• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.07a
• /
• pp.513-516
• /
• 2022

본 논문에서는 텍스트 마이닝 기법을 이용하여 MZ 세대를 이해하는 키워드를 도출하고자 한다. MZ 세대의 비중이 높아지면서, MZ 세대를 분석하려고 하는 많은 연구들이 수행되고 있다. 이에 본 연구에서는 MZ 세대를 이해하기 위하여 네이트 판의 연령별 게시판 크롤링을 통해 빅데이터를 수집하였다. 그리고 텍스트 마이닝 기법을 활용하여 10대, 20대, 30대의 각각의 키워드를 도출할 수 있었다. 본 논문에서 도출된 키워드는 이는 MZ 세대를 이해하는데 중요한 키워드로 볼 수 있을 것이다. 향후 연구로는 MZ 세대와 기성 세대를 비교하기 위하여 추가 크롤링을 통해 세대 간 비교 연구를 수행하고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.07a
• /
• pp.369-370
• /
• 2023

새로운 연구를 시작하기 위해서는 과거의 연구 동향을 분석해야 한다. 이를 위해 많은 양의 과거 연구 데이터를 조사해야 하는데, 모든 데이터를 직접 분류하는 방법은 많은 시간과 노력이 필요하기 때문에 비효율적이며, 텍스트 마이닝 기법을 활용한 키워드분석만으로는 연구 동향을 이해하기에 어려움이 존재한다. 이러한 전통적인 키워드 추출 방법의 한계점을 보완하기 위해 본 논문에서는 텍스트 마이닝 기반 GPT 활용 기법을 제안한다. 본 연구에서는 특정 도메인에 대해 텍스트 마이닝 기법을 활용하여 키워드를 추출하고, 이러한 키워드를 해당 도메인의 데이터로 미세 조정(fine-tuning)된 GPT의 입력으로 사용한다. GPT 결과로 생성된 문장을 텍스트 마이닝으로 나온 결과와 비교 분석한다. 이를 통해 연구 분야의 동향 분석을 보다 쉽게 할 수 있을 것으로 기대된다.

• The Journal of Information Systems
• /
• v.32 no.3
• /
• pp.19-39
• /
• 2023

Purpose This study aims to identify user experience factors that can enhance both metaverse utilization and satisfaction based on the honeycomb model. For this we presented two research questions: first, what are the experience factors of metaverse users? Second, do metaverse user experience factors impact satisfaction? Design/methodology/approach To address these questions, a mixed-methodology approach is employed, including text mining techniques to analyze online reviews and quantitative econometric analysis to reveal the relationship between user experience factors and satisfaction. A total of 69,880 reviews and ratings data were collected. Findings The analysis revealed eight metaverse user experience factors: entertainment, operability, virtual reality, immersion, economic activity, visual performance, avatar, and sociality, all of which were found to have a positive impact on user satisfaction.

• International Journal of Advanced Culture Technology
• /
• v.12 no.2
• /
• pp.448-454
• /
• 2024

This study provides academic implications by considering trends of domestic research regarding therapy for sensory integration intervention based on vestibular-proprioceptive system. For the analysis of this study, text mining with the use of R program and social network analysis method have been used and 53 papers have been collected. In conclusion, this study presents significant results as it provided basic rehabilitation data for sensory integration intervention based on vestibular-proprioceptive system through new research methods by analyzing with big data method by proposing the results through visualization from seeking research trends of sensory integration intervention based on vestibular-proprioceptive system through text mining and social network analysis.

• Asia Marketing Journal
• /
• v.26 no.2
• /
• pp.104-128
• /
• 2024

More consumers are choosing pro-consumption for social change, but scholars know little about why and how consumers engage in pro-consumption behaviors. A newly emerged pro-consumption behavior called "Don-jjul," which appeared during the COVID-19 pandemic in South Korea, refers to compensating businesses that have engaged in altruistic actions by boosting their sales. This study used Latent Dirichlet Allocation (LDA) of topic modeling, sentiment analysis, and in-depth interviews to investigate the perceptions, motivations, and emotions regarding Don-jjul. As a result, the study revealed pro-consumers' perceptions of Don-jjul as "collective pro-consumption for contributing to social well-being." Don-jjul has two main motives: "supporting underdogs with difficulties" and "compensating good businesses economically." We also found two ambivalent emotions evoked by Don-jjul: "respect for good business owners" and "concerns regarding the misuse of Don-jjul." The results contribute to pro-consumption research for social well-being, providing business opportunities for retailers and CSR managers with a deep understanding of pro-consumers.

