• Journal of Korea Multimedia Society
• /
• v.18 no.11
• /
• pp.1391-1399
• /
• 2015

In recent times, studies sentiment analysis are being actively conducted by implementing natural language processing technologies for analyzing subjective data such as opinions and attitudes of users expressed on the Web, blogs, and social networking services (SNSs). Conventionally, to classify the sentiments in texts, most studies determine positive/negative/neutral sentiments by assigning polarity values for sentiment vocabulary using sentiment lexicons. However, in this study, sentiments are classified based on Thayer's model, which is psychologically defined, unlike the polarity classification used in opinion mining. In this paper, as a method for classifying the sentiments, sentiment categories are proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying sentiment categories to user posts, sentiments can be determined through the similarity measurement between the sentiment adjective candidates and the sentiment keywords. The test results of the proposed method show that the average accuracy rate for all the sentiment categories was 90.7%, which indicates good performance. If a sentiment classification system with a large capacity is prepared using the proposed method, then it is expected that sentiment analysis in various fields will be possible, such as for determining social phenomena through SNS.

• Journal of the HCI Society of Korea
• /
• v.1 no.1
• /
• pp.73-80
• /
• 2006

Mitologia is about visualizing relations of information in user-oriented method. Most information given in life has invisible relations with each other. By analyzing the common characters and relations of information, we can not only measure the importance of the information but also grasp the overall properties of the information. Especially human relations are the major concerns of social network having several visualization methodologies shown by analyzing relations of each individual in society. We applied social network theory to grasp relationships between characters in Greek mythology representing a limited society. But the current tools of social network analysis have limits that they show the information one-sided way because of the ignorance of user-oriented design. Mitologia attempts to suggest the visual structure model more effective and easy to understand in analyzing data. We extracted connections among myth characters by evaluating classes, frequencies of appearance and emotional links they have. And we raised the understanding of users with furnishing the proper interaction to the information. The initial interface offers 4 kinds of indexes helping to access character nodes easily, while zoom-in function can be used for the detailed relations. The Zoom-in is quite different from usual filtering methods. It makes the irrelative information invisible so that users can find out the characters' relation more easily and quickly. This project suggests the layout to show overall information relationships and the appropriate interactions to present detailed information at the same time.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.53-77
• /
• 2015

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.

• Information Systems Review
• /
• v.24 no.1
• /
• pp.1-19
• /
• 2022

Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

• The Journal of the Korea Contents Association
• /
• v.20 no.9
• /
• pp.271-284
• /
• 2020

Good reviews that Half-Life: Alyx received created a turning point for the vitalization of the VR market with an uncertain future due to the absence of a revenue model together with the increased sales of VR equipment. Based on previous studies, this study categorized the characteristics of VR games according to graphs, first-person system, interface, controller, interaction technology, sound and stories. The analysis results show that the seven elements corresponded with "interactions" and "virtual images," which represent the completion level of games as part of VR components, and were connected to one another, which led to the proposal of an upper-rank concept "presence" to put them together. "Immersiveness," which represents users' emotions as part of VR components, was analyzed with the immersion theory to assign tasks of proper difficulty level for users' abilities. In the research process, the Delphi technique and FGI were administered to a panel of 15 experts to ensure objectivity. Finally, "presence" and "immersiveness" are characteristics in proportion to each other and can be valid concepts in future analysis of VR games as well as Half-Life: Alyx, which led to the proposal a new concept framework called 'presence effects' by combining the two words.

• Archives of design research
• /
• v.17 no.1
• /
• pp.119-126
• /
• 2004

MMO RPG Online game as a new means of human computer interaction has been made rapid progress. Even though many kinds of online game have published, few games are being well received by its user. So we have a problem to be solved what is the fundamentals to attract game users. I will apply a game theory of the Roger Caillois who lived as an typical french thinker. Although Roger Caillois had never experienced a computer game since he died at 1978, his game theory was acknowledged that a game can be classified into four sections to grab the human's interest related with culture. Four sections of game were consisted of Ilinx, Mimicry, Alea, and Agon. The competition with people in game is essential to "Agon" that which stands on the basis of a pride when one is definitely superior to the others. In same way, "Alea" is that which stands on the basis of fortune, Mimicry is that which stands on the basis of simulation, Ilinx is that which stands on the basis of dizzy. In conclusion, I will explain that a game theory of the Roger Caillois is useful tool for deveoper to understand game fundamentals.d game fundamentals.

• Journal of the Architectural Institute of Korea Planning & Design
• /
• v.34 no.12
• /
• pp.85-94
• /
• 2018

The purpose of this paper was to propose a model that recognizes potential users' emotional response toward design by classifying Electroencephalography(EEG). Studies in neuroscience and psychology have made an effort to recognize subjects' emotional response by analyzing EEG data. And this approach has been adopted in design since it is critical to monitor users' subjective response in the preface of design. Moreover, the building design process cannot be reversed after construction, recognizing clients' affection toward design alternatives plays important role. An experiment was conducted to record subjects' EEG data while they view their most/least liked images of small-house designs selected by them among the eight given images. After the recording, a subjective questionnaire, PANAS, was distributed to the subjects in order to describe their own affection score in quantitative way. Google TensorFlow was used to build and train the model. Dataset for model training and testing consist of feature columns for recorded EEG data and labels for the questionnaire results. After training and testing, the measured accuracy of the model was 0.975 which was higher than the other machine learning based classification methods. The proposed model may suggest one quantitative way of evaluating design alternatives. In addition, this method may support designer while designing the facilities for people like disabled or children who are not able to express their own feelings toward alternatives.

• Journal of Internet Computing and Services
• /
• v.17 no.3
• /
• pp.107-113
• /
• 2016

Following the recent advancement in the use of social networks, a vast amount of different online reviews is created. These variable online reviews which provide feedback data of contents' are being used as sources of valuable information to both contents' users and providers. With the increasing importance of online reviews, studies on opinion mining which analyzes online reviews to extract opinions or evaluations, attitudes and emotions of the writer have been on the increase. However, previous sentiment analysis techniques of opinion-mining focus only on the classification of reviews into positive or negative classes but does not include detailed information analysis of the user's satisfaction or sentiment grounds. Also, previous designs of the sentiment analysis technique only applied to one content domain that is, either product or movie, and could not be applied to other contents from a different domain. This paper suggests a sentiment analysis technique that can analyze detailed satisfaction of online reviews and extract detailed information of the satisfaction level. The proposed technique can analyze not only one domain of contents but also a variety of contents that are not from the same domain. In addition, we design a system based on Hadoop to process vast amounts of data quickly and efficiently. Through our proposed system, both users and contents' providers will be able to receive feedback information more clearly and in detail. Consequently, potential users who will use the content can make effective decisions and contents' providers can quickly apply the users' responses when developing marketing strategy as opposed to the old methods of using surveys. Moreover, the system is expected to be used practically in various fields that require user comments.

• Journal of Digital Convergence
• /
• v.19 no.10
• /
• pp.469-489
• /
• 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 Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.71-89
• /
• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.