• Journal of Digital Convergence
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• v.20 no.3
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• pp.131-143
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• 2022

Focusing on FinTech keywords, this study is analyzing newspaper articles and Twitter data by using text mining methodology in order to understand trends in the industry of domestic digital financial service. In the growth of FinTech lifecycle, the frequency analysis has been performed by four important points: Mobile Payment Service, Internet Primary Bank, Data 3 Act, MyData Businesses. Utilizing frequency analysis, which combines the keywords 'China', 'USA', and 'Future' with the 'FinTech', has been predicting the FinTech industry regarding of the current and future position. Next, sentiment analysis was conducted on Twitter to quantify consumers' expectations and concerns about FinTech services. Therefore, this study is able to share meaningful perspective in that it presented strategic directions that the government and companies can use to understanding future FinTech market by combining frequency analysis and sentiment analysis.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.9-18
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• 2021

Constructing the Metropolitan Railway Express (the GTX) may have an impact on consumer confidence and housing sales price located near the planned route. This study looked at how consumers' psychology and housing prices change as the large-scale transport infrastructure plane was planned. Also, it looked at the relationship between consumer sentiment and housing prices to analyze the impact of new transportation facilities inflows. Using a correlation analysis, the relationship between the consumer sentiment index and the actual transaction price of apartments was identified. The impact of GTX on the consumer sentiment index and the actual transaction price of apartments was looked at using the Difference-in-Differences methodology. Our finding shows that the construction plan of a large-scale transportation infrastructure in the metropolitan area affects the sentiment of housing consumption and actual transactions. In a situation where the government is speeding up the construction of a wide-area transportation network such as GTX with the goal of becoming a city where people can commute to downtown Seoul within 30 minutes, policies that can stabilize the housing market in transportation hubs should be suggested.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Journal of Industrial Convergence
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• v.21 no.8
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• pp.23-33
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• 2023

Consumer review analysis is important for product development, customer satisfaction, competitive advantage, and effective marketing. Increased use of wireless earphones is expected to reach $45.7 billion by 2026 with growth in lifestyle. Therefore, in consideration of the growth and importance of the market, consumer reviews of wireless earphones from Apple and Samsung were analyzed. In this study, 11,320 wireless earphone reviews from Apple and Samsung sold on Coupang were collected to analyze consumers' purchase intentions and analyze consumer satisfaction through analysis of the frequency, sensitivity, and LDA topic model of text mining. As a result of topic modeling, 16 topics were derived and classified into sound quality, connection, shopping mall service, purchase intention, battery, delivery, and price. As a result of brand comparison, Samsung purchased a lot for gift purposes, had a high positive sentiment for price, and Apple had a high positive sentiment for battery, sound quality, connection, service, and delivery. The results of this study can be used as data for related industries as a result of research that can obtain improvements and insights on customer satisfaction, quality and market trends, including manufacturing, retail, marketers, and consumers.

• Information Systems Review
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• v.20 no.2
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• pp.111-137
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• 2018

Considering the increase in health insurance benefits and the elderly population of the baby boomer generation, the amount consumed by health care in 2020 is expected to account for 20% of US GDP. As the healthcare industry develops, competition among the medical services of hospitals intensifies, and the need of hospitals to manage the quality of medical services increases. In addition, interest in online reviews of hospitals has increased as online reviews have become a tool to predict hospital quality. Consumers tend to refer to online reviews even when choosing healthcare service providers and after evaluating service quality online. This study aims to analyze the effect of sentiment score of healthcare service quality on hospital rating with Yelp hospital reviews. This study classifies large amount of text data collected online primarily into five service quality measurement indexes of SERVQUAL theory. The sentiment scores of reviews are then derived by SERVQUAL dimensions, and an econometric analysis is conducted to determine the sentiment score effects of the five service quality dimensions on hospital reviews. Results shed light on the means of managing online hospital reputation to benefit managers in the healthcare and medical industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.391-406
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• 2015

Social media have emerged as new communication channels between consumers and companies that generate a large volume of unstructured text data. This social media content, which contains consumers' opinions and interests, is recognized as valuable material from which businesses can mine useful information; consequently, many researchers have reported on opinion-mining frameworks, methods, techniques, and tools for business intelligence over various industries. These studies sometimes focused on how to use opinion mining in business fields or emphasized methods of analyzing content to achieve results that are more accurate. They also considered how to visualize the results to ensure easier understanding. However, we found that such approaches are often technically complex and insufficiently user-friendly to help with business decisions and planning. Therefore, in this study we attempt to formulate a more comprehensive and practical methodology to conduct social media opinion mining and apply our methodology to a case study of the oldest instant noodle product in Korea. We also present graphical tools and visualized outputs that include volume and sentiment graphs, time-series graphs, a topic word cloud, a heat map, and a valence tree map with a classification. Our resources are from public-domain social media content such as blogs, forum messages, and news articles that we analyze with natural language processing, statistics, and graphics packages in the freeware R project environment. We believe our methodology and visualization outputs can provide a practical and reliable guide for immediate use, not just in the food industry but other industries as well.

