• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.51-79
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• 2009

Today the service industry is growing, and differentiated services for marketing and service quality is emerging as a serious problem. Until now, the WebCam is used for observation, and the negative aspects, such as from a human rights issue has become. Discipline and Punish is not to use a webcam to customer satisfaction research is needed. Therefore, wireless monitoring in WebCam service quality of the service reconfiguration and service quality factors for the configuration of the customer's preference regarding how research was recognized. Configuration of the service quality factors WebCam service customer satisfaction and customer loyalty was to identify factors that affect. These services to study the correlation between the quality variables WebCam provides wireless monitoring services for the preschool and children, a survey of the house. Configuring a wireless monitoring service quality in order to research the factors WebCam video quality and transmission speed, mobility and portability, convenience, visibility, reliability, and the interaction of the castle set. Severely and video quality and transmission speed, visibility, reliability, customer satisfaction, the interaction factors are said. Inclination to participate in social issues, innovation, digital devices that use a skilled, depending on the difference in the service quality has been confirmed. However, the age and quality of service awareness and job types showed a low correlation, and the marketing of these results will be discussed and how that can be used.

• Review of Culture and Economy
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• v.17 no.2
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• pp.3-28
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• 2014

The characteristics of performing arts differ from those of utilitarian goods in terms of economics. Factors other than price need to be considered to understand the demand for performing arts. Audience surveys as well as econometric demand studies have confirmed that socio-economic factors such as age, income, employment, and education are major determinants of the demand for performing arts. This study focused on the attributes of concerts rather than consumer characteristics to determine the concerts audiences select in terms of transaction cost. Genre, price, internet search trends, and the purpose of performance as well as price are tested as determinants of demand by using the data set for a major concert hall in Seoul. Genre and the specific purpose of concerts influence the demand for concerts. Internet search trends of the performer are used as indicators of popularity and information exposure, which are positively correlated with demand. This result supports the hypothesis that larger audiences would attend concerts that require lower information search costs. To note, price has a positive effect on demand in the higher price range, which means that concerts at higher prices attract larger audiences, whereas normal goods have a negative slope in the demand curve. This result can be explained by the hypothesis that consumers use price as an indicator of the quality expected of a concert. Transaction cost for selecting classical concerts thus forms an inverse-U shape curve against ticket price. These results provide some explanation of why audiences of classical music choose to attend concerts at high ticket prices while offering evidence in favor of the hypothesis that performing arts are selected in a social context.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.51-63
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• 2021

Identifying characters who appear in a story and analyzing their relationships is essential to understanding the story. This paper aims to identify the two actants (or character roles) as Helper and Opponent in Greimas's Actantial model by identifying Subject (i.e., protagonist) and analyzing the emotional interactions between the Subject and the two actants (Helper/Opponent). Our proposed method consists of three steps. First, we identify objects (i.e., characters) appearing in the text story. Next, we extract relational information through the interaction of the characters, and then classify emotions in the text expressed as relational information. Finally, we represent the flow of emotional relations among characters as a directed graph. The node with the highest degree is considered as the Subject because it includes the most relational information. The node that sends the most positive/negative emotions to the Subject is considered as the Helper/Oppenent, respectively. Our research contributes to the computer-based narrative understanding by providing a computational method that automatically extracts the three key character roles (Subject, Helper, and Opponent) from the text story.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.21-29
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• 2018

In the era of the 4-th industrial revolution, the concept of smart factory is emerging. There are efforts to predict the occurrences of facility errors which have negative effects on the utilization and productivity by using data analysis. Data composed of the situation of a facility error and the type of the error, called the facility error log, is required for the prediction. However, in many manufacturing companies, the types of facility error are not precisely defined and categorized. The worker who operates the facilities writes the type of facility error in the form with unstructured text based on his or her empirical judgement. That makes it impossible to analyze data. Therefore, this paper proposes a framework for constructing a phrase network to support the identification and classification of facility error types by using facility error logs written by operators. Specifically, phrase indicating the types are extracted from text data by using dictionary which classifies terms by their usage. Then, a phrase network is constructed by calculating the similarity between the extracted phrase. The performance of the proposed method was evaluated by using real-world facility error logs. It is expected that the proposed method will contribute to the accurate identification of error types and to the prediction of facility errors.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.35-47
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• 2022

