose: The purpose of this study was to identify the supportive care needs of parents of preterm children in South Korea using text data from a portal site. Methods: In total, 628 online newspaper articles and 1,966 social network service posts published between January 1 and December 31, 2019 were analyzed. The procedures in this study were conducted in the following order: keyword selection, data collection, morpheme analysis, keyword analysis, and topic modeling. Results: The term "yirundung-yi", which is a native Korean word referring to premature infants, was confirmed to be a useful term for parents. The following four topics were identified as the supportive care needs of parents of preterm children: 1) a vague fear of caring for a baby upon imminent neonatal intensive care unit discharge, 2) real-world difficulties encountered while caring for preterm children, 3) concerns about growth and development problems, and 4) anxiety about possible complications. Conclusion: Supportive care interventions for parents of preterm children should include general parenting methods for babies. A team composed of multidisciplinary experts must support the individual growth and development of preterm children and manage the complications of prematurity using highly accessible media.

CrossRef is an association of scholarly publishers that develops shared infrastructure to support more effective scholarly communications. It is a registration agency for the digital object identifier (DOI), and has built additional services for CrossRef members around the DOI and the bibliographic metadata that publishers deposit in order to register DOIs for their publications. Among these services are CrossCheck, powered by iThenticate, which helps publishers screen for plagiarism in submitted manuscripts and FundRef, which gives publishers standard way to report funding sources for published scholarly research. To add to these services, Cross-Ref launched CrossRef text and data mining services in May 2014. This article will explain the thinking behind CrossRef launching this new service, what it offers to publishers and researchers alike, how publishers can participate in it, and the uptake of the service so far.

COVID-19로 인해 국내외 조직들은 전례없는 상황을 겪고 있으며 이에 대응하기 위한 리더십 연구 역시 부족한 실정이다. 본 연구는 COVID-19 전과 후의 리더십을 데이터로 규명하고자 했다. 이를 위해 한국 기업 조직에서 COVID-19 전과 후로 위기에 요구되는 리더십 행동 차이를 데이터 마이닝 방법을 통해서 규명했다. COVID-19가 팬데믹(Pandamic) 수준으로 선포 되기 이전에 구성원들은 리더들이 내재적/외재적 보상에 더욱 공을 들이고 수평적 조직문화를 만들어주기를 기대한 반면, COVID-19 시대에는 심리적 안전감, 잦은 소통, 미래에 대한 방향성 제시 등을 요구하고 있었다. 이와 같은 위기 상황에서 부각되는 리더십을 본 연구자들은 변혁적 리더십으로 간주하고 두 번째 연구를 설계하여 구성원들의 태도변수인 회복 탄력성과 관계를 실증했다. 회복 탄력성은 위기 상황에서 구성원들이 갖춰야 할 요소로 자주 언급된다. 변혁적 리더십이 COVID-19 시대에 도 회복 탄력성에 유의미하게 영향을 미치는지 살펴봤다. 분석 결과, 변혁적 리더십은 회복 탄력성과 정(+)의 관계를 가지고, 정서적 몰입은 변혁적 리더십과 회복 탄력성의 관계를 부분적으로 매개하였다. 본 연구는 COVID-19 이후로 요구되는 위기 리더십을 데이터로 규명했다는데 실무적으로 기여점을 가지고 있다. 더불어, 위기 상황에서 종종 강조되는 구성원 태도인 회복 탄력성과 관련성을 실증했다는 이론적인 기여점이 있다.

Many organizations are under crisis due to the pandemic of COVID-19, and they may need to understand different leadership during the crisis. This study consisted of two phases. In the first phase, leadership required after COVID-19 was studied by topical modeling which is one of data mining techniques. In the second phase, the primary purpose was to study the correlation between perceived leadership behaviors and personal performance through empirical analysis. Specifically, we analyzed the change in perceived leadership demanded by members of an organization by comparing before and after the declaration of COVID-19. Prior to the declaration, the members frequently mentioned leadership behaviors related to intrinsic and external rewards. After the declaration, on the other hand, words and phrases related to psychological safety, effective communication, and clear direction to the future were frequently observed. Based on the results of the first study, we determined the transformational leadership as the kind of leadership demanded during a crisis like COVID-19, and the second study was designed to test the relationship between perceived transformational leadership and the individual resilience among the members of the organization. In addition, we hypothesized that affective commitment mediates the relationship between the two variables, and the mediation was tested. As a result, we observed a positive relationship between the perceived transformational leadership and the individual resilience, and it was evident that affective commitment partially mediates the relationship. This research contributes to reveal the leadership behaviors demanded in the crisis of COVID-19 by the data mining technique which is important in HR practices. In addition, this research has a theoretical contribution by investigating the leadership style demanded during the time of a crisis and by relating to the individual attitudes. finally, limitations of this research and directions of future research are discussed.

