• Journal of Digital Convergence
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• v.19 no.10
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• pp.469-489
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• 2021

This study explores consumers' responses to socially assistive robotics by using text-mining method focusing on Companion Pets from Hasbro as it gives emotional support. We conducted text frequency analysis, LDA analysis using R programming. The key findings are 1)the most frequently used words the mimicry of living pets and the appearance of companion pets, 2)the five topics were derived from the LDA analysis and classified keywords in each topic split between positive and negative, 3)user, product, environment affect the interaction between consumer and companion pets, 4)consumers who have difficulty in cognition and physical conditions use companion pets to replace living pets. This study provides an understanding of consumer responses in companion pets and gives practical implications that may improve the efficacy of usage for consumers and understand the companion robot, which provides emotional support in COVID-19.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.103-116
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• 2020

This paper starts with the recognition of the problem of the need for a link between text-centered acting and body-centered acting. This study is focused on Brecht's theory of acting to overcome loss of presence by repetition which have been discussed many times by not only actors, but also acting educators. Brecht's acting theory has already been mentioned by many researchers as an alternative to conventional actor training. However, not many studies have been conducted on practical applicable methods. The purpose of this study is to provide the basis for the actual practice of Brecht acting and possibility that his acting theory can serve as a link between text and body-centered acting theory. As a research method, we first conduct theoretical considerations on the concepts and limitations of text-centered representational acting and body-centered post-drama acting. Then distinguish between text and body-centered acting tools among Brecht's epic theatre, to summarize the terms and concepts he uses and to identify the existing effects he reaches while acting. Finally, this paper proposes an teaching model that transforms and develops Brecht's acting theory through the writer's teaching experience. However, there are limitations in generalizing its effectiveness because this study is based on the writer's experience. We hope that further research will help the diversity of acting education by developing in-depth insights on Brecht acting theory and various models of acting teaching methods.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.11-21
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• 2022

All-solid-state batteries are one of the promising candidates for next-generation batteries and are drawing attention as a key component that will lead the future electric vehicle industry. This study analyzes 10,280 comments on Reddit, which is a global social media, in order to identify policy issues and public interest related to all-solid-state batteries from 2016 to 2021. Text mining such as frequency analysis, association rule analysis, and topic modeling, and sentiment analysis are applied to the collected global data to grasp global trends, compare them with the South Korean government's all-solid-state battery development strategy, and suggest policy directions for its national research and development. As a result, the overall sentiment toward all-solid-state battery issues was positive with 50.5% positive and 39.5% negative comments. In addition, as a result of analyzing detailed emotions, it was found that the public had trust and expectation for all-solid-state batteries. However, feelings of concern about unresolved problems coexisted. This study has an academic and practical contribution in that it presented a text mining analysis method for deriving key issues related to all-solid-state batteries, and a more comprehensive trend analysis by employing both a top-down approach based on government policy analysis and a bottom-up approach that analyzes public perception.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.35-49
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• 2024

Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.1-18
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• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Asia pacific journal of information systems
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• v.18 no.4
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• pp.105-130
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• 2008

