• The Korean Journal of Applied Statistics
• /
• v.32 no.2
• /
• pp.301-317
• /
• 2019

There has been significant research conducted on how to discover various insights through text data using statistical techniques. In this study we analyzed text data produced by the Korean National Police Agency to identify trends in the work by year and compare work characteristics among local authorities by identifying distinctive keywords in documents produced by each local authority. A preprocessing according to the characteristics of each data was conducted and the frequency of words for each document was calculated in order to draw a meaningful conclusion. The simple term frequency shown in the document is difficult to describe the characteristics of the keywords; therefore, the frequency for each term was newly calculated using the term frequency-inverse document frequency weights. The L2 norm normalization technique was used to compare the frequency of words. The analysis can be used as basic data that can be newly for future police work improvement policies and as a method to improve the efficiency of the police service that also help identify a demand for improvements in indoor work.

• Journal of Korea Society of Industrial Information Systems
• /
• v.25 no.6
• /
• pp.109-123
• /
• 2020

There has been a major change in the way of life and thinking of all mankind due to covid19. In particular, managerial issues related to supply chain such as global supply chain disruption, and trade friction among countries are drawing the attention. Accordingly, a number of studies are being conducted on the supply chain challenges and solutions to overcome the covid19 crisis, but published research on the impact of covid19 on supply chain management is lacking. In this study, network text analysis is conducted mainly on news articles and this study summarizes the issues related to supply chain management in the era of covid19. The trend analysis results indicated that actively discussed area was global supply chain restructuring and confirmed that main topics are re-shoring, applications of new technology, and the new normal in supply chains. The findings are expected to help expand the scope of research in supply chain management research in the covid19 era.

• Journal of Digital Convergence
• /
• v.19 no.2
• /
• pp.21-32
• /
• 2021

The current study investigates how Malaysian ESL learners' L2 (English) speaking fluency is reflected in advanced L2 reading and what difficulties they encounter in reading comprehension. Nine Malaysian students attending a Korean university participated in qualitative research using in-depth and semi-structured interviews. The data revealed that L2 was a very familiar language, and their speaking fluency in L2 reduced the anxiety of L2 reading in general. However, it did not play a significant role in reading at an advanced level. Their difficulties in reading were mainly due to a lack of vocabulary knowledge. However, insufficient background knowledge and interest also frustrated their reading tasks. These factors lowered their reading comprehension, causing inaccurate interpretations or discouraging their endeavors to find messages from the given text. Thus, these findings should be carefully addressed in reading classes for Korean L2 learners as well as international students.

• Knowledge Management Research
• /
• v.21 no.4
• /
• pp.157-173
• /
• 2020

In accommodation sharing economy, customers take a risk of uncertainty about product quality, which is an important factor affecting users' satisfaction. This risk can be lowered by the information disclosed by the facility provider. Self-presentation of the hosts can make a positive effect on listing performance by eliminating psychological distance through emotional interaction with users. This paper analyzed the self-presentation text provided by Airbnb hosts and found key aspects in the text. In order to extract the aspects from the text, host descriptions were separated into sentences and applied the Attention-Based Aspect Extraction method, an unsupervised neural attention model. Then, we investigated the relationship between aspects in the host description and the listing performance via linear regression models. In order to compare their impact between the three facility types(Entire home/apt, Private rooms, and Shared rooms), the interaction effects between the facility types and the aspect summaries were included in the model. We found that specific aspects had positive effects on the performance for each facility type, and provided implication on the marketing strategy to maximize the performance of the shared economy.

• Science of Emotion and Sensibility
• /
• v.26 no.1
• /
• pp.101-114
• /
• 2023

This research aimed to recognize the determinant characteristics affecting consumers' clothing fit evaluation by employing text mining and social network analysis. For this aim, we first extracted text data linked to clothing fit from 2,000 consumer reviews collected from social network services and conducted semantic network examination and CONCOR analysis. As a result, we reported that "pants" and "skirts" were the most commonly associated clothing items with consumers' clothing fit evaluation. And the length of clothing was most commonly investigated. Then, the "waist" and "hip" were the most critical body parts affecting consumers' perception of clothing fit. Further, the four keywords including "wide," "large," "short," and "long" were the most employed ones in consumer reviews when evaluating clothing fit. This study is meaningful in that it specifically recognized the structural relationship and semantic meanings of keywords relevant to consumers' evaluation of clothing fit, which could bring empirical reference information for advanced clothing fit.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.486-489
• /
• 2021