This study aims to identify the degree of job stress related to COVID-19, emotional labor, empowerment, and retention intention of nurses in hospitals dedicated to infectious diseases, and confirmed the effect of job stress related to COVID-19, emotional labor, and empowerment on retention intention. The data collection of this study was conducted from August 27, 2021 to September 17, 2021 through a structured questionnaire targeting 162 nurses at an infectious disease hospital in S city. The data were analyzed analyzed using frequency and percentage, mean and standard deviation, t-test, ANOVA, Pearson's Correlation Coefficient, and multiple linear regression using SPSS/WIN 25.0. As a result of analyzing differences according to general characteristics, retention intention showed a significant difference according to work department and work satisfaction. And as a result of analyzing the correlation between retention intention and COVID-19 related job stress, emotional labor, and empowerment, it showed a significant negative correlation (r=-0.215, p=0.006) with job stress related to COVID-19 and a significant positive correlation (r=0.343, p<0.001) with empowerment. As a result of multiple linear regression analysis, job satisfaction, job stress related to COVID-19, and empowerment were identified as significant variables affecting retention intention (F=23.751, p<0.001), and the explanatory power was 30.0%. Through the above results, we intend to provide basic data for strategic development for efficient nursing manpower management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2169-2190
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• 2022

The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.65-76
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• 2022

The purpose of this study aimed to identify the level of knowledge & preventive health behaviors related to COVID-19, self-efficacy, anxiety, and perceived stress of students at a nursing college and to investigate the correlation between them. The data were collected from 133 students at a nursing college in Seoul, Korea, from April 15, 2022 to May 10, 2022 through a Google online questionnaire. The data were analyzed using SPSS/WIN 25.0 to perform descriptive statistics, t-test, one-way ANOVA, Pearson's Correlation Coefficients. As a result of analyzing the difference according to general characteristics, preventive health behavior showed a significant difference according to the necessity of COVID-19 infection control education, and self-efficacy showed a difference according to the subjective health status. The degree of instability of the subjects was shown to have significant differences according to grade and subjective health status, and perceived stress showed significant differences according to subjective health status. The result of analyzing the correlation between preventive health behavior and knowledge, self-efficacy, anxiety, and perceived stress showed that there was no significant correlation, but self-efficacy had a significant negative correlation with anxiety and perceived stress, and anxiety had a significant positive correlation with perceived stress. The results of this study will be used as basic data for education programs and countermeasures to prevent COVID-19 infections.

• Korean Journal of Culture and Social Issue
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• v.16 no.3
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• pp.331-354
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• 2010

Previous psychological studies in gambling have mainly focused on the characteristics of gamblers. the purpose of the present study was to categorize gambles in Korea into subtypes based on winning probability and money and to examine variations of gambling behaviors and problems across the gamble subtypes. A survey on 1,304 gamble participants were conducted, of their gambling behaviors, personal and social problems, and CPGI. First, factor analyses on perceived winning probability and money revealed 6 subtypes of gambles: amusement type, lottery type, internet type, slot-machine type, racing type and casino type. Secondly, comparisons among gamble subtypes revealed the differences in gambling behaviors, gambling-related cognitions, emotional experiences during gambling, and personal/social problems related to gambling. The gambling behaviors in slot-machine type, racing type, and casino type were more negative than those in amusement type, and lottery type. Gamblers in internet type were found to have potential for latent problems of gambling. In discussion, the academic and practical values and implications of typology of gambles were further discussed.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.47-56
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• 2024

This paper proposes a method for fine-tuning BERT-based natural language processing models to perform sentiment analysis on Korean review data. By varying the input sequence length during this process and comparing the performance, we aim to explore the optimal performance according to the input sequence length. For this purpose, text review data collected from the clothing shopping platform M was utilized. Through web scraping, review data was collected. During the data preprocessing stage, positive and negative satisfaction scores were recalibrated to improve the accuracy of the analysis. Specifically, the GPT-4 API was used to reset the labels to reflect the actual sentiment of the review texts, and data imbalance issues were addressed by adjusting the data to 6:4 ratio. The reviews on the clothing shopping platform averaged about 12 tokens in length, and to provide the optimal model suitable for this, five BERT-based pre-trained models were used in the modeling stage, focusing on input sequence length and memory usage for performance comparison. The experimental results indicated that an input sequence length of 64 generally exhibited the most appropriate performance and memory usage. In particular, the KcELECTRA model showed optimal performance and memory usage at an input sequence length of 64, achieving higher than 92% accuracy and reliability in sentiment analysis of Korean review data. Furthermore, by utilizing BERTopic, we provide a Korean review sentiment analysis process that classifies new incoming review data by category and extracts sentiment scores for each category using the final constructed model.