• Journal of Information Technology Services
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• v.15 no.3
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• pp.71-95
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• 2016

In recent years, text mining has been used to extract meaningful insights from the large volume of unstructured text data sets of various domains. As one of the most representative text mining applications, topic modeling has been widely used to extract main topics in the form of a set of keywords extracted from a large collection of documents. In general, topic modeling is performed according to the weighted frequency of words in a document corpus. However, general topic modeling cannot discover the relation between documents if the documents share only a few terms, although the documents are in fact strongly related from a particular perspective. For instance, a document about "sexual offense" and another document about "silver industry for aged persons" might not be classified into the same topic because they may not share many key terms. However, these two documents can be strongly related from the R&D perspective because some technologies, such as "RF Tag," "CCTV," and "Heart Rate Sensor," are core components of both "sexual offense" and "silver industry." Thus, in this study, we attempted to discover the differences between the results of general topic modeling and R&D perspective topic modeling. Furthermore, we package social issues from the R&D perspective and present a prototype system, which provides a package of news articles for each R&D issue. Finally, we analyze the quality of R&D perspective topic modeling and provide the results of inter- and intra-topic analysis.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.4
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• pp.19-27
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• 2023

In this study, a total of 2,584 domestic research papers with the keywords "Aviation Safety" and "Aviation Accidents" were subjected to Text Mining analysis. Various text mining techniques, including keyword frequency analysis, word correlation analysis, network analysis, and topic modeling, were applied to examine the research trends in the field of aviation safety. The results revealed a significant increase in research using the keyword "Aviation Safety" since 2015, with over 300 papers published annually. Through keyword frequency analysis, it was observed that "Aircraft" was the most frequently mentioned term, followed by "Drones" and "Unmanned Aircraft." Phi coefficients were calculated for words closely related to "Aircraft," "Aviation," "Drones," and "Safety." Furthermore, topic modeling was employed to identify 12 distinct topics in the field of aviation safety and aviation accidents, allowing for an in-depth exploration of research trends.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.471-492
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• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.40-56
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• 2022

This paper aims to implement a topic modeling technique for extracting the topics of online discussions among library professionals in India. Topic modeling is the established text mining technique popularly used for modeling text data from Twitter, Facebook, Yelp, and other social media platforms. The present study modeled the online discussions of Library and Information Science (LIS) professionals posted on Lis Links. The text data of these posts was extracted using a program written in R using the package "rvest." The data was pre-processed to remove blank posts, posts having text in non-English fonts, punctuation, URLs, emails, etc. Topic modeling with the Latent Dirichlet Allocation algorithm was applied to the pre-processed corpus to identify each topic associated with the posts. The frequency analysis of the occurrence of words in the text corpus was calculated. The results found that the most frequent words included: library, information, university, librarian, book, professional, science, research, paper, question, answer, and management. This shows that the LIS professionals actively discussed exams, research, and library operations on the forum of Lis Links. The study categorized the online discussions on Lis Links into ten topics, i.e. "LIS Recruitment," "LIS Issues," "Other Discussion," "LIS Education," "LIS Research," "LIS Exams," "General Information related to Library," "LIS Admission," "Library and Professional Activities," and "Information Communication Technology (ICT)." It was found that the majority of the posts belonged to "LIS Exam," followed by "Other Discussions" and "General Information related to the Library."

• Journal of Information Technology Services
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• v.18 no.2
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• pp.123-141
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• 2019

The goal of this study is to review the major research trend on the convergence studies of AI and healthcare technologies. For the study, 15,260 English articles on AI and healthcare related topics were collected from Scopus for 55 years from 1963, and text mining techniques were conducted. As a result, seven key research topics were defined : "AI for Clinical Decision Support System (CDSS)", "AI for Medical Image", "Internet of Healthcare Things (IoHT)", "Big Data Analytics in Healthcare", "Medical Robotics", "Blockchain in Healthcare", and "Evidence Based Medicine (EBM)". The result of this study can be utilized to set up and develop the appropriate healthcare R&D strategies for the researchers and government. In this study, text mining techniques such as Text Analysis, Frequency Analysis, Topic Modeling on LDA (Latent Dirichlet Allocation), Word Cloud, and Ego Network Analysis were conducted.