The purpose of this study was to investigate whether block-based programming language, which is currently being used in elementary and secondary schools, attracts students' interest and motivates them to learn. In addition, this study was to investigate how block-based programming language can help students improve their computing thinking ability and have a good effect on learning text-based programming to learn in high school. In addition, this study tried to study the direction of education linked with artificial intelligence and programming, which are popular in the era of the Fourth Industrial Revolution. The interest in software education has increased so much that software and information education from elementary school to high school has achieved quantitative and qualitative growth that can not be compared with before. However, in the field of artificial intelligence, discussions have begun, but we can not say that we have yet established ourselves in our education. We will discuss how block-based programming and text-based programming will be combined with artificial intelligence and educated.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.10
• /
• pp.105-111
• /
• 2024

In this paper, we propose methods for the named entity normalization of traditional herbal formula found in medical texts. Specifically, we developed methodologies to determine whether mentions, such as full names of herbal formula and their abbreviations, refer to the same concept. Two different approaches were attempted. First, we built a supervised classification model that uses BERT-based contextual vectors and character similarity features of herbal formula mentions in medical texts to determine whether two mentions are identical. Second, we applied a prompt-based querying method using GPT-4o mini and GPT-4o to perform the same task. Both methods achieved over 0.9 in Precision, Recall, and F1-score, with the GPT-4o-based approach demonstrating the highest Precision and F1-Score. The results of this study demonstrate the effectiveness of machine learning-based approaches for named entity normalization in traditional medicine texts, with the GPT-4o-based method showing superior performance. This suggests its potential as a valuable foundation for the development of intelligent information extraction systems in the traditional medicine domain.

• 한국벤처창업학회:학술대회논문집
• /
• 2020.06a
• /
• pp.31-37
• /
• 2020

높은 위험을 안고 사업을 하는 벤처 기업은 자금 조달이 쉽지 않다. 벤처 캐피탈은 벤처의 재정적 필요를 해결하고 부족한 역량을 보충함으로써 벤처의 성공을 돕고 고위험 고수익의 벤처 생태계를 지탱하는 중요한 역할을 담당한다. 국내 벤처 캐피탈의 성장과 지속적인 관심에도 불구하고 학문적 성과가 충분히 축적되고 있는지는 의문이다. 이에 따라 본 연구는 2011년부터 2019년까지 벤처창업을 주제로 한 연구의 주요 흐름을 텍스트 마이닝 방법을 통해 고찰함으로써 문제를 진단하고 시사점을 도출하고자 한다. KCI 키워드 트렌드와 벤처 캐피탈의 성장에 관한 시계열 상관분석의 결과 학술적 성과가 벤처 캐피탈의 성장 추이를 따라가지 못하는 것으로 보인다. 또한 벤처창업연구의 주제 흐름을 바이그램과 TF-IDF로 관찰한 결과 2016 이후 창업 기업에 대한 연구 관심이 두드러지고 2019년에 들어 벤처 캐피탈에 관한 연구 커뮤니티의 관심이 높아진 것으로 나타났다. 본 연구의 결과는 벤처 캐피탈에 관한 주요 연구 토픽을 보다 더 적극적으로 발굴하고 탐구함으로써 연구 커뮤니티의 책무를 강화하고 한국의 벤처 캐피탈 성장과 그에 따른 이슈들을 논의할 이론적 기틀 마련이 필요함을 환기한다.

• Journal of Digital Convergence
• /
• v.17 no.5
• /
• pp.137-143
• /
• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• Proceedings of the Korea Contents Association Conference
• /
• 2019.05a
• /
• pp.259-260
• /
• 2019

이 연구는 텍스트마이닝 및 사회연결망 분석을 통하여 지금까지 축적된 연구주제의 핵심어와 네트워크 지식구조를 확인하여 무용경영 연구의 흐름과 동향을 분석하는데 목적이 있다. 무용경영 연구동향에 관한 텍스트마이닝 분석 결과, 전반적으로 무용경영 연구에서 가장 높은 빈도를 나타낸 특정 토픽으로는 'Performing arts', 'Entrepreneurship', 'Dance', 'Audience development', 'Dance management' 등이 도출되었다. 사회연결망 분석을 실시한 결과, 'Entrepreneurship', 'Dance Marketing', 'Marketing'에서 노드간의 연결성이 높은 것으로 나타났다. 또한 국외에서는 꾸준히 관객개발(audience development)과 공연마케팅(performing arts marketing)이 주요 쟁점으로 다루어져 왔다. 이와 같은 연구동향 및 지식구조 분석을 토대로 이 연구는 보다 확장된 무용경영 연구의 관점을 제안하였다.