• The Journal of the Korea Contents Association
• /
• v.19 no.10
• /
• pp.45-58
• /
• 2019

As modern people's daily lives are becoming more harsh in Korea, the so-called Kidults generation has appeared since a few years ago as adults have come back to their childhood sensibility and are exposed to various cultures online, and there are many people who have a hobby for collecting toys. However, as there is currently no formalized system for individual toy trade online, it is difficult to acquire expertise and share information with each other through a major portal site's $caf{\acute{e}}$, and is exposed to security or fraud while trading toys. In this paper, we design and implementation of a web-based toy trading system. Analyzing the advantages and disadvantages of the various trading and relay systems currently in use, it will provide opportunities for professional toy knowledge and information exchange to many users who have a hobby of collecting toys, and will greatly help vitalize the toy market through a secure and convenient trading environment between individuals.

• The Journal of the Korea Contents Association
• /
• v.22 no.4
• /
• pp.47-57
• /
• 2022

Newtro is gaining popularity recently as one of the media content genres. Newtro contents are not just a restoration, but are the creations that create new values by adapting to the current situation and appealing to the nostalgia for the past. A total of 254 people participated the online survey. Result showed that users prefer Newtro content that is in the 'moderate incongruity' state with moderate novelty, rather than content that is very new or too familiar. In addition, both 'personal nostalgia' from direct past experiences and 'vicarious nostalgia' from indirect experiences were found to have a positive effect on preference for Newtro content. Finally, it was confirmed that the image of Newtro contents affected the purchase intention of Newtro contents positively. But the attitude toward Newtro contents did not. Based on these results, this study suggested the implications for the differentiation strategy of Newtro contents.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.49 no.2
• /
• pp.61-73
• /
• 2021

This study aimed to identify the landscape perception and landscape characteristics of users by utilizing SNS data generated by their experiences. Therefore, how to recognize the main places and scenery appearing on the island, and what are the characteristics of the main scenery were analyzed using online text data and photo data. Text data are text mining and network structural analysis, while photographic data are landscape identification models and color analysis. As a result of the study, First, as a result of frequency analysis of Bakji·Banwol-do topics, we were able to derive keywords for local landscapes such as 'Purple Bridge', 'Doori Village', and location, behavior, and landscape images by analyzing them simultaneously. Second, the network structure analysis showed that the connection between key and undrawn keywords could be more specifically analyzed, indicating that creating landscapes using colors is affecting regional activation. Third, after analyzing the landscape identification model, it was found that artificial elements would be excluded to create preferred landscapes using the main targets of "Purple Bridge" and "Doori Village", and that it would be effective to set a view point of the sea and sky. Fourth, Bakji·Banwol-do were the first islands to be created under the theme of color, and the colors used in artificial facilities were similar to the surrounding environment, and were harmonized with contrasting lighting and saturation values. This study used online data uploaded directly by visitors in the landscape field to identify users' perceptions and objects of the landscape. Furthermore, the use of both text and photographic data to identify landscape recognition and characteristics is significant in that they can specifically identify which landscape and resources they prefer and perceive. In addition, the use of quantitative big data analysis and qualitative landscape identification models in identifying visitors' perceptions of local landscapes will help them understand the landscape more specifically through discussions based on results.

• Archives of design research
• /
• v.15 no.1
• /
• pp.143-152
• /
• 2002

As the Internet has been a daily instrument of our lives, the numbers of Internet users are increasing rapidly. Especially, we have to pay special attention to about rapid increasing of juvenile users. In the 1990's, Kids are growing up literally surrounded by new technologies and mu1timedia experiences. For these kids, most of the techno1ologies that we adults find surprising or even incredible are a part of their everyday landscape, a fact of life. Currently, only few of research and discussion has gone into understanding this field. And most of these web sites, set importance on furnishing information only. So educational characters of web are not manifested fully as well as children soon get board with learning with Internet so that feel difficulties in searching and accepting information. At this point, we must try to develop educational sites Not only to show information but also to offer a rich and entertaining time for kids while providing playful teaming and increased technological fluency. Fer this purpose, Web site should be all about combining play with learning. Site navigation should be easy and the pages load quickly. The page download time is also being considerable, which could send kids withy mouse-fingers looking for entertainment elsewhere. Everything about the site must have a familiar feel, uses adequate colors to be satisfied with the juveniles. Multimedia can help the communications in the websites. To maximize the educational effect, technological research and continues invest are need, in addition to usability test.

