• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.8
• /
• pp.2222-2240
• /
• 2023

Brand fandom refers to a collection of consumers with strong emotions toward a brand. Studying the dynamics of brand fandom can help brands understand which services or strategies influence their consumers to become a part of brand fandom. However, existing literature on fandom in the last three decades has mainly used qualitative methods, and there is still a lack of research on fandom using quantitative methods. Specifically, previous studies lack a framework for locating fandoms from online textual data and analyzing their dynamics. This study proposes a framework for exploring brand fandom dynamics based on online textual data. This framework consists of four phases based on the design thinking model: Preparing Data, Defining Fandom Categories, Generating Fandom Dynamics, and Analyzing Fandom Dynamics. This framework uses techniques such as social network analysis and process mining, combined with brand personality theory. We demonstrate the applicability of this framework using case studies of two Korean home appliance brands. The dataset contains 14,593 posts by consumers in 374 online communities. The results show that the proposed framework can analyze brand fandom dynamics using textual customer data. Our study contributes to the interdisciplinary research at the intersection of data-driven service design and consumer culture quantification.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.1
• /
• pp.24-30
• /
• 2019

Modern people argue that mental health care is necessary because of various factors such as unstable income and conflict with others. Recently, equipments capable of measuring electrocardiogram (ECG) in wearable equipment have been widely used. In the case of overseas, it can be seen as a medical assistant [14]. By using such functions, studies are being conducted to distinguish representative emotions (joy, sadness, anger, etc.) with objective values. However, most studies are increasing accuracy by collecting complex bio-signals in a limited environment. Therefore, we examine the factors that have the greatest influence on the change and discrimination of biometric information on each stimulus.

• ETRI Journal
• /
• v.37 no.6
• /
• pp.1199-1210
• /
• 2015

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated two-phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components - multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.

• International journal of advanced smart convergence
• /
• v.11 no.2
• /
• pp.95-101
• /
• 2022

The development of the 4th industry has increased social media, and the rise of COVID-19 has stimulated non-face-to-face services. People's consumption patterns are also changing a lot due to non-face-to-face services. In this paper, food content keywords are derived through social network-based semantic network analysis, emotions are analyzed, and keywords applied to food recommendation platforms are input. We collected food, influencer, and corona keyword analysis data through Textom. A lot of research has been done through online reviews of existing influencer content. However, there is a lack of research on keyword sentiment analysis provided by influencers rather than consumers and research perspectives. This paper uploads language and topics derived through online reviews of existing publications and subscribers, and goes beyond the limits used in marketing methods. By analyzing keywords that influencers suggest when uploading content, you can apply data that applies them to food recommendation platforms and applications.

• Journal of Digital Convergence
• /
• v.17 no.2
• /
• pp.463-473
• /
• 2019

This study aimed to investigate the relationship between soccer games of Korea vs. Japan and emotions of anti-Japan and anti-Korea, empirically. For that, this study selected 2,400 comments from Naver and 5CH where people could write their SNS comments on EAFF E-1 football championship 2017. The study results got by frequency analysis and one-way ANOVA were as follows. First, Korean showed amity with own team and hostility to the opponents, and stronger hostility toward Japan. Japanese showed hostility to own team, and it was especially strong when vs. Korea. Second, Korean showed stronger hostility toward Japan than others. Japanese showed stronger hostility to own team when vs Korea. From those results, this study could conclude that soccer games of Korea vs. Japan could be a field to express those emotions rather than effect on the emotions of anti-Korea and anti-Japan. By the empirical method of this study on the emotions of anti-Japan and anti-Korea unlike advance studies, this could receive favorable evaluation.

• Journal of Digital Convergence
• /
• v.13 no.2
• /
• pp.227-235
• /
• 2015

The aim of this study was to identify the type of occupational balance of college students and analyze the differences between positive or negative emotions according to degree of flow. The Experience Sampling Methods (ESM) were conducted for 12 college students using smart phones. As a result, in activities of daily living group, apathy showed the highest percentage (34%) and in over work group, flow showed the highest percentage (41%). In occupational balance group, frequency of labor and leisure activities was high but boredom was experience a lot in leisure activities. When the students experienced flow in main activities, mean score of positive emotions showed the highest value. Through this study, we identified the differences of flow experience in occupational performance based on type of occupational balance and confirmed improvement of positive emotions in activities experienced flow.

• Journal of Industrial Convergence
• /
• v.18 no.3
• /
• pp.117-124
• /
• 2020

In addition to education, the effects of color on humans have been studied in various areas such as human emotions, behaviors, and relationships between bodies. This study is based on the assumption that the color expression of University students can be observed to understand the inner and inclinations of University students, and that color expression activities can be a means of psychological expression, self-expression, and emotional relief. This study is aimed to find how much the color mind expression program effects the psychological mind state of University students. As a result of the study, the color mind expression program influenced the expression of inner feelings and emotions of University students, and had a positive effect on color psychology and utilization. In addition, overall, the color mind expression program was effective in positive and psychological recovery of negative emotions, such as being able to honestly express the difficulties felt in the process of preparing for employment.

• Journal of Digital Convergence
• /
• v.18 no.3
• /
• pp.111-121
• /
• 2020

The purpose of this study is to examine the satisfaction of consumers using the mobile navigation service which is continuously growing. This study examined variables that affect the service use behaviors and satisfaction of 1200 consumers using service. According to the results of the study, consumers used the service 1-2 or 3-4 times per week, more frequently used in the lunch time(weekends) and in the morning(weekdays). The core functional dimension was shown to have the largest effect on satisfaction with the service. Among consumption emotions, positive emotions had effects in the positive direction while negative emotions had effects in the negative direction, and the effect of innovativeness was not significant. Through the foregoing, the use behavior and the formation of satisfaction could be analyzed and the results of this study can be used in establishing service strategies later and can be also used as basic data of the navigation industry.

• Journal of Internet of Things and Convergence
• /
• v.9 no.6
• /
• pp.37-44
• /
• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.3
• /
• pp.75-82
• /
• 2020

This study was attempted to identify differences in depression and psychological emotions according to the degree of self-elasticity of college freshmen in COVID-19. The survey was conducted online from April 6 to 13, 2020, and the results were analyzed using a total of 220 copies of the survey data, excluding insufficient surveys. As a result of the analysis, compared to the case where the self-elasticity is below average, the psychological emotion caused by COVID-19 as well as the depression of new students is statistically significant in all emotions such as anxiety, irritability, helplessness, anger, fear, confusion, and distrust. It was found to be higher. Based on these results, several roles and coping strategies were suggested to help students recover and maintain their psychological stability and mental health.