• Journal of Intelligence and Information Systems
• /
• v.22 no.1
• /
• pp.63-82
• /
• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.13 no.1
• /
• pp.135-142
• /
• 2013

Contour detection is important for many computer vision applications, such as shape discrimination and object recognition. In many cases, local luminance changes turn out to be stronger in textured areas than on object contours. Therefore, local edge features, which only look at a small neighborhood of each pixel, cannot be reliable indicators of the presence of a contour, and some global analysis is needed. The novelty of this operator is that dilation is limited to Deluanary triangular. An efficient implementation is presented. The grouping algorithm is then embedded in a multi-threshold contour detector. At each threshold level, small groups of edges are removed, and contours are completed by means of a generalized reconstruction from markers. Both qualitative and quantitative comparison with existing approaches prove the superiority of the proposed contour detector in terms of larger amount of suppressed texture and more effective detection of low-contrast contour.

• Korean Journal of Cognitive Science
• /
• v.17 no.4
• /
• pp.303-321
• /
• 2006

According to Harris'(1966) distributional hypothesis, understanding the meaning of a word is thought to be dependent on its context. Under this hypothesis about human language ability, this paper proposes a computational model for native speaker's language processing mechanism concerning word sense disambiguation, based on two sets of experiments. Among the three computational models discussed in this paper, namely, the logic model, the probabilistic model, and the probabilistic inference model, the experiment shows that the logic model is first applied fer semantic disambiguation of the key word. Nexr, if the logic model fails to apply, then the probabilistic model becomes most relevant. The three models were also compared with the test results in terms of Pearson correlation coefficient value. It turns out that the logic model best explains the human decision behaviour on the ambiguous words, and the probabilistic inference model tomes next. The experiment consists of two pans; one involves 30 sentences extracted from 1 million graphic-word corpus, and the result shows the agreement rate anong native speakers is at 98% in terms of word sense disambiguation. The other pm of the experiment, which was designed to exclude the logic model effect, is composed of 50 cleft sentences.

• Journal of Digital Convergence
• /
• v.15 no.8
• /
• pp.501-509
• /
• 2017

The purpose of this study was to review the effects of the physical fatigue induced by overtraining on cognitive information processing and emotional change of athletes by neurophysiologic study. And to provide basic data for the athletes and coaches in the sports field to improve understanding of the overtraining symptoms from the brain physiological point of view. We reviewed the domestic and international academic materials and research reports published in the last 20 years using electronic databases and examined the literature that is consistent with the purpose of this study among the retrieved documents. Based on the reviewed literature, we examined the cognitive information processing and emotional change of the athlete according to the training, and overtraining can accumulate the fatigue of the body, which can cause severe side effects in terms of cognitive ability and emotional ability of athletes as well as deterioration of performance. The study using P300 and EEG cerebral hemispheric asymmetry difference indices also confirmed the possibility of a new approach to understand psychological and physiological effects of overtraining on information processing and emotional changes.

• Archives of design research
• /
• v.11 no.1
• /
• pp.173-184
• /
• 1998

This study deals with the visual quality in the future urban landscape and architectural environment, and as such, aims to identify a scientific and objective aesthetic and visual quality from the perspective of empirical aesthetics. The empirical aesthetics provides a framework that can be utilized in understanding human perception, consciousness, and behavior and a way to categorize the visual quality and to explain and predict its effect. The study examines various theories on environmental perception, cognition, and some new approaches to environmental aesthetics, and tries to present aesthetic properties that can be applied to environmental design. First, the aesthetic experience in visual perception can be defined as a combined effect of psychobiological properties and human activity, i.e. an interaction between the formal and symbolic signs in environment and the conceptual framework of man. The effect of visual quality differs and varies a great deal, depending on the sociocultural, personal and collective value system, so it is hard to define it in absolute terms. Second, the impact of visual quality and its aesthetic effect has to do with pleasure, preference, the aptitude for survival, and self regulation. Third, aesthetics is one of the areas that can benefit a great deal from an interdisciplinary approach. and an empirical study such as this can be used as a basis for design, planning, and evaluation.

• Journal of Internet Computing and Services
• /
• v.20 no.5
• /
• pp.87-94
• /
• 2019

In this paper, the method of analyzing the unformatted data of consumers accumulated on social networks in the era of the Fourth Industrial Revolution by utilizing data from the service design and social psychology aspects was proposed. First, the fandom phenomenon, which shows subjective and collective behavior in a space on a social network rather than physical space, was defined from a data service perspective. The fandom model has been transformed into a collective level of customer Persona that has been analyzed at a personal level in traditional service design, and social network analysis that analyzes consumers' big data has been presented as an efficient way to pattern and visually analyze it. Consumer data collected through social leasing were pre-processed by column based on correlation, stability, missing, and ID-ness. Based on the above data, the company's brand strategy was divided into active and passive interventions and the effect of this strategic attitude on the growth direction of the consumer's fandom community was analyzed. To this end, the fandom model of consumers was proposed by dividing it into four strategies that the brand strategy had: stand-alone, decentralized, integrated and centralized, and the fandom shape of consumers was proposed as a growth model analysis technique that analyzes changes over time.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.3
• /
• pp.51-64
• /
• 2021