Consumers of e-learning market differ from those of other markets in that they are replaced in a specific time scale. For example, e-learning contents aimed at highschool senior students cannot be consumed by a specific consumer over the designated period of time. Hence e-learning service providers need to attract new groups of students every year. Due to lack of information on products designed for continuously emerging consumers, the consumers face difficulties in making rational decisions in a short time period. Increased uncertainty of product purchase leads customers to herding behaviors to obtain information of the product from others and imitate them. Taking into consideration of these features of e-learning market, this study will focus on the online herding behavior in purchasing e-learning contents. There is no definite concept for e-learning. However, it is being discussed in a wide range of perspectives from educational engineering to management to e-business etc. Based upon the existing studies, we identify two main view-points regarding e-learning. The first defines e-learning as a concept that includes existing terminologies, such as CBT (Computer Based Training), WBT (Web Based Training), and IBT (Internet Based Training). In this view, e-learning utilizes IT in order to support professors and a part of or entire education systems. In the second perspective, e-learning is defined as the usage of Internet technology to deliver diverse intelligence and achievement enhancing solutions. In other words, only the educations that are done through the Internet and network can be classified as e-learning. We take the second definition of e-learning for our working definition. The main goal of this study is to investigate what factors affect consumer intention to purchase e-learning contents and to identify the differential impact of the factors between consumers with purchase experience and those without the experience. To accomplish the goal of this study, it focuses on herding behavior and perceived usefulness as antecedents to behavioral intention. The proposed research model in the study extends the Technology Acceptance Model by adding herding behavior and usability to take into account the unique characteristics of e-learning content market and e-learning systems use, respectively. The current study also includes consumer experience with e-learning content purchase because the previous experience is believed to affect purchasing intention when consumers buy experience goods or services. Previous studies on e-learning did not consider the characteristics of e-learning contents market and the differential impact of consumer experience on the relationship between the antecedents and behavioral intention, which is the target of this study. This study employs a survey method to empirically test the proposed research model. A survey questionnaire was developed and distributed to 629 informants. 528 responses were collected, which consist of potential customer group (n = 133) and experienced customer group (n = 395). The data were analyzed using PLS method, a structural equation modeling method. Overall, both herding behavior and perceived usefulness influence consumer intention to purchase e-learning contents. In detail, in the case of potential customer group, herding behavior has stronger effect on purchase intention than does perceived usefulness. However, in the case of shopping-experienced customer group, perceived usefulness has stronger effect than does herding behavior. In sum, the results of the analysis show that with regard to purchasing experience, perceived usefulness and herding behavior had differential effects upon the purchase of e-learning contents. As a follow-up analysis, the interaction effects of the number of purchase transaction and herding behavior/perceived usefulness on purchase intention were investigated. The results show that there are no interaction effects. This study contributes to the literature in a couple of ways. From a theoretical perspective, this study examined and showed evidence that the characteristics of e-learning market such as continuous renewal of consumers and thus high uncertainty and individual experiences are important factors to be considered when the purchase intention of e-learning content is studied. This study can be used as a basis for future studies on e-learning success. From a practical perspective, this study provides several important implications on what types of marketing strategies e-learning companies need to build. The bottom lines of these strategies include target group attraction, word-of-mouth management, enhancement of web site usability quality, etc. The limitations of this study are also discussed for future studies.

• The Journal of Information Systems
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• v.25 no.1
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• pp.1-23
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• 2016

Purpose Self-congruity deals with the effect of symbolic value-expressive attributes on consumer decision and behavior, which is the theoretical foundation of the "non-utilitarian destination positioning". Functional congruity refers to utilitarian evaluation of a product or service by consumers. In addition, recent years, social network services, especially mobile social network services have created many opportunities for e-WOM communication that enables consumers to share personal consumption related information anywhere at any time. Moreover, self-construal is a hot and popular topic that has been discussed in the field of modem psychology as well as in marketing area. This study aims to examine the moderating effect of self-construal on the relationship between self-congruity, functional congruity and tourists' positive electronic word of mouth (e-WOM). Design/methodology/approach In order to verify the hypotheses, we developed a questionnaire with 32 survey items. We measured all the items on a five-point Likert-type scale. We used Sojump.com to collect questionnaire and gathered 218 responses from whom have visited Korea before. After a pilot test, we analyzed the main survey data by using SPSS 20.0 and AMOS 18.0, and employed structural equation modeling to test the hypotheses. We first estimated the measurement model for its overall fit, reliability and validity through a confirmatory factor analysis and used common method bias test to make sure that whether measures are affected by common-method variance. Then we tested the hypotheses through the structural model and used regression analysis to measure moderating effect of self-construal. Findings The results reveal that the effect of self-congruity on tourists' positive e-WOM is stronger for tourists with an independent self-construal compared with those with interdependent self-construal. Moreover, it shows that the effect of functional congruity on tourists' positive e-WOM becomes salient when tourists' self-construal is primed to be interdependent rather than independent. We expect that the results of this study can provide important implications for academic and practical perspective.

• Asia pacific journal of information systems
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• v.20 no.1
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• pp.141-170
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• 2010

