• Journal of Korea Society of Industrial Information Systems
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• v.22 no.5
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• pp.9-15
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• 2017

In the Digital Age, All Data used in the Internet is Digitized and Transmitted and Received Over a Communications Network. Therefore, it is Important to Transmit Data with Confidentiality and Integrity, Since Digital Data may be Tampered with and Tampered by Illegal users. Steganography is an Efficient Method for Ensuring Confidentiality and Integrity Together with Encryption Techniques. I Propose a Hangul Steganography Method that Inserts a Secret Message based on a Changing Insertion Position and a Changing Eojeol Size in a Cover Medium. Considering the Insertion Capacity of 3.35% and the File Size Change of 0.4% in Hangul Text Steganography, Experimental Results Show that the Jaro_score Value needs to be Maintained at 0.946.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.49-61
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• 2017

Wikipedia infoboxes have emerged as an important structured information source on the web. To compose infobox for an article, considerable amount of manual effort is required from an author. Due to this manual involvement, infobox suffers from inconsistency, data heterogeneity, incompleteness, schema drift etc. Prior works attempted to solve those problems by generating infobox automatically based on the corresponding article text. However, there are many articles in Wikipedia that do not have enough text content to generate infobox. In this paper, we present an automated approach to generate infobox for movie domain of Wikipedia by extracting information from several sources of the web instead of relying on article text only. The proposed methodology has been developed using semantic relations of article content and available semi-structured information of the web. It processes the article text through some classification processes to identify the template from the large pool of template list. Finally, it extracts the information for the corresponding template attributes from web and thus generates infobox. Through a comprehensive experimental evaluation the proposed scheme was demonstrated as an effective and efficient approach to generate Wikipedia infobox.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3725-3748
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• 2018

Text data distribution is often imbalanced. Imbalanced data is one of the challenges in text classification, as it leads to the loss of performance of classifiers. Many studies have been conducted so far in this regard. The proposed solutions are divided into several general categories, include sampling-based and algorithm-based methods. In recent studies, feature selection has also been considered as one of the solutions for the imbalance problem. In this paper, a novel one-sided feature selection known as probabilistic feature selection (PFS) was presented for imbalanced text classification. The PFS is a probabilistic method that is calculated using feature distribution. Compared to the similar methods, the PFS has more parameters. In order to evaluate the performance of the proposed method, the feature selection methods including Gini, MI, FAST and DFS were implemented. To assess the proposed method, the decision tree classifications such as C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per F-measure suggested that the proposed feature selection has significantly improved the performance of the classifiers.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.123-131
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• 2020

The text ranking algorithm is a representative method for keyword extraction, and its importance is emphasized highly. In this paper, we compare the performance of recent research and experiments with TF-IDF, SMART, INQUERY and CCA algorithms, which are used in text ranking algorithm.. After explaining each algorithm, we compare the performance of each algorithm based on the data collected from news and Twitter. Experimental results show that all of four algorithms can extract specific words from news data equally. However, in the case of Twitter, CCA has the best performance to extract specific words, and INQUERY shows the worst performance. We also analyze the accuracy of the algorithm through six comparison metrics. The experimental results present that CCA shows the best accuracy in the news data. In case of Twitter, TF-IDF and CCA show similar performance and demonstrate good performance.

• The Journal of Information Systems
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• v.30 no.3
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• pp.201-216
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• 2021

Purpose The purpose of this study is to analyze the trend of patent technology in textile materials using text mining methodology based on Dynamic Embedded Topic Model and Structural Topic Model. It is expected that this study will have positive impact on revitalizing and developing textile materials industry as finding out technology trends. Design/methodology/approach The data used in this study is 866 domestic patent text data in textile material from 1974 to 2020. In order to analyze technology trends from various aspect, Dynamic Embedded Topic Model and Structural Topic Model mechanism were used. The word embedding technique used in DETM is the GloVe technique. For Stable learning of topic modeling, amortized variational inference was performed based on the Recurrent Neural Network. Findings As a result of this analysis, it was found that 'manufacture' topics had the largest share among the six topics. Keyword trend analysis found the fact that natural and nanotechnology have recently been attracting attention. The metadata analysis results showed that manufacture technologies could have a high probability of patent registration in entire time series, but the analysis results in recent years showed that the trend of elasticity and safety technology is increasing.

• ETRI Journal
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• v.43 no.1
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• pp.95-108
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• 2021

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.