Ik-seon dong is one of the hot place in Seoul especially in these days. This means lots of money and people have been gathering in Ik-seon dong, gentrification was resulted in here. This showed Seoul’s these day’s gentrification, Too fast. Actually Seoul wanted to make detailed design plan in Ik-seon dong, but because of rapid speed, it was really hard work. As a result the plan was made in 2018, but the gentrification of Ik-seon dong give lots of information. In this situation, for analysing the gentrification, proper information, such as land use, the number of visitors, would be needed. However it is really difficult work because there isn’t data base about that. Text-mining is a analysis method using social network, so in this research, there are proper opportunity to reveal Ik-seon dong’s Gentrification.

도덕적인 인간과 정의로운 시민은 도덕과 교육이 지향하는 인간상이 다. 도덕과 교육의 목표로서 두 인간상은 도덕성과 시민성, 도덕교육과 시민교육에 대한 논의의 연속선 상에 있다. 본 연구는 이러한 문제의 식을 바탕으로 도덕적인 인간과 정의로운 시민에 대한 한국 청소년의 인식을 확인하기 위해 진행되었다. 전국의 중·고등학생 942명을 대상 으로, 두 인간상에 대한 학생들의 서술을 텍스트 마이닝 기법을 활용하여 분석하였다. 연구결과에 따르면, 첫째, 두 인간상에 대한 학생들 의 인식은 상당 부분 중첩되어 있었다. 둘째, 도덕적인 인간을 정의하 는 방식에 있어 중학생들은 덕목을 중심으로 서술한 반면, 고등학생들 은 윤리에 대한 심화된 이해를 중심으로 서술하였다. 셋째, 정의로운 시민을 정의하는 방식은 학교 급의 변화에 따라 더 구체적이고 세부적 으로 나타났다. 연구결과를 바탕으로, 본 연구는 도덕과 교육과 도덕과 시민교육을 위한 몇 가지 제안을 한다.

A moral person and a just citizen are the desirable human character of moral education. This study aims to analyze the perception of a moral person and a just citizen. Korean youth's responses to two human characters were analyzed using the text-mining method. According to research, there was an overlapping domain between a moral person and a just citizen. In addition, middle school students described a moral person based on virtues, whereas high school students used the deeper terms of ethics. Finally, the detailed perceptions of a just citizen increased with age. On that basis, this study discusses moral and citizen education.

본 논문은 ‘공공(임대)주택’에 대한 경기도의회 의원들의 입장을 분석한다. 경기도의회(3 대, 1991년)부터 10대(2021년)까지 경기도의회 홈페이지에 등재된 30년의 ‘속기(회의)록 (Text-Big data)’을 대상으로 정당별로 그리고 선수별로 의원 발언을 분류하고 ‘공공(임대) 주택’에 대한 의원 발언이 긍정적인지 부정적인지를 분석하여 발언의 추이와 의원의 입장 차이를 밝힌다. 연구 방법은 빅데이터 분석에 적합한 텍스트마이닝(Text-Mining)을 위해 R 을 사용하여 단어 정제와 빈도 분석을 실시하였다. 또한 발언 내용을 긍정과 부정의 관점 에서 분석하기 위해 내용 분석을 실시하였다. 분석 결과 경기도의회 속기(회의)록에서 '주 택'과 관련한 발언 중 '공공(임대)주택' 발언이 차지하는 비중은 22%로 나타났다. 1. 정당별, 진보와 보수 간의 발언 빈도의 격차는 시기별 역전 현상에도 불구하고 ‘공공(임대)주택’은 보수와 진보 모두 90% 이상 ‘긍정적’인 내용의 발언으로 이루어져 있는 것으로 분석 되었 고 2. 초선 및 재선 이상의 의원들의 ‘공공(임대)주택’에 대한 긍정적인 발언은 90%(52명, 101회)로 의원들 대부분이 긍정적 입장을 나타내었다.