User created content (UCC) is created and shared by common users on line. From the user's perspective, the increase of UCCs has led to an expansion of alternative means of communications, while from the business perspective UCCs have formed an environment in which an abundant amount of new contents can be produced. Despite outward quantitative growth, however, many aspects of UCCs do not meet the expectations of general users in terms of quality, and this can be observed through pirated contents and user-copied contents. The purpose of this research is to investigate effective methods for fostering production of creative user-generated content. This study proposes two core elements, namely, reward and motivation, which are believed to enhance content creativity as well as the mediating factor and users' committement, which will be effective for bridging the increasing motivation and content creativity. Based on this perspective, this research takes an in-depth look at issues related to constructing the dimensions of reward and motivation in UCC services for creative content product, which are identified in three phases. First, three dimensions of rewards have been proposed: task dimension, social dimension, and organizational dimention. The task dimension rewards are related to the inherent characteristics of a task such as writing blog articles and pasting photos. Four concrete ways of providing task-related rewards in UCC environments are suggested in this study, which include skill variety, task significance, task identity, and autonomy. The social dimensioni rewards are related to the connected relationships among users. The organizational dimension consists of monetary payoff and recognition from others. Second, the two types of motivations are suggested to be affected by the diverse rewards schemes: intrinsic motivation and extrinsic motivation. Intrinsic motivation occurs when people create new UCC contents for its' own sake, whereas extrinsic motivation occurs when people create new contents for other purposes such as fame and money. Third, commitments are suggested to work as important mediating variables between motivation and content creativity. We believe commitments are especially important in online environments because they have been found to exert stronger impacts on the Internet users than other relevant factors do. Two types of commitments are suggested in this study: emotional commitment and continuity commitment. Finally, content creativity is proposed as the final dependent variable in this study. We provide a systematic method to measure the creativity of UCC content based on the prior studies in creativity measurement. The method includes expert evaluation of blog pages posted by the Internet users. In order to test the theoretical model of our study, 133 active blog users were recruited to participate in a group discussion as well as a survey. They were asked to fill out a questionnaire on their commitment, motivation and rewards of creating UCC contents. At the same time, their creativity was measured by independent experts using Torrance Tests of Creative Thinking. Finally, two independent users visited the study participants' blog pages and evaluated their content creativity using the Creative Products Semantic Scale. All the data were compiled and analyzed through structural equation modeling. We first conducted a confirmatory factor analysis to validate the measurement model of our research. It was found that measures used in our study satisfied the requirement of reliability, convergent validity as well as discriminant validity. Given the fact that our measurement model is valid and reliable, we proceeded to conduct a structural model analysis. The results indicated that all the variables in our model had higher than necessary explanatory powers in terms of R-square values. The study results identified several important reward shemes. First of all, skill variety, task importance, task identity, and automony were all found to have significant influences on the intrinsic motivation of creating UCC contents. Also, the relationship with other users was found to have strong influences upon both intrinsic and extrinsic motivation. Finally, the opportunity to get recognition for their UCC work was found to have a significant impact on the extrinsic motivation of UCC users. However, different from our expectation, monetary compensation was found not to have a significant impact on the extrinsic motivation. It was also found that commitment was an important mediating factor in UCC environment between motivation and content creativity. A more fully mediating model was found to have the highest explanation power compared to no-mediation or partially mediated models. This paper ends with implications of the study results. First, from the theoretical perspective this study proposes and empirically validates the commitment as an important mediating factor between motivation and content creativity. This result reflects the characteristics of online environment in which the UCC creation activities occur voluntarily. Second, from the practical perspective this study proposes several concrete reward factors that are germane to the UCC environment, and their effectiveness to the content creativity is estimated. In addition to the quantitive results of relative importance of the reward factrs, this study also proposes concrete ways to provide the rewards in the UCC environment based on the FGI data that are collected after our participants finish asnwering survey questions. Finally, from the methodological perspective, this study suggests and implements a way to measure the UCC content creativity independently from the content generators' creativity, which can be used later by future research on UCC creativity. In sum, this study proposes and validates important reward features and their relations to the motivation, commitment, and the content creativity in UCC environment, which is believed to be one of the most important factors for the success of UCC and Web 2.0. As such, this study can provide significant theoretical as well as practical bases for fostering creativity in UCC contents.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.09a
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• pp.125-153
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• 2005

It has been widely known that the seismic piezo-cone penetration test (SCPTU) is one of the most useful techniques for investigating the geotechnical characteristics including dynamic soil properties. As the practical applications in Korea, SCPTU was carried out at two sites in Busan and four sites in Incheon, which are mainly composed of alluvial or marine soil deposits. From the SCPTU waveform data obtained from the testing sites, the first arrival times of shear waves were and the corresponding time differences with depth were determined using the cross-over method, and the shear wave velocity profiles (VS) were derived based on the refracted ray path method based on Snell's law and similar to the trend of cone tip resistance (qt) profiles. In Incheon area, the testing depths of SCPTU were deeper than those of conventional down-hole seismic tests. Moreover, for the application of the conventional CPTU to earthquake engineering practices, the correlations between VS and CPTU data were deduced based on the SCPTU results. For the empirical evaluation of VS for all soils together with clays and sands which are classified unambiguously in this study by the soil behavior type classification Index (IC), the authors suggested the VS-CPTU data correlations expressed as a function of four parameters, qt, fs, $\sigma$, v0 and Bq, determined by multiple statistical regression modeling. Despite the incompatible strain levels of the down-hole seismic test during SCPTU and the conventional CPTU, it is shown that the VS-CPTU data correlations for all soils clays and sands suggested in this study is applicable to the preliminary estimation of VS for the Korean deposits and is more reliable than the previous correlations proposed by other researchers.