• Information Systems Review
• /
• v.22 no.2
• /
• pp.77-99
• /
• 2020

Smart speaker is a device that provides an interactive voice-based service that can search and use various information and contents such as music, calendar, weather, and merchandise using artificial intelligence. Since AI technology provides more sophisticated and optimized services to users by accumulating data, early smart speaker manufacturers tried to build a platform through aggressive marketing. However, the frequency of using smart speakers is less than once a month, accounting for more than one third of the total, and user satisfaction is only 49%. Accordingly, the necessity of strengthening the user experience of smart speakers has emerged in order to acquire a large number of users and to enable continuous use. Therefore, this study analyzes the user experience of the smart speaker and proposes a method for enhancing the user experience of the smart speaker. Based on the analysis results in two stages, we propose ways to enhance the user experience of smart speakers by model. The existing research on the user experience of the smart speaker was mainly conducted by survey and interview-based research, whereas this study collected the actual review data written by the user. Also, this study interpreted the analysis result based on the smart speaker user experience dimension. There is an academic significance in interpreting the text mining results by developing the smart speaker user experience dimension. Based on the results of this study, we can suggest strategies for enhancing the user experience to smart speaker manufacturers.

• Science of Emotion and Sensibility
• /
• v.18 no.1
• /
• pp.59-66
• /
• 2015

To date, contents' usability of most multimedia devices has been focused on developer not on user, which made difficult in solving the problems or fulfilling the needs while people using real system. Although user-centered UX and UI researches have been studied and have resulted in innovation in some part, it does not show great effect on usability as it is not easy to interpret human emotions and needs and to apply those to system. Usability is the matter on how deeply smart devices can interpret and analyze human mind not on how much functions and technologies are improved. This study aims to help with usability improvement based on user when people use smart devices in multimedia environment. We studied the interaction between human and contents by analyzing the effect of human emotions and personalities on preference and consumption of contents' type. This study was done by assuming that proper analysis on human emotions may increase user satisfaction on multimedia environment. We analyzed contents preference by gender and emotion. The results showed that there is significant relationship between 'Happy' emotion and 'Comedy Program' preference and men are more prefer it than women. However, it does not reveal any significant relationship between 'Sad' emotion and contents preferences but women are slightly more prefer 'Comedy Program' than men. This result supports the Zillmann's 'mood based management', which suggests that the needs for pleasant contents are revealed to relieve sadness when people are in a sad mood. In addition, our finding corresponds with Oliver's insistence on meeting all four factors, insight, meaningfulness, understanding and reflection, rather than just pleasure for more satisfaction. This study focused on temporary emotional factors and contents and additionally on effect of users' emotion, personality and preference on type of contents consumption. This relationship between emotions and contents study would suggest the better direction for developing smart devices with great contents usability and user satisfaction in the future.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.199-219
• /
• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.69-92
• /
• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Cartoon and Animation Studies
• /
• s.51
• /
• pp.361-390
• /
• 2018

Users of KakaoTalk emoticons use Kakao Talk emoticons as means of communicating their emotions in virtual space. Emotional state is represented by design element (auxiliary, color, form, motion) and storytelling element contained in emoticons. The purpose of this study is to investigate the factors of the storytelling and design elements of kakaoTalk emoticons and how they prefer the kakaoTalk emoticons as emotional expression means. In terms of storytelling, crocodiles, peaches, dogs, ducks, lions, moles, and rabbits were made up of ordinary fruits and animals. Most of the emoticons are composed of stories with unique personality, and each story has a complex one by one, which makes it easy for users to approach and use them. In terms of design, I used various auxiliary elements (flame, sweat, tears, runny nose, angry eyes, etc.) to express angry, sincere, nervous, begging, joy, and sadness. The color elements consisted of most of the warm color series with the unique colors (green, red, yellow, pink, white, black, brown, etc.) of emoticon characters regardless of feelings of joy, anger, sadness, pleasure. The form factor is composed of a round shape when expressing factors such as joy and sadness. On the other hand, when FRODO and NEO express sadness and anger, they represent the shape of a rectangle. The motion elements are horizontal, vertical, and oblique expressions of APPEACH, NEO, TUBE, and JAY-G, expressing emotional expressions of sadness, anger, and pleasure. APEACH, TUBE, MUZI & / Shows the dynamic impression of the oblique and the radiation / back / forward / rotation. The anger of TUBE and FRODO shows horizontal / vertical / diagonal and radial motion. As a result of this study, storytelling is structured in accordance with each emoticon character. In terms of design, auxiliary elements such as flame, sweat, and tears are represented by images. The color elements used the unique colors of the character series regardless of the difference of emotion. The form factor represented various movements for each emotion expression. These findings will contribute to the development of communication, emotional design and industrial aspects. Despite the significance of the above paper, I would like to point out that the analysis framework of the storytelling and the semiotic analysis of the supplementary elements are not considered as limitations of the study.