• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2241-2246
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• 2015

Recently for efficient management, companies have collected and investigated information about customers' feedback by using a system that analyzes big data from social media. This paper proposes more accurate and efficient evaluation method of collecting and investigating customers' feedback using social big data for travel agency, which is representative company of hospitality industry. First, it designs service model and, as a test-bed, analyzes media channel, customer satisfaction, and brand-image etc. of big 5 travel agencies in Korea. In addition, we suggest an analysis result of evaluating preference with positive rate and negative rate by proposed evaluation method. It allows a travel agency to know which area should be improved corresponding to evaluation item; thus, suggested evaluation method is effective to manage customers even more efficiently.

• Journal of Information Science Theory and Practice
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• v.10 no.1
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• pp.30-44
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• 2022

This research aims to identify and reveal the meaning of memes about COVID-19, and explain changes in meme content in regard to the information development of COVID-19 in Indonesia published by the mass media and online media. The data is sourced from memes that are shared on social media, especially WhatsApp groups in the period of January to June 2020. To reveal the meaning of memes, a semiotic analysis of Roland Barthes' model is used. Qualitative analysis is used to reveal the relationship between memes and the information development of COVID-19 in Indonesia. The results of the study show that meme content has shifted according to three stages of information development of COVID-19 in Indonesia published by the mass media and online media. When COVID-19 was not yet confirmed in Indonesia, meme content contained mostly humor that was not educative, moreover it seemed to underestimate the coronavirus. Meme content which is mostly humorous and pornographic occurred when COVID-19 was confirmed in Indonesia and during the lockdown policy. At last, as the government has begun to distribute social assistance, the meme content looks more critical. The results of this research emphasize the urgency of the mass media as a source of public information during the COVID-19 pandemic, become a reference for the community in making memes as a forum for expressing feelings and social criticism, and serve as a reference for the government to act and make the right decisions related to the pandemic that is endemic in their country.

• Journal of Distribution Science
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• v.19 no.6
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• pp.51-64
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• 2021

Purpose: A restaurant is a business of the physical distribution of food and beverages which includes intercultural interaction. This research paper aims to examine the influence of Japanese taste as a product advantage, determining service quality factor in the restaurant business, the e-CRM that involves technology, social media campaigns to reach the target market of young people, and customer satisfaction on customer loyalty in Sushi Tei restaurant. This study also intends to find direct and indirect influence between the variables. Research design, data and methodology: Quantitative research was conducted using a survey method by distributing 100 questionnaires to Sushi Tei customers in Jakarta, consisting of Gen Z and Millennials. Samples were taken at each outlet in the Jakarta area on weekend and weekdays by purposive sampling. Data was generated by path analysis Results: The results showed that Japanese taste, service quality, and e-CRM significantly influence customer satisfaction and customer loyalty. However, social media campaigns failed to influence customer loyalty. As a result, customer satisfaction has a significant influence on customer loyalty within Sushi Tei restaurant. Conclusion: The study also proposed significant implications to develop and maintain a relationship with the customer to gain satisfaction and loyalty in the food and beverage industry.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.241-251
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• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.164-172
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• 2023

In this study, we identify the recognition of 'The First Slam Dunk', which is gaining popularity as a sports-based cartoon through big data analysis of social media channels, and provide basic data for the development and development of various contents in the sports industry. Social media channels collected detailed social big data from news provided on Naver and Google sites. Data were collected from January 1, 2023 to February 15, 2023, referring to the release date of 'The First Slam Dunk' in Korea. The collected data were 2,106 Naver news data, and 1,019 Google news data were collected. TF and TF-IDF were analyzed through text mining for these data. Through this, semantic network analysis was conducted for 60 keywords. Big data analysis programs such as Textom and UCINET were used for social big data analysis, and NetDraw was used for visualization. As a result of the study, the keyword with the high frequency in relation to the subject in consideration of TF and TF-IDF appeared 4,079 times as 'The First Slam Dunk' was the keyword with the high frequency among the frequent keywords. Next are 'Slam Dunk', 'Movie', 'Premiere', 'Animation', 'Audience', and 'Box-Office'. Based on these results, 60 high-frequency appearing keywords were extracted. After that, semantic metrics and centrality analysis were conducted. Finally, a total of 6 clusters(competing movie, cartoon, passion, premiere, attention, Box-Office) were formed through CONCOR analysis. Based on this analysis of the semantic network of 'The First Slam Dunk', basic data on the development plan of sports content were provided.

