• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.131-137
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• 2018

People write reviews of numerous products or services on the Internet, in their blogs or community bulletin boards. These unstructured data contain important emotions and opinions about the author's product or service, which can provide important information for future product design or marketing. However, this text-based information cannot be evaluated quantitatively, and thus they are difficult to apply to mathematical models or optimization problems for product design and improvement. Therefore, this study proposes a method to quantitatively extract user's opinion or preference about a specific product or service by utilizing a lot of text-based information existing on the Internet or online. The extracted unstructured text information is decomposed into basic unit words, and positive rate is evaluated by using existing emotional dictionaries and additional lists proposed in this study. This can be a way to effectively utilize unstructured text data, which is being generated and stored in vast quantities, in product or service design. Finally, to verify the effectiveness of the proposed method, a case study was conducted using movie review data retrieved from a portal website. By comparing the positive rates calculated by the proposed framework with user ratings for movies, a guideline on text mining based evaluation of unstructured data is provided.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1108-1121
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• 2019

As the quality and level of life rise, many people are doing search for various pieces of information about tourism. In addition, users prefer the search methods reflecting individual opinions such as SNS and blogs to the official websites of tourist destination. Many of previous studies focused on a recommendation system for tourist courses based on the GPS information and past travel records of users, but such a system was not capable of recommending the latest tourist trends. This study thus set out to collect and analyze the latest SNS data to recommend tourist destination of high interest among users. It also aimed to propose an altered TSP algorithm to recommend the optimal routes to the recommended destination within an area and a system to recommend the optimal tourist courses by applying the Elasticsearch engine. The altered TSP algorithm proposed in the study used the location information of users instead of Dijkstra's algorithm technique used in previous studies to select a certain tourist destination and allowed users to check the recommended courses for the entire tourist destination within an area, thus offering more diverse tourist destination recommendations than previous studies.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.66-71
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• 2021

The purpose of this paper is to identify keywords related to museums, gamification, and visitors, and provide basic data that the museum market can be expanded by using gamification. That used to collect data for blogs, news, cafes, intellectuals, academic information by Naver and Daum which is Web documents in Korea, and Google Web, news, Facebook, Baidu, YouTube, and Twitter for analysis. For the data analysis period, a total of one year of data was selected from April 16, 2020 to April 16, 2021, after Corona. For data collection and analysis, the frequency and matrix of keywords were extracted through Textom, a social matrix site, and the relationship and connection centrality between keywords were analysed and visualized using the Netdraw function in the UCINET6 program. In addition, We performed CONCOR analysis to derive clusters for similar keywords. As a result, a total of 25,761 cases that analysing the keywords of museum, gamification and visitors were derived. This shows that the museum, gamification, and spectators are related to each other. Furthermore, if a system using gamification is developed for museums, the museum market can be developed.

• The Journal of Information Systems
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• v.30 no.2
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• pp.189-214
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• 2021

Purpose This paper aims to effectively utilize user-generated content (UGC) and analyze the market structure of a relatively new market which lacks rich user review information. Specifically, we propose a domain-specific text mining tool for the domestic craft beer market and visualize the market structure by incorporating how individual beer products are positioned in the perceptual map of consumers. Design/methodology/approach We collect user review information from Naver blogs, and extract words that describe beers. We identify semantic relationships between beer products through text mining, and then depending on these semantic relationships, construct a graph representing the market structure of the domestic craft beer market based on the consumer's perceptual map. Findings First, beer products produced in the same brewery are perceived as very similar to consumers. Second, only two products, 'Heukdang Milky Stout' and 'Gompyo', was noticeably distinguishable from other products. Third, even though 'Gyeongbokgung' is from a different brewery, it is located very close to the products of 'Jeju Beer' brewery such as 'Jeju Baeknokdam Ale' and 'Seongsan Ilchulbong Ale', which suggests the influence of 'landmark series.' We successfully show that our methodology effectively describes the market structure of the craft beer market.

• Journal of Distribution Science
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• v.19 no.4
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• pp.25-35
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• 2021

Purpose: This study aims to analyze the factors affecting customer satisfaction in the customer reviews of omni-channel, posted on Internet blogs, cafes, and YouTube using text mining analysis. Research, data, and Methodology: In this study, frequency analysis is performed and the LDA (Latent Dirichlet Allocation) is used to analyze social big data to respond to reviewers' reaction to the recently opened omni-channel shopping reviews by L Shopping Company. Additionally, based on the topic analysis, we conduct a sentiment analysis on purchase reviews and analyze the characteristics of each topic on the positive or negative sentiments of omni-channel app users. Results: As a result of a topic analysis, four main topics are derived: delivery and events, economic value, recommendations and convenience, and product quality and brand awareness. The emotional analysis reveals that the reviewers have many positive evaluations for price policy and product promotion, but negative evaluations for app use, delivery, and product quality. Conclusions: Retailers can establish customized marketing strategies by identifying the customer's major interests through text mining analysis. Additionally, the analysis of sentiment by subject becomes an important indicator for developing products and services that customers want by identifying areas that satisfy customers and areas that evoke negative reactions.

