Text Mining Analysis Research Articles

Emotional Construction and Consumer Retention in Otome Game Brands: A Case Study of Crisis Public Relations in "Light and Night"

In recent years, with the awakening of female consciousness and the flourishing of the gaming industry, otome games targeting female groups have emerged. These games stimulate continuous emotional consumption by guiding players to build virtual intimate relationships with game characters, thus unleashing enormous commercial potential. This paper, based on the Theory of Parasocial Interaction, selects two typical crisis events in the game production and consumption conversion process. By conducting text mining and public opinion analysis, this paper aims to analyze the emotional marketing, public relations strategies, and feasible measures of otome game brands.

A text-mining analysis of Latin America Universities’ mission statements from a ‘Third Mission’ perspective

ABSTRACT University Mission Statements (UMS) were born by imitating a common practice in the business world, where private organisations state their core values and main purposes. Recently, there has been growing interest in analysing UMS through the lens of the Third Mission (TM) of universities. Despite the increasing attention to UMS analysis of TM at the national level, there remains a dearth of research that conducts international comparisons of UMS across various countries utilizing a TM perspective. In this study, we adopt a text mining approach commonly employed in Natural Language Processing studies to categorize 1304 UMS from 19 Latin American countries based on generalized social and economic orientations of TM. By eschewing classifications reliant on specific TM aspects and activities, our analysis aims to facilitate meaningful regional and cross-country comparisons. On average, the orientation of UMS from Latin American countries is 82% social and 55% economic, significant variations observed across countries reflecting the particularities of each Higher Education System. Our results are comparable to previous research analysing single-country UMS, utilizing interpretive content analysis and a Third Mission lens. This evidence offers a quantitative perspective that aligns with existing regional narratives, enriching our understanding of the Third Mission across Latin America.

Return to the United States: Impact of Reshoring Announcements and Reshoring Risks on Market Valuation

With soaring labor and logistics costs in developing countries, supply chain disruptions during the COVID-19 pandemic triggered Western firms to “reshore” some of their offshore operations (performed in-house or outsourced) for certain strategically important products or production processes from foreign countries to their home countries. Although reshoring can create more domestic jobs and reduce supply chain risks, the impact of various external and internal risks associated with reshoring on market reaction remains unclear. This observation motivates us first to conduct a text mining analysis, revealing four important types of reshoring risks inherent to (1) foreign currency fluctuation, (2) intellectual property (IP) protection, (3) reshoring types (in-house, insourced, or outsourcing-to-outsourcing (OTO)), and (4) reshoring location choice (Republican- versus Democrat-led states). We then examine how these risk factors help explain the variations in reshoring’s market valuation based on 281 reshoring initiatives of 132 publicly traded firms in the United States announced between 2009 and 2022. Our empirical analysis reveals that the market reacts more positively to a firm’s reshoring announcement when the firm reshores under a high-currency-fluctuation environment or from countries with weak IP protection. However, the market’s reaction is more negative when the firm’s reshoring announcement entails insourced reshoring operations or when the reshored location is a Democrat- rather than Republican-led state. We do not find a significant market reaction to OTO reshoring. This paper was accepted by Vishal Gaur, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00599 .

텍스트마이닝을 활용한 영유아 교육에서 돌봄 연구의 동향 분석

Objectives The purpose of this study is to analyze the overall trend of care research and obtain directions and implications for follow-up research by exploring trends in ‘care’ research in infant and toddler education using text mining. Methods Method For this purpose, the title, keywords, and Korean abstract of the paper were analyzed through text mining. The subject of analysis was a total of 76 degree and academic journal papers published in Korea from 2004, when care research emerged in infant and toddler education, to 2024. Results Using LDA analysis, the text mining analysis results were visualized as a word cloud, and as a result of the analysis of papers over the entire analysis period, the most frequent keyword was ‘infant/toddler teacher’, and the same pattern was seen in the keywords analyzed by curriculum period. Research on care in infant and toddler education conducted over the past 20 years has focused on ‘infant and toddler teachers’ and ‘infants and toddlers’, and although there are a small number of topics related to care in infant and toddler education in each curriculum period, discourse analysis and social change have continued to be conducted. It was confirmed that the studies reflected had been conducted steadily. In the care discourse on infant and toddler education, it was confirmed that interest in physical and mental self-care is increasing, from parental care and infant and toddler teacher care to public care that the state is responsible for and society works with. Conclusions By conceptualizing the characteristics that emerged from the analysis results, we categorized the discovered knowledge, and suggested that academic discussions on the policy agenda for care in early childhood education should be active and implications for self-care.