• Women's Health Nursing
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• v.29 no.2
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• pp.128-136
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• 2023

Purpose: Topic modeling is a text mining technique that extracts concepts from textual data and uncovers semantic structures and potential knowledge frameworks within context. This study aimed to identify major keywords and network structures for each major topic to discern research trends in women's health nursing published in the Korean Journal of Women Health Nursing (KJWHN) using text network analysis and topic modeling. Methods: The study targeted papers with English abstracts among 373 articles published in KJWHN from January 2011 to December 2021. Text network analysis and topic modeling were employed, and the analysis consisted of five steps: (1) data collection, (2) word extraction and refinement, (3) extraction of keywords and creation of networks, (4) network centrality analysis and key topic selection, and (5) topic modeling. Results: Six major keywords, each corresponding to a topic, were extracted through topic modeling analysis: "gynecologic neoplasms," "menopausal health," "health behavior," "infertility," "women's health in transition," and "nursing education for women." Conclusion: The latent topics from the target studies primarily focused on the health of women across all age groups. Research related to women's health is evolving with changing times and warrants further progress in the future. Future research on women's health nursing should explore various topics that reflect changes in social trends, and research methods should be diversified accordingly.

• ETRI Journal
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• v.33 no.6
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• pp.914-923
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• 2011

This paper concerns robust and reliable speaker model training for text-independent speaker verification. The baseline speaker modeling approach is the Gaussian mixture model (GMM). In text-independent speaker verification, the amount of speech data may be different for speakers. However, we still wish the modeling approach to perform equally well for all speakers. Besides, the modeling technique must be least vulnerable against unseen data. A traditional approach for GMM training is expectation maximization (EM) method, which is known for its overfitting problem and its weakness in handling insufficient training data. To tackle these problems, variational approximation is proposed. Variational approaches are known to be robust against overtraining and data insufficiency. We evaluated the proposed approach on two different databases, namely KING and TFarsdat. The experiments show that the proposed approach improves the performance on TFarsdat and KING databases by 0.56% and 4.81%, respectively. Also, the experiments show that the variationally optimized GMM is more robust against noise and the verification error rate in noisy environments for TFarsdat dataset decreases by 1.52%.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.183-190
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• 2019

In the era of the Fourth Industrial Revolution, digital transformation, which means changes in all industrial structures, politics, economics and society as well as IT technology, is an important issue. It is difficult to know which research topic is being studied because digital transformation is being studied in various fields. Convergence research is possible because a research topic is studied in various fields such as computer science area and Decision science area. However, it is difficult to know the specific research status of the research topic. In this study, eight research topics were derived using the topic modeling technique of text mining for abstract of academic literature and the trend of each topic was analyzed. We also proposed to create a Topic-Word Proportions Table in the LDA based Topic modeling process to predict the topic of new literature. The results of this study are expected to contribute to advanced convergence research on topic of digital transformation. It is expected that the literature related to each research topic will be grasped and contribute to the design of a new convergence research.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.701-713
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• 2023

Purpose: The primary purpose of this study is to integrate text mining and Quality Function Deployment (QFD) to automatically extract valuable information from customer reviews, thereby establishing a QFD frame- work to confirm genuine customer needs for New Product Development (NPD). Methods: Our approach combines text mining and QFD through topic modeling and sentiment analysis on a large data set of 56,873 customer reviews from Zappos.com, spanning five running shoe brands. This process objectively identifies customer requirements, establishes priorities, and assesses competitive strengths. Results: Through the analysis of customer reviews, the study successfully extracts customer requirements and translates customer experience insights and emotions into quantifiable indicators of competitiveness. Conclusion: The findings obtained from this research offer essential design guidance for new product develop- ment endeavors. Importantly, the significance of these results extends beyond the running shoe industry, presenting broad and promising applications across diverse sectors.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.396-404
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• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.