Housing continues to be a social issue, and one of the most important policies to be addressed by central and local governments. As we approach the 30th anniversary of local self-government, it is necessary to examine the position of local councils in the debate over public (rental) housing. However, there are only a few previous studies in this regard, and they are limited to analyzing local governments (such as Seoul Metropolitan Government) rather than local councils. This paper analyzes the stance of members of the Gyeonggi Provincial Assembly on 'public (rental) housing'. Using 30 years of 'shorthand (meeting) logs' (Text-Big data) listed on the Gyeonggi Provincial Assembly website from the 3rd (1991) to the 10th (2021), we classify lawmakers' remarks by party and player, and analyze whether their remarks on 'public (rental) housing' are positive or negative to reveal the trend of remarks and differences in lawmakers' positions. The research method utilized R, a text-mining tool suitable for big data analysis, to analyze the frequency of remarks, and the researcher read and organized the content of the secondary remarks. As a result of the analysis, the proportion of 'public (rental) housing' remarks among remarks related to 'housing' in the shorthand (meeting) record of the Gyeonggi Provincial Assembly was 22%. 1.Despite the disparity in the frequency of remarks between political parties and between liberals and conservatives, the position on 'public (rental) housing' was analyzed as consisting of more than 90% of 'positive' remarks by both conservatives and liberals. 2.The positive remarks on 'public (rental) housing' by first-time and re-elected legislators were 90% (52, 101 times), indicating that most of the legislators expressed a positive position.

As technology becomes integral to customer experiences, the study investigates the adoption of technologies in hotels and their influence on traditional service. The integration of IoT is explored for its potential to create new experiences and increase customer satisfaction, alongside challenges such as security concerns and high investment costs. Therefore, this research gathered Google News data using the keywords "Smart Hotel" and "Hotel IoT" to analyze emerging trends in the hospitality sector. Co-occurrence network analysis and Latent Dirichlet Allocation (LDA) topic modeling unveil key clusters and topics, emphasizing customer experience, technology amenities, and intelligent operation. The findings contribute valuable insights into the evolving landscape of smart hotels and the relationship between IoT and the hospitality sector.

The Environmental, Social, and Governance (ESG) metric is currently the gold standard for assessing how large corporations perform in these three areas of their daily operations. Despite the effectiveness of ESG in analyzing large corporations, little attention has been paid to micro-level research. Investment proposals that include ESG concepts are becoming more common, with major platforms encouraging entrepreneurs to consider these issues in their pitches. As a result, the conventional wisdom regarding these proposals is that the majority of them will result in a higher success rate due to the ESG trend. We investigate whether this is true by analyzing a Kickstarter dataset containing over 9000 online entrepreneurial pitches. To determine which characteristics of these entrepreneurial proposals resulted in increased investment, we used Ordinary Least Squares (OLS) and Logistic Regression. Contrary to popular belief, our findings revealed that using ESG themes in micro-entrepreneurial pitches increased the likelihood of failure..

With the development of deep learning techniques, text mining is producing breakthrough performance improvements, promising future applications, and practical use cases across many fields. Likewise, even though several attempts have been made in the field of financial information, few cases apply the current technological trends. Recently, companies and government agencies have attempted to conduct research and apply text mining in the field of financial information. First, in this study, we investigate various works using text mining to show what studies have been conducted in the financial sector. Second, to broaden the view of financial application, we provide a description of several text mining techniques that can be used in the field of financial information and summarize various paradigms in which these technologies can be applied. Third, we also provide practical cases for applying the latest text mining techniques in the field of financial information to provide more tangible guidance for those who will use text mining techniques in finance. Lastly, we propose potential future research topics in the field of financial information and present the research methods and utilization plans. This study can motivate researchers studying financial issues to use text mining techniques to gain new insights and improve their work from the rich information hidden in text data.

Recently, demand on application or using Bigdata analytics for CRM (Customer Relationship Management) has emerged in industry and academic research. However, most of previous text analytics studies validated algorithms and reported results of analysis without theoretical background or standardized framework. According to this reasons, expanding studies on various contexts and utilization have been limited. This study aims to develop theory-based framework on text mining techniques to evaluate service quality. Hence, previous studies and business cases are reviewed for selecting appropriate algorithms for measuring service quality. In this process, developed framework was applied to analysis customer’s online reviews. This study will be useful initial guideline on business operators who want to evaluate their service quality from user-generated-contents. It also has values on introductory business research on applied text data analysis and expand research scope and method on service research.