• Journal of Information Technology Applications and Management
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• v.30 no.5
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• pp.1-19
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• 2023

This study examines Korean users' attitudes and emotions toward Melon and Spotify, which lead the music streaming market. We used Text Mining, Semantic Network Analysis, TF-IDF, Centrality, CONCOR, and Word2Vec analysis. As a result of the study, MelOn was used in a user's daily life. Based on Melon's advantages of providing various contents, the advantage is judged to have considerable competitiveness beyond the limits of the streaming app. However, the MelOn users had negative emotions such as anger, repulsion, and pressure. On the contrary, in the case of Spotify, users were highly interested in the music content. In particular, interest in foreign music was high, and users were also interested in stock investment. In addition, positive emotions such as interest and pleasure were higher than MelOn users, which could be interpreted as providing attractive services to Korean users. While previous studies have mainly focused on technical or personal factors, this study focuses on consumer reactions (online reviews) according to corporate strategies, and this point is the differentiation from others.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.195-203
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• 2021

Recently, big data, a major technology in the IT field, has been expanding into various industrial sectors and research on how to utilize it is actively underway. In most Internet industries, user reviews help users make decisions about purchasing products. However, the process of screening positive, negative and helpful reviews from vast product reviews requires a lot of time in determining product purchases. Therefore, this paper designs and implements a system that analyzes and aggregates keywords using LDA, a big data analysis technology, to provide meaningful information to users. For the extraction of document topics, in this study, the domestic book industry is crawling data into domains, and big data analysis is conducted. This helps buyers by providing comprehensive information on products based on user review topics and appraisal words, and furthermore, the product's outlook can be identified through the review status analysis.

• Korean Institute of Interior Design Journal
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• v.22 no.6
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• pp.88-97
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• 2013

This study aims to find out various users' diverse interior design needs for their housing and interior design through the personality, which is intrinsic and consistent traits of the individual. The survey research followed the literature reviews including personality studies and interior design assessments. 176 undergraduate and graduate students as controlled by age, sex, and major answered the questionnaire. Their housing and interior design attitudes, the semiotic assessment of interior design styles, and interior design preference were compared in accordance with four pairs of preference dichotomy of MBTI (Myers-Briggs Type Indicator): Extraversion -Introversion, Sensing-iNtuition, Thinking-Feeling, Judging-Perceiving. As a result, the framework of housing and interior design needs by the users' personality types are proposed. It shows specific needs for 16 types of personality based on eight preference dichotomy: extroversion-open, introversion-closed, sensing-functional, intuition-emotional, thinking-restricted, feeling-receptive, judging-simple, and perceiving-creative.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.55-60
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• 2020

As personalized customer services create a society that emphasizes the personality of an individual, the number of product reviews and quantity of user data generated by users on the internet in mobile shopping apps and sites are increasing. Such product review data are classified as unstructured data. Unstructured data have the potential to be transformed into information that companies and users can employ, using appropriate processing and analyses. However, existing systems do not reflect the detailed information they collect, such as user characteristics, purchase preference, or purchase priority while analyzing review data. Thus, it is challenging to provide customized recommendations for various users. Therefore, in this study, we have developed a product recommendation system that takes into account the user's priority, which they select, when searching for and purchasing a product. The recommendation system then displays the results to the user by processing and analyzing their preferences. Since the user's preference is considered, the user can obtain results that are more relevant.

• Journal of the Semiconductor & Display Technology
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• v.18 no.1
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• pp.79-86
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• 2019

In the recent industry, unmanned systems are expected to continue to spread throughout the economy. Especially in case of cosmetics, Users do not buy it right away. They look around, test it thoroughly, check the reviews, and decide on the purchase. Therefore, unmanned system using kiosk is more popular than face - to - face service of clerks. In this study, the persona analysis was completed based on the results obtained from the questionnaires and in - depth interviews. After sorting out the needs of the users, we applied them to the kiosk UI design of "Lalavela". The purpose of this study is to propose a kiosk UI design that helps many users who want to know information about the product though they are reluctant to ask directly to the clerk.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.87-94
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• 2020