Among global K-pop fandoms Chinese fandom is a longtime and very strong consumer power in the K-pop industry due to its adjacent geographical location and low cultural barriers. Yet, there is a significant lack of research on Chinese K-pop fandom; most of what is available is from the producers' points of view. Research that explores the position and experience of Chinese fans, who are both mass audience and consumers, is slow to emerge. Thus, there is a need for systematic and careful study of Chinese K-pop fandom. In this research, we conducted a phenomenological study of the experience of Chinese fans of K-pop culture. We conducted participatory observation and in-depth interviews (including a FGI) with Chinese superintendents and active participants in Chinese K-pop fan clubs and fan pages. We then coded the data, following the theory of fandom developed by scholars such as Fiske (1992), and analyzed the phenomenon of Chinese K-pop fandom from the frame of the overall societal environment. We considered the psychological and behavioral characteristics of Chinese fans and the influences they receive from their turbulent social environments. We also considered the expectations of and opinions on the development of K-pop culture from their perspective. Based on our findings, the significant role and influence of independent fans and interdependent fandom in the development of K-pop culture and industry are identified. Finally, we emphasize that the role as facilitators of K-pop agencies is critical in the establishment of communication and trust between fans, K-pop artists, and the agencies.

• Korean Journal of Culture and Social Issue
• /
• v.23 no.4
• /
• pp.451-473
• /
• 2017

This study explores the aftermath of state violence and the meaning of its healing by analyzing testimony therapy designed to deal with trauma of the victims of May 18 democratization movement("May 18"). In order to have a point of view that May 18 had catastrophic impacts on many individuals and communities and to observe the after-effects of such event to individuals' lives, this study analyzed the contents of testimony therapy sessions of four people who were tortured, arrested and detained after their participating in May 18 in 1980. The experiences of those participants with their testimony therapy and the interactions they had with the audience during the sessions made sure what are the key factors to healing the trauma from state violence. Nineteen topics were drawn from analyzing the statements made in the therapy process, and out of them, seven subjects below were chosen as the most significant; 'repetitive pain', 'social and economic damage', 'isolation and disconnection', 'transition of suffering to other generations', and 'Safety', 'healing through connections', 'obligation as a survivor'. Based on its findings, this study also examined the characteristics of trauma caused by state violence and made suggestions for healing such trauma.

• Journal of the Korea Society for Simulation
• /
• v.18 no.4
• /
• pp.9-20
• /
• 2009

Navigation system providing route-guidance and traffic information is one of the most widely used driver-support system these days. Most of the navigation system is based on the 2D map paradigm so the information is ed and encoded from the real world. As a result it imposes a cognitive burden to the driver to interpret and translate the ed information to real world information. As a new concept of navigation system, augmented-reality navigation system (AR navigation) is suggested recently. It provides navigational guidance by imposing graphical information on real image captured by camera mounted on a vehicle in real-time. The ultimate goal of navigation system is to assist the driving task with least driving workload whether it is based on the abstracted graphic paradigm or realistic image paradigm. In this paper, we describe the comparative studies on how map navigation and AR navigation affect for driving tasks by experimental research. From the result of this research we obtained a basic knowledge about the two paradigms of navigation systems. On the basis of this knowledge, we are going to find the optimal design of navigation system supporting driving task most effectively, by analyzing characteristics of driving tasks and navigational information from the human-vehicle interface point of view.

• Economic Analysis
• /
• v.27 no.1
• /
• pp.67-90
• /
• 2021

This study found an interesting fact that the nonlinear relationship structure between volatility and trading volume changed before and after the COVID-19 pandemic according to empirical analysis using Bitcoin (BTC) market data that sensitively reflects investors' trading behavior. That is, their relationship appeared positive (+) in a stable market state before COVID-19 pandemic, as in theory based on the information flow paradigm. In a state under severe market stress due to COVID-19 pandemic, however, their dependence structure changed and even negative (-). This can be seen as a consequence of increased market stress caused by COVID-19 pandemics from a behavioral economics perspective, resulting in structural changes in the asset market and a significant impact on the nonlinear dependence of volatility and trading volume (in particular, their dependence at extreme quantiles). Hence, it should be recognized that in addition to information flows, psychological phenomena such as behavioral biases or herd behavior, which are closely related to market stress, can be a key in changing their dependence structure. For empirical analysis, this study performs a test of Ross (2015) for detecting a structural change, and proposes a Copula Regression Quantiles (CRQ) approach that can identify their nonlinear relationship structure and the asymmetric dependence in their distribution tails without the assumption of i.i.d. random variable. In addition, it was confirmed that when the relationship between their extreme values was analyzed by linear models, incorrect results could be derived due to model specification errors.