• Journal of Nutrition and Health
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• v.56 no.2
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• pp.203-216
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• 2023

Purpose: This study examines the weight control behavior depending on university students' motives of using social media. Methods: The participants were 447 university students in the Jeonbuk area. Collected data were analyzed using factor analysis, cluster analysis, analysis of variance, and χ² tests with SPSS v. 26.0. Considering the motives of using social media, we investigated the usage of social media, dietary behavior related to social media, and weight control behavior. Results: Using the K-clustering method, the motives to use social media were categorized into three clusters: cluster 1 was the interest-centered group, cluster 2 was the multipurpose information-seeking group, and cluster 3 was the relationship-centered group. Among the various social media sites, YouTube (86.8%), Instagram (76.1%), and Facebook (61.1%) were the most visited by the subjects. The dietary behavior related to social media in cluster 2 was significantly higher than clusters 1 and 3 (p < 0.001). Clusters 1 and 2 showed a significantly higher dissatisfaction with one's weight (p < 0.05) and consequent interest in weight control than cluster 3 (p < 0.001). Cluster 2 used weight control-related information from social media significantly more than other clusters (p < 0.05). Weight control experiences in cluster 1 and 2 were significantly higher than in cluster 3 (p < 0.001). Conclusion: Differences in dietary behavior related to social media and weight control behavior were observed between cluster types of motivation to use social media. Based on the usage motives of university students and their behaviors, we propose that educational programs should be conducted for weight control using social media.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.396-404
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• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.163-176
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• 2022

As the business ecosystem has become more uncertain, the sources of supply chain risk have also been becoming more diverse. In particular, due to the development of informational technology in recent years, firms need to consider the emerging supply chain risk sources as well as traditional supply chain risk sources. A typical example is negative word-of-mouth by social media. Therefore, we investigated the virality of negative word-of-mouth on manufacturing firms by using YouTube as a representative social media. More specifically, we investigated how the social capital of the video creator influences the virality of negative word-of-mouth and how the emotional tone of the video affects the virality of negative word-of-mouth. In conclusion, the social capital of the video creator influenced the scale and speed of negative word-of-mouth. Furthermore, negative emotion words moderated the relation between the social capital of the video creator and the scale of negative word-of-mouth.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.193-200
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• 2021

Recently, posts and opinions on tourist attractions are actively shared on social media. These social big data provide meaningful information to identify objective images of tourist destinations recognized by consumers. Therefore, an in-depth understanding of the tourist image is possible by analyzing these big data on tourism. The study is to analyze destination images in Gangwon-do using big data from social media. It is wanted to understand destination images in Gangwon-do using semantic network analysis and then provided suggestions on how to enhance image to secure differentiated competitiveness as a destination for tourists. According to the frequency analysis results, as tourism in Gangwon-do, Sokcho, Gangneung, and Yangyang were mentioned at a high level in that order, and the purpose of travel was restaurant tour, gourmet food, family trip, vacation, and experience. In particular, it was found that they preferred day trips, weekends, and experiences. Four suggestions were made based on the results. First, it is necessary to develop various types of hotels, accommodation facilities and experience-oriented tour packages. Second, it is necessary to develop a day-to-day travel package that utilizes proximity to the Seoul metropolitan area. Third, it is necessary to promote traditional restaurants and local food. Finally, it is necessary to develop tourist package suitable for healing and family travel. Through this research, the destination image of Gangwon-do was identified and a tourism marketing strategy was presented to improve competitiveness. It also provided a theoretical basis for the use of the big data of tourism consumers in the field of tourism business.