Social reviews such as SNS feeds and blog articles have been widely used to extract keywords reflecting opinions and complaints from users' perspective, and often include proper nouns or new words reflecting recent trends. In general, these words are not included in a dictionary, so conventional morphological analyzers may not detect and extract those words from the reviews properly. In addition, due to their high processing time, it is inadequate to provide analysis results in a timely manner. This paper presents a method for efficient keyword extraction from social reviews based on the notion of cohesion scoring. Cohesion scores can be calculated based on word frequencies, so keyword extraction can be performed without a dictionary when using it. On the other hand, their accuracy can be degraded when input data with poor spacing is given. Regarding this, an algorithm is presented which improves the existing cohesion scoring mechanism using the structure of a word tree. Our experiment results show that it took only 0.008 seconds to extract keywords from 1,000 reviews in the proposed method while resulting in 15.5% error ratio which is better than the existing morphological analyzers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.263-283
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• 2024

Users' comments after online shopping are critical to product reputation and business improvement. These comments, sometimes known as e-commerce reviews, influence other customers' purchasing decisions. To confront large amounts of e-commerce reviews, automatic analysis based on machine learning and deep learning draws more and more attention. A core task therein is sentiment analysis. However, the e-commerce reviews exhibit the following characteristics: (1) inconsistency between comment content and the star rating; (2) a large number of unlabeled data, i.e., comments without a star rating, and (3) the data imbalance caused by the sparse negative comments. This paper employs Bidirectional Encoder Representation from Transformers (BERT), one of the best natural language processing models, as the base model. According to the above data characteristics, we propose the F_MixBERT framework, to more effectively use inconsistently low-quality and unlabeled data and resolve the problem of data imbalance. In the framework, the proposed MixBERT incorporates the MixMatch approach into BERT's high-dimensional vectors to train the unlabeled and low-quality data with generated pseudo labels. Meanwhile, data imbalance is resolved by Focal loss, which penalizes the contribution of large-scale data and easily-identifiable data to total loss. Comparative experiments demonstrate that the proposed framework outperforms BERT and MixBERT for sentiment analysis of e-commerce comments.

• Science of Emotion and Sensibility
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• v.27 no.1
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• pp.69-80
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• 2024

This study aimed to gain valuable insights into the performance and user satisfaction of applications (apps) through a thorough analysis of Instagram user reviews collected from Google Play. The study utilized text mining and sentiment analysis techniques and systematically identified emotions and opinions embedded in user reviews to deeply understand the areas of improvement and user experiences of the app. It analyzes how Instagram reviews reflect the diverse experiences of users and how they reveal the strengths and weaknesses of the app. For this purpose, sentiment analysis using the naive Bayes algorithm was conducted, and the results were expected to aid in the improvement of Instagram's services. In addition, the study aimed to assist developers in better understanding and utilizing user feedback, ultimately contributing to enhanced user satisfaction. This study explored the complex relationship between social media usage patterns and user opinions by seeking ways to provide a better user experience through these insights.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.05a
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• pp.321-349
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• 2007

The objective of this study is to investigate factors influencing trust on internet shopping mall and to examine whether these factors on trust have differences between Korean and Chinese users. Based on the literature reviews, this study employs six factors in two groups of influencing factors, web-site characteristics and individual characteristics as key determinants of trust in internet shopping mall. Analysis of 470 responses (Korean: 320 users, Chinese: 150 users) of survey questionnaire indicates the following: In terms of the relationship between web-site characteristics factors and the trust, the results showed clear differences between Korean and Chinese samples. For the Korean samples, transaction security and interactivity were the only significant influencing factors. For the Chinese samples, perceived reputation and perceived size were the only significant factors. In terms of individual characteristics factors, the influencing factors were found identical for the samples. Both propensity to trust and familiarity were significant factors in both Korean and Chinese samples. Based on the findings, managerial implications are discussed in building user trust in internet shopping mall in Korean and Chinese market.

• Journal of Information Technology Applications and Management
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• v.14 no.2
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• pp.71-96
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• 2007

The objectives of this study are to investigate factors influencing trust on internet shopping mall and to examine whether these factors on trust have differences between Korean and Chinese users. Based on the literature reviews, this study employs six factors in two groups of influencing factors, web-site characteristics and individual characteristics as key determinants of trust in internet shopping mall. Analysis of 470 responses(Korean: 320 users, Chinese: 150 users) of survey questionnaire indicates the following: In terms of the relationship between web-site characteristics factors and the trust, the results showed significant differences between Korean and Chinese samples. For the Korean samples, transaction security and interactivity were the only significant influencing factors. For the Chinese samples, perceived reputation and perceived size were the only significant factors. In terms of individual characteristics factors, the influencing factors were found identical for the samples. Both propensity to trust and familarity were significant factors in both Korean and Chinese samples. Based on the findings, managerial implications are discussed in building user trust in internet shopping mall in Korean and Chinese market.