• The Journal of Information Systems
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• v.29 no.4
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• pp.137-151
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• 2020

Purpose Domestic asset management fintech companies are expected to grow by leaps and bounds along with the implementation of the "Data bills." Contrary to the market fever, however, academic research is insufficient. Therefore, we want to analyze user reviews of asset management fintech companies that are expected to grow significantly in the future to derive strengths and complementary points of services that have been provided, and analyze key elements of asset management fintech companies. Design/methodology/approach To analyze large amounts of review text data, this study applied text mining techniques. Bank Salad and Toss, domestic asset management application services, were selected for the study. To get the data, app reviews were crawled in the online app store and preprocessed using natural language processing techniques. Topic Modeling and Aspect-Sentiment Analysis were used as analysis methods. Findings According to the analysis results, this study was able to derive the elements that asset management fintech companies should have. As a result of Topic Modeling, 7 topics were derived from Bank Salad and Toss respectively. As a result, topics related to function and usage and topics on stability and marketing were extracted. Sentiment Analysis showed that users responded positively to function-related topics, but negatively to usage-related topics and stability topics. Through this, we were able to extract the key elements needed for asset management fintech companies.

• Fashion & Textile Research Journal
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• v.24 no.5
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• pp.545-556
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• 2022

To overcome the pandemic, a new strategy for innovation is in demand throughout the value chains of the fashion industry that emphasize the importance of fashion technology. Accordingly, as various viewpoints and fields of debate are unfolding to consider the direction of change led by fashion technology, it is necessary to make an active value judgment precedent by understanding the differences between various opinions. This study aims to derive keywords from fashion technology used during the pandemic, to infer the characteristics of each type of perspective and to understand their characteristics. For the research, this study combines text mining analysis and content analysis. Text mining analysis is used to find statistical patterns by collecting keywords from big data from online media, and content analysis is used to interpret the data qualitatively. After analyzing the results of this study, the following observations are made. First, the perspective of positive acceptance seeks to maximize the perception and sensory action of fashion through technology; this amplifies experience, an opportunity for innovation and efficiency. Second, critical vigilance highlights the side effects of radical changes in fashion technology, characterized by concerns about capital-centered polarization, threats to human rights, and infringement of creative thinking. Lastly, the perspective of gradual adoption is the gradual convergence of technologies, characterized by the pursuit of an appropriate balance.

• Journal of Distribution Science
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• v.22 no.3
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• pp.113-124
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• 2024

Purpose: This study is a quantitative study and analyzes the effect of evaluating the extreme and usefulness of product reviews on sales performance by using text mining techniques based on product review big data. We investigate whether the perceived helpfulness of product reviews serves as a mediating factor in the impact of product review extremity on sales performance. Research design, data and methodology: The analysis emphasizes customer interaction factors associated with both product review helpfulness and sales performance. Out of the 8.26 million Amazon product reviews in the book category collected by He & McAuley (2016), text mining using natural language processing methodology was performed on 300,000 product reviews, and the hypothesis was verified through hierarchical regression analysis. Results: The extremity of product reviews exhibited a negative impact on the evaluation of helpfulness. And the helpfulness played a mediating role between the extremity of product reviews and sales performance. Conclusion: Increased inclusion of extreme content in the product review's text correlates with a diminished evaluation of helpfulness. The evaluation of helpfulness exerts a negative mediating effect on sales performance. This study offers empirical insights for digital market distributors and sellers, contributing to the research field related to product reviews based on review ratings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4467-4488
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• 2014

The bibliometric analysis is widely used for understanding research domains, trends, and knowledge structures in a particular field. The analysis has majorly been used in the field of information science, and it is currently applied to other academic fields. This paper describes the analysis of academic literatures for classifying research domains and for suggesting empty research areas in the telecommunications policy. The application software is developed for retrieving Thomson Reuters' Web of Knowledge (WoK) data via web services. It also used for conducting text mining analysis from contents and citations of publications. We used three text mining techniques: the Keyword Extraction Algorithm (KEA) analysis, the co-occurrence analysis, and the citation analysis. Also, R software is used for visualizing the term frequencies and the co-occurrence network among publications. We found that policies related to social communication services, the distribution of telecommunications infrastructures, and more practical and data-driven analysis researches are conducted in a recent decade. The citation analysis results presented that the publications are generally received citations, but most of them did not receive high citations in the telecommunications policy. However, although recent publications did not receive high citations, the productivity of papers in terms of citations was increased in recent ten years compared to the researches before 2004. Also, the distribution methods of infrastructures, and the inequity and gap appeared as topics in important references. We proposed the necessity of new research domains since the analysis results implies that the decrease of political approaches for technical problems is an issue in past researches. Also, insufficient researches on policies for new technologies exist in the field of telecommunications. This research is significant in regard to the first bibliometric analysis with abstracts and citation data in telecommunications as well as the development of software which has functions of web services and text mining techniques. Further research will be conducted with Big Data techniques and more text mining techniques.