• Fashion & Textile Research Journal
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• v.24 no.1
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• pp.66-80
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• 2022

Even before the Covid-19 outbreak, the second-hand fashion market has been growing as the fashion industry strives towards sustainability. It has also accelerated due to the economic contraction caused by the pandemic. In previous studies, the second-hand market has been steadily studied; however, the research is insufficient compared to the diversified market. Therefore, this study investigates changes in consumers' perception of the second-hand fashion market affected by Covid-19. This study collected text data with the keyword 'second-hand fashion' from various blogs. We analyzed 24,000 posts before and after the Covid-19 outbreak by applying the LDA algorithm for topic modeling and content analysis. Seven and nine different topics for the period before and after the pandemic respectively were derived. The results revealed that during the pandemic the consumers realized the practical value of sustainability in their daily lives than they did before the pandemic. Furthermore, they tried to minimize transaction anxiety by using diverse platforms with advanced technology. They also realized economic value by buying and selling sneakers in the popular sneakers resale market. The results could help understand the rapidly growing second-hand fashion market during Covid-19.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.41-46
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• 2022

The purpose of this study is to confirm and analyze the impact on consumers through big data keyword analysis on weak food. For data collection, web documents, blogs, news, cafes, intellectuals, academic information, and Google Web, news, and Facebook provided by Naver and Daum were used as analysis targets. The data analysis period was set from January 2018 to December 2021. For data collection and analysis, the frequency and matrix of keywords were extracted through Textom, a social matrix site, and the relationship and connection centrality between keywords were analyzed and visualized using the Netdraw function among UCINET6 programs. In addition, CONCOR analysis was conducted to derive clusters for similar keywords. As a result of analyzing yakseon food with keywords, a total of 35,985 cases of collected data were derived. Through this, it was confirmed that medicinal food affects consumers. Furthermore, if a business model is created and developed through yakseon food, it will be possible to lead the popularization of yakseon food.

• International Journal of Knowledge Content Development & Technology
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• v.12 no.4
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• pp.41-65
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• 2022

This study would understand the overall perception of our society about public libraries, analyzing the texts related to public libraries, utilizing the semantic connection network & sentiment analysis. For this purpose, this study collected data from the last five years with keywords, 'Library' and 'Lifelong Learning Center' from January 1, 2016 through November 30, 2020 through the blogs and cafés of major domestic portal sites. With the collected data, text mining, centrality of keywords, network structure, structural equipotentiality, and sensitivity analyses were conducted. As a result of the analysis, First, 'reading' and 'book' were identified as representative keywords that form the social perception of public libraries. Second, it turned out that there were keywords related to the use of the library and the untact service due to the recent spread of COVID-19. Third, in seeking a plan for the development of public libraries through the keywords drawn to have positive meanings, it is necessary to create continuous services that can form a new image of the library, breaking away from the existing fixed role and image of the library and increase the convenience of use. Fourth, facilities and facilities for library services were recognized from a neutral point of view. Fifth, the spread of infectious diseases, social distancing, and temporary closure and closure of libraries are negatively related to public libraries, and awareness of librarians has been identified as negative keywords.

• Journal of Korean Academy of Nursing
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• v.53 no.4
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• pp.439-452
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• 2023

Purpose: This study aimed to derive considerations for the enactment of nursing law by analyzing the trends and social perceptions of nursing law mentioned in major daily newspapers, cafes, and blogs. Methods: Main texts and comments that included nursing law as a keyword were collected from major daily news and online postings from January 2021 to August 2022. The data collected through web crawling were analyzed using a TousFlux program used for big data analysis. Results: During the period of study, the awareness level around nursing law enactment increased. In particular, public concern over nursing law enactment intensified due to the two political parties' policy pledges related to nursing law in January 2022 and the failure to introduce the nursing law to the national assembly judiciary committee in May 2022. Except in December 2021, public perception of nursing law enactment was generally favorable, with public opinion tilting more in favor of than against enactment. Conclusion: Public opinion should be considered when drafting and implementing the nursing law to make it easier for the people to understand what the law constitutes. In addition, it is necessary to pay attention to and continuously promote the relationship between medical care and nursing in the nursing law system of developed nations. Lastly, nursing law enactment can enhance nurses' retention intention and provide a sense of efficacy to medical services.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.332-341
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• 2023

Notable trends in the restaurant start-up market after the lifting of social distancing include increasing interest in start-ups, emphasizing the importance of food quality and diversity, decreasing the relative importance of delivery services, and increasing interest in certain industries. The data collection period is three years from April 2021 to May 2023, including before and after social distancing, and texts extracted from blogs, news, cafes, web documents, and intellectuals provided by Naver, Daum, and Google were collected. For the collected data, the top 30 words were derived through a refining process. In addition, based on April 2021, the application period of social distancing, data from April 2021 to April 2022, and data from May 2022 to May 2023, Through these changes in trends, founders can capture new opportunities in the market and develop start-up strategies. In conclusion, this paper provides important insights for founders in accurately understanding the changes in food service start-up trends and in developing strategies appropriate to the current market situation.