• International journal of advanced smart convergence
• /
• v.13 no.3
• /
• pp.300-305
• /
• 2024

Smart tourism enhances communication between tourists and residents, improves quality of life, increases the utilization of local tourism resources, and helps manage cities efficiently. This paper analyzes recent issues and trends in smart tourism, derives key factors for activating smart tourism based on the analyzed data, and conducts research on promoting smart tourism. Using smart tourism as a keyword, data was collected through Textom. The collection scope included a total of 33,588 pieces of data related to smart tourism over the past year, from May 1, 2023, to May 1, 2024. The data was analyzed using text mining and social network analysis techniques. Through this analysis, the paper suggests directions for the development of smart tourism, enabling the activation of local tourism and effective urban management.

• Journal of Information Technology Services
• /
• v.14 no.1
• /
• pp.237-249
• /
• 2015

The volume of unstructured text data generated by various social media has been increasing rapidly; therefore, use of text mining to support decision making has also been increasing. Especially, issue Clustering-determining a new relation with various issues through clustering-has gained attention from many researchers. However, traditional issue clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be discovered using traditional issue clustering methods, even if those issues are strongly related in other perspectives. Therefore, issue clustering that fits each of criteria needs to be performed by the perspective of analysis and the purpose of use. In this study, a multi-dimensional issue clustering is proposed to overcome the limitation of traditional issue clustering. We assert, specifically in this study, that issue clustering should be performed for a particular purpose. We analyze the results of applying our methodology to two specific perspectives on issue clustering, (i) consumers' interests, and (ii) related R&D terms.

• Journal of Information Technology Applications and Management
• /
• v.27 no.1
• /
• pp.59-73
• /
• 2020

There is a growing interest in convergence. National R & D is also providing various policies and institutional support to promote convergence research. Convergence research, however, does not clearly specify its characteristics at the academic and government levels. This research proceeds with the process of collecting, refining, analyzing, modeling, verifying and visualizing national R & D data through the National Science and Technology Information Service (NTIS). The method is to derive the convergence research characteristics and to derive through text mining, focusing on the unstructured information of national R & D project data. The study confirmed that there was a difference in perception between the definition of converged research and the research site. In order to improve this, the research suggested that convergence among research subjects, collaboration among research topics reflecting various backgrounds and characteristics of researchers, and analysis of characteristics of convergence research using information were suggested in the process of establishing convergence policy.

• Journal of Information Technology Services
• /
• v.17 no.1
• /
• pp.79-89
• /
• 2018

Sentiment analysis is a technique of text mining that extracts feelings of the person who wrote the sentence like movie review. The preliminary researches of sentiment analysis identify sentiments by using the dictionary which contains negative and positive words collected in advance. As researches on deep learning are actively carried out, sentiment analysis using deep learning model with morpheme or word unit has been done. However, this model has disadvantages in that the word dictionary varies according to the domain and the number of morphemes or words gets relatively larger than that of phonemes. Therefore, the size of the dictionary becomes large and the complexity of the model increases accordingly. We construct a sentiment analysis model using recurrent neural network by dividing input data into phoneme-level which is smaller than morpheme-level. To verify the performance, we use 30,000 movie reviews from the Korean biggest portal, Naver. Morpheme-level sentiment analysis model is also implemented and compared. As a result, the phoneme-level sentiment analysis model is superior to that of the morpheme-level, and in particular, the phoneme-level model using LSTM performs better than that of using GRU model. It is expected that Korean text processing based on a phoneme-level model can be applied to various text mining and language models.

• Journal of Korea Society of Industrial Information Systems
• /
• v.23 no.3
• /
• pp.87-95
• /
• 2018

Recently, with the technological development of artificial intelligence, related market is expanding rapidly. In the artificial intelligence technology field, which is still in the early stage but still expanding, it is important to reduce uncertainty about research direction and investment field. Therefore, this study examined technology trends using text mining and topic modeling among big data analysis methods and suggested trends of core technology and future growth potential. We hope that the results of this study will provide researchers with an understanding of artificial intelligence technology trends and new implications for future research directions.