• Journal of Digital Convergence
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• v.18 no.8
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• pp.179-184
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• 2020

This study aims to investigate the effect that local government's information release range on Covid-19 patients' contact trace on consumer sentiment in the region. One hundred twenty-eight residents of Gangnam-gu and Seocho-gu participated in the study. The results showed that when ambiguity of information increased perceived anxiety on Covid-19 patients' contact trace, which in turn led lower intention to visit the commercial district near the Covid-19 patients' contact trace. Based on the findings, several suggestions was proposed for future research, including longitudinal studies covering even the "long-term" changes in consumer sentiment, the effect of implicit anxiety, and the behavioral difference between residence and non-residence.

• Journal of Korea Trade
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• v.24 no.8
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• pp.39-62
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• 2020

Purpose - Korean exports account for a vast proportion of Korean GDP, and large volumes of Korean products are sold in the United States. Identifying and characterizing actual and potential product hazards related to Korean products is critical to safeguard Korean export trade, as severe quality issues can impair Korea's reputation and reduce global consumer confidence in Korean products. In this study, we develop country-of-origin-based product risk analysis methods for social media with a specific focus on Korean-labeled products, for the purpose of safeguarding Korean export trade. Design/methodology - We employed two social media datasets containing consumer-generated product reviews. Sentiment analysis is a popular text mining technique used to quantify the type and amount of emotion that is expressed in the text. It is a useful tool for gathering customer opinions regarding products. Findings - We document and discuss the specific potential risks found in Korean-labeled products and explain their implications for safeguarding Korean export trade. Finally, we analyze the false positive matches that arise from the established dictionaries that were used for risk discovery and utilize these classification errors to suggest opportunities for the future refinement of the associated automated text analytic methods. Originality/value - Various studies have used online feedback from social media to analyze product defects. However, none of them links their findings to trade promotion and the protection of a specific country's exports. Therefore, it is important to fill this research gap, which could help to safeguard export trade in Korea.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.115-122
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• 2019

In the online marketplace, consumers are exposed to various products and freely express opinions. As consumer product reviews have a important effect on the success of online markets and other consumers, online market needs to accurately analyze the consumers' emotions about their products. Text mining, which is one of the data analysis techniques, can analyze the consumer's reviews on the products and efficiently manage the products. Previous studies have analyzed specific domains and less than 20,000 data, despite the different accuracy of the analysis results depending on the data domain and size. Further, there are few studies on additional factors that can improve the accuracy of analysis. This study analyzed 72,530 review data of food product domain that was not mainly covered in previous studies by using ensemble technique. We also examined the influence of summary review on improving accuracy of analysis. As a result of the study, this study found that Boosting ensemble technique has the highest accuracy of analysis. In addition, the summary review contributed to improving accuracy of the analysis.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• The Korean Fashion and Textile Research Journal
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• v.18 no.1
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• pp.1-14
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• 2016

This study analyzes consumers' preference trend, positive and negative factors in regards to luxury brands by researching changes in the consumer awareness of luxury brands, preference trends and psychological awareness based on big data to suggest a creative business strategy for corporations that can help Korean brands enter global luxury brand markets. The study results are as follows. Preferred items (consumer) psychology, positive awareness and negative awareness were derived based on the last five years of social big data on Korean consumers' preferred brands. First, the Korean consumers' preferred brands for the recent five years indicated that Dolce & Gabbana (2013), ESCADA (2012), Gucci (2011, 2009) and Chanel (2010) were most preferred and Prada, Louis Vuitton, Hermes, Burberry, Fendi, Givenchy and Dior were also shown to be preferred brands. Second, bags (such as shoulder bags) were shown to be the most preferred items for luxury brand items that consumers wished to own. Third, it was analyzed that keywords for consumer psychology in regards to luxury brands included: diverse, new, outstanding, overwhelming, luxurious, glamorous, worldwide, famous, success and good. Fourth, consumers' positive awareness regarding luxury brands included: diverse, luxury, famous, outstanding, perfect, bright and luxurious. Fifth, negative awareness included: price factors of expensive, high price and excessive as well as factors to be improved upon such as old, bland, flashy, crude, unfriendly and fake.

• Journal of the Korea Fashion and Costume Design Association
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• v.26 no.2
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• pp.87-99
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• 2024

Freediving, an underwater leisure sport that involves diving without the use of a breathing apparatus, has gained popularity among younger demographics through the viral spread of images and videos on social media platforms. This study employs prominent Big Data analysis techniques, including text mining, Latent Dirichlet Allocation (LDA) topic analysis, and opinion mining to explore the keywords associated with freediving suits over the past five years. The research aims to analyze the rapidly evolving market trends of freediving suits and the increasingly complex and diverse consumer perceptions to provide foundational data for activating the freediving suit market and developing strategies for sustained growth. The study identified the keyword 'size' related to freediving suits and conducted opinion mining on 'freediving suit sizes'. Although the results showed a higher positive than negative sentiment, negative keywords were also extracted, indicating the need to understand and mitigate the negative factors associated with 'size'. The findings offer vital guidelines for the advancement of the freediving suit market and enhancing consumer satisfaction. This study is important as it contributes foundational data for continuous growth strategies of the freediving suit market.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.197-210
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• 2016

With the advent of text analytics, VOC (Voice of Customer) data become an important resource which provides the managers and marketing practitioners with consumer's veiled opinion and requirements. In other words, making relevant use of VOC data potentially improves the customer responsiveness and satisfaction, each of which eventually improves business performance. However, unstructured data set such as customers' complaints in VOC data have seldom used in marketing practices such as predicting service time as an index of service quality. Because the VOC data which contains unstructured data is too complicated form. Also that needs convert unstructured data from structure data which difficult process. Hence, this study aims to propose a prediction model to improve the estimation accuracy of the level of customer satisfaction by combining unstructured from textmining with structured data features in VOC. Also the relationship between the unstructured, structured data and service processing time through the regression analysis. Text mining techniques, sentiment analysis, keyword extraction, classification algorithms, decision tree and multiple regression are considered and compared. For the experiment, we used actual VOC data in a company.

• The Korean Fashion and Textile Research Journal
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• v.22 no.1
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• pp.38-54
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• 2020

This study examines the time causal relationship between the operation profit of the listed fashion companies and the macro variables. Operating profit data of 36 listed fashion companies from 2000 to 2017 has been used. Macro variables include household income, household expenditure, number of Korean overseas travelers, number of foreigner travelers and sentiment index. The study results are as follows. First, the number of outbound travelers from Korea has a negative effect on the operating profit of listed fashion companies; however the number of foreigner visiting Korea has a positive effect at 0 time lag. Second, the consumer sentiment index had a positive effect on the sales and the operating profits of the listed fashion companies with a time difference between the 3rd and the 4th quarter. Third, a disposable income has a positive effect on the operating profit of listed fashion companies. Last, educational expenses have a negative effect on operating profit with a time lag between the first and the second quarter. The findings can be used as useful information to analyze the fashion industry and help fashion companies improve their financial performances.

• 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 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.