• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.137-142
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• 2024

User interface (UI) functions are distorted and appear as a dark pattern that intentionally deceives or entices users. Consumers who are unaware of dark patterns are constrained in their choices, resulting in unnecessary economic damage. In this study, we aimed to investigate consumers' various shopping emotions and behavioral intentions after recognizing dark patterns in online shopping malls through qualitative research methods. As a result of the study, the rate of perception differed slightly depending on the type of dark pattern, and it was found that it induced consumer emotions such as distrust of the company, user deception, and displeasure. It has been found that the behavior after recognizing the dark pattern shows passive behaviors such as vowing to prevent recurrence and warning acquaintances rather than actively protesting to the company and demanding compensation for damages.

• Human Ecology Research
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• v.60 no.1
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• pp.21-38
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• 2022

This study uses text mining compares and contrasts consumers' social media discourses on dietary related zero-waste movement before and after COVID-19. The results indicate that the amount of buzz on social networks for the zero- waste movement has been increasing after COVID-19. Additionally, the results of frequency analysis and topic modeling revealed that subjects associated with zero-waste movement were more diversified after COVID-19. Although the results of a sentiment analysis and word cloud visualization confirmed that consumers' positive responses toward the zero-waste have been increasing, they also revealed a need to educate and encourage those who are still not aware of the need for zero-waste. Finally, consumers mentioned only a small number of companies participating in zero-waste movement on SNS, indicating that the level of active involvement by such companies is much lower than that of consumers. Theoretical and educational implications as well as those for government policy-making are considered.

• Journal of Naturopathy
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• v.11 no.1
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• pp.51-61
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• 2022

Background: There was a need to investigate the interest in beauty and consumption sentiment in beauty shops on COVID-19 among beauty customers in their 20s and older in Daejeon Metropolitan City. Purpose: To investigate the degree of interest in beauty and consumption sentiment of beauty shops according to COVID-19 targeting beauty customers. Methods: A survey was conducted targeting beauty customers in their 20s or older in Daejeon. A total of 263 questionnaires were analyzed using the SPSS 27.0 program. Results: Beauty customers' interest in beauty was above average in hair style, makeup, skin care and body shape management, but nail care was below average. In addition, it was found that there were some statistically significant differences or no difference in beauty shop consumer sentiment due to COVID-19 according to gender, marital status, age, and occupation. It was found that there was a positive (+) correlation between interest in beauty and consumption sentiment of beauty shops due to COVID-19. Conclusion: The beauty interest in beauty of the customers in their 20s or older in Daejeon Metropolitan City and the consumption sentiment of beauty shops on COVID-19 outbreak indicated some statistically significant differences depending on gender, marital status, age, and occupation, or seems no difference. Therefore, it is evaluated that the results will serve as basic data for research in this field.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.5
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• pp.1-11
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• 2024

Online reviews play a crucial role in assessing a company's market value and are a significant factor influencing profitability. As such, sentiment analysis of online reviews has emerged as a key indicator for predicting business success. This study focuses on restaurant reviews from Yelp, one of the leading online review platforms, utilizing the Yelp Open Dataset. Six machine learning algorithms were applied to predict the sentiment polarity of these reviews: Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting Machine (GBM), XGBoost, and LightGBM. Performance evaluations demonstrated that Logistic Regression, SVM, and LightGBM achieved the highest accuracy, with a score of 0.91. The primary contribution of this study is its ability to transform unstructured review text into quantifiable data, enabling businesses, especially startups, to effectively analyze customer feedback and predict ratings. These insights are expected to assist business owners in forecasting consumer behavior and developing strategic marketing approaches.

• Journal of Korean Society of Forest Science
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• v.108 no.1
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• pp.97-108
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• 2019

In a situation where production of short-term income forestry products has been stagnant or decreased in recent years, the government or related agencies are trying to promote consumption of short-term income forest products. While consumer sentiment studies on short-term income forestry are being conducted as part of efforts to encourage consumption, most of the studies rely solely on a survey-based method. In the information age, consumer sentiment toward consumer goods is reflected mostly on social networking sites due to the spread of the Internet. It is necessary to avoid relying solely on a survey-based method in existing research and directly analyze social networking sites that reflect consumers' wishes. In response, this study identified consumer preferences for major short-term income forest products through social big data analyses and used the results to establish strategies for promoting the sale of short-term income forest products. This paper is different from previous research using only a survey-based method, and it uses SNS to understand consumer preferences. The results of this study are expected to directly help the government or related agencies promote consumption of short-term income forest products and, ultimately, help improve forest-related income and promote healthy forest condition.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.69-90
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• 2023

The purpose of this study is to evaluate the service attributes and consumer reactions of telemedicine apps in South Korea and visualize their differentiation by constructing positioning maps. We crawled 23,219 user reviews of 6 major telemedicine apps in Korea from the Google Play store. Topics were derived by BERTopic modeling, and sentiment scores for each topic were calculated through KoBERT sentiment analysis. As a result, five service characteristics in the application attribute category and three in the medical service category were derived. Based on this, a two-dimensional positioning map was constructed through principal component analysis. This study proposes an objective service evaluation method based on text mining, which has implications. In sum, this study combines empirical statistical methods and text mining techniques based on user review texts of telemedicine apps. It presents a system of service attribute elicitation, sentiment analysis, and product positioning. This can serve as an effective way to objectively diagnose the service quality and consumer responses of telemedicine applications.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1235-1249
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• 2022

This study applied the web crawling technique for extracting big data news on water quality accidents in the water supply system and presented the algorithm in a procedural way to obtain accurate water quality accident news. In addition, in the case of a large-scale water quality accident, development patterns such as accident recognition, accident spread, accident response, and accident resolution appear according to the occurrence of an accident. That is, the analysis of the development of water quality accidents through key keywords and sentiment analysis for each stage was carried out in detail based on case studies, and the meanings were analyzed and derived. The proposed methodology was applied to the larval accident period of Incheon Metropolitan City in 2020 and analyzed. As a result, in a situation where the disclosure of information that directly affects consumers, such as water quality accidents, is restricted, the tone of news articles and media reports about water quality accidents with long-term damage in the event of an accident and the degree of consumer pride clearly change over time. could check This suggests the need to prepare consumer-centered policies to increase consumer positivity, although rapid restoration of facilities is very important for the development of water quality accidents from the supplier's point of view.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.437-442
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• 2021

In this study, a recommendation algorithm was implemented by learning a deep learning-based classification model for consumer data. For this purpose, a meaningful result is presented as a result of learning using ResNet50, which is commonly used in classification tasks by converting user data into images.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.139-148
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• 2024

In the post-pandemic era, the demand for online education platforms has surged, leading to increased consumer reliance on online reviews for decision-making. This study investigates the impact of Chinese online customer reviews on consumer purchase behavior in online education. By examining the role of trust, review sentiment, and the quantity and timeliness of reviews, the research aims to understand how these factors influence consumer decisions. By using regression model, findings reveal that negative reviews, timely feedback, and a higher volume of reviews positively affect consumer purchase decisions, while course pricing demonstrates an inverse relationship. Furthermore, cognitive and affective trust mediate the relationship between reviews and purchase behavior, highlighting a reverse U-shaped effect on consumer decision inclination. These insights provide valuable implications for online education providers, emphasizing the need to manage and leverage online reviews to foster consumer trust and improve sales performance.