• Journal of the Korea Furniture Society
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• v.28 no.1
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• pp.80-87
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• 2017

As modern society attaches important to a value in mental aspect and characteristics of consumers who become diverse and individualistic, lighting design also changes closely with everyday life. This study suggests a new concept of indoor lighting design, combining natural objects with lattice and Korean paper, one of our representative traditional materials in lighting design used in everyday life. In particular, it was designed with aesthetic sense of traditional culture and Korean sentiment besides external effect and function by combining Korean paper with natural objects such as insect and plant, material that could be easily obtain around us. As a result, it is intended to enhance quality of life and pursue happiness by suggesting a new concept of lighting design which is modern, harmonizes with everyday life of modern humans who become individualistic, and can arouse sensibility, overcoming the limitations of traditional lighting in indoor lighting.

• International Journal of Human Ecology
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• v.9 no.1
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• pp.81-95
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• 2008

This paper examined the impact of psychological characteristics of consumers on household debt decisions. With the use of the Heckit models (the traditional approach to the selection problem) this study undertook an empirical study of the influence of a wide range of factors on financial decisions. This study used U.S. household-level data that offers detailed information on household debt, expectations about future income, expectations about future economic conditions, the amount of financial risk the respondent was willing to take, and the amount of time allotted for planning family savings and spending. This study showed that respondents with both substantial financial risk tolerance and positive expectations about future income were likely to have larger household debt showing that researchers and policy-makers need to consider consumer sentiment and preference measures in modeling behavior in credit markets. Additional results showed that household debt is significantly related to two key economic variables: income and net worth.

• Journal of Fashion Business
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• v.21 no.5
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• pp.61-77
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• 2017

Fashion companies are increasingly becoming aware of the importance of Digilog as a response strategy to an emotional stimulus, in order to win the hearts of consumers, because the Digilog provides a new type of emotional value. The features of Digilog found in modern fashion are characterized as follows: first, the "Fashion Image of Hybrid Nature" expresses nature in a new light or reinterprets existing expressions of nature, by using cutting-edge technology based on the psychological desire to return to, adapt with, and harmonize with nature. Second, the "Fashion Image of Nostalgia," which exhibits past forms of regressive fashion, is a fashion code that can be understood as a social trend. It has a digital exterior, with retro materials and old perfumes that reflect psychological comfort, as its expressive medium. Third, the "Lifestyle through the Technique of Interaction" is the sharing of information through consumer participation and delivery, or its interaction. Fourth, the "Fashion Design through the Technique of Customizing" allows consumers to actively participate in the design process. It reflects the consumer's desire to personally design fashion products. Fifth, the "Emotion Sharing through the Technique of Storytelling," which focuses on intangible values, is based on the sentiment of communication between the consumer and the brand, thereby satisfying the inner values as well as the aesthetic demands of consumers. This study confirmed that digital fashion, which uses digital technology based on analog sentiments, has opened up a new environment for fashion culture and has also widened the boundaries of fashion.

• Archives of design research
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• v.15 no.4
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• pp.201-214
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• 2002

The discussions on Korean advertisement have been continued since 1980's. Comparing with other fields, years of research had not bear as fruitful achievements as we expected. The present situation where korean own culture has been disappearing day by day, this thesis will have significant meaning by investigating Korean culture and advertisement reflecting its culture. This thesis put emphasis on consumers who have been neglected by discussions. So I intended to study the expression of Korean advertisement and their attitudes focused on celluar phone. To investigate their attitudes, I first settled the concept of Korean Advertisement, then posed questions for identifying a shift in their altitudes. As a result of posing questions, I can see that they take friendly attitudes toward Korean advertisement regardless of their ages. And the younger they are, the less they are friendly with advertisement. The element of appeal was differentiated between ages and appeal by korean own sentiment is more effective. I think this outcome verifies the necessity to have concerns on Korean advertisement and make distinct advertisement strategy keeping step with advertisement targets.