Exploring Leadership and Other Influential Dimensions When Assessing Online Learning Modules Within Higher Education Institutions: Insights From Flow Theory and Two-Factor Principles

This study explores the concept of flow status as a crucial factor affecting student engagement in online learning, using Herzberg’s motivation-hygiene theory as an analytical framework. Herzberg proposed two main principles influencing learner satisfaction: motivating factors associated with increased satisfaction and hygiene factors associated with dissatisfaction avoidance. While both motivation and hygiene factors influence learner satisfaction, this study applies the concept of flow status and incorporates elements of leadership to the concept to examine the implementation of effective learning systems in higher education institutions. A qualitative approach was applied to data collected from 24 interviews with stakeholders in the field of learning, including students, tutors, curriculum designers/programme leaders and policy makers in education. Interviews were recorded, transcribed, and subjected to text mining analysis. Most students interviewed identified various hygiene factors as motivating factors, such as their grades and supportive guidance from their tutors, thereby underscoring the importance of leadership visualisation in learning design. These hygiene factors were particularly appreciated in the disruptive environment experienced during the COVID-19 period. Students expressed scepticism about pre-set online group work, suggesting a preference for the ability to choose to participate in various types of group work. Tutors emphasised the importance of motivating factors in student engagement, recognising the significance of students’ technological readiness and effective teamwork in relation to how well they concentrated on their learning. This study develops practical insights for higher education educators, considering contemporary technological impacts and the immersive effects of online module design. The results propose a conceptual model that fosters students’ ability to concentrate in disruptive environments, highlighting the necessity of clear leadership from both system designers and implementers to enhance student engagement in online learning. This study’s findings are expected to serve as a valuable analytical tool for stakeholders in the field of education.

Social Norms of Body Image for Young Women Over the Past Decade: A Text Mining Analysis of News Big Data

Social Norms of Body Image for Young Women Over the Past Decade: A Text Mining Analysis of News Big Data

Text mining on sustainability reports of top 40 airlines and bibliometric analysis of airline's sustainability

This study aims to comprehensively explore sustainability practices, guidelines, and emerging trends in the airline industry through an in-depth analysis of their sustainability reviews. A thorough bibliometric analysis of airline sustainability was conducted using the Scopus Database. Additionally, employing text-mining techniques, a meticulous analysis focused on the sustainability reports of the leading 20 airlines compared to the subsequent 20, considered as followers, in the textual examination of sustainability reports. The results revealed thematic disparities between these two cohorts. The top 20 airlines prioritized significant concerns such as safety and other sustainability-related aspects like biodiversity. In contrast, follower companies placed a higher emphasis on financial considerations. This analysis illuminates the primary focuses of airlines and the spectrum of sustainability-related issues. Moreover, offering valuable insights for both researchers and industry practitioners, this study presents a repository of pertinent data related to sustainability practices in the aviation sector. Integrating text mining and bibliometric analysis emphasizes essential facets of airline sustainability, resulting in a comprehensive overview of the research landscape.

텍스트 마이닝을 활용한 대학생 중소기업 취업 주요 이슈 분석

This study conducted text mining analysis using news data to objectively evaluate the effects of various policies, such as support for college student and small business employment, implemented during the tenures of past presidents. After collecting a total of 13,939 news articles, preprocessing such as removal of stop words was performed, and keyword analysis and correlation analysis were conducted. The news data was collected from 'Big Kinds' of the Korea Press Foundation. The main results analyzed are as follows: initially, a high-level analysis of the entire period showed that 'jobs' were the most frequently mentioned, followed by 'large companies', 'Seoul', 'workers', and 'job seekers'. Secondly, the yearly word cloud analysis results showed that President Moon Jae-in was associated with Covid-19, manufacturing, subsidies, minimum wage, in that order. President Park Geun-hye was associated with 'workers', youth, subsidies, manufacturing, in that order. President Lee Myung-bak was associated with 'unemployed', manufacturing, vocational high schools (Meister schools), college students, in that order. Thirdly, the TF-IDF analysis revealed distinct differences according to the timing of presidential changes. Additionally, among keywords related to government policies, terms such as National Employment Support System, Tomorrow Learning Credit, and Employment Encouragement Allowance emerged. Through this, it was possible to confirm that these support programs are mentioned in relation to college student employment in small businesses among various government support initiatives.

A Comparative Analysis on Discourse Reviews about Differentiated Semiotic Value Consumption Brand Groups in Cosmetics Consumers: Focusing on Text Mining

Purpose: This study began by investigating the similarities and differences among segmented markets within the color cosmetics industry, which are subdivided into low-cost color, high-priced, and imported color cosmetics, positioned as differentiated identities, and what are the similarities and differences between the segmented markets of the color cosmetics brand group.Methods: To perform this study, we used Python version 3.10.6 to crawl discourse from attracting value-driven consumers in the color cosmetics category on Naver Shopping Mall. After that, we completed the pre-processing steps and then conducted Word Cloud analysis, N-gram analysis, Topic Modeling analysis, Similarity analysis, and Sentiment analysis.Results: The text mining analysis showed several interesting findings. Notably, we found a new distribution channel where innovative consumers make purchases through live broadcasts performed within a short period. Furthermore, the analysis indicated that while low-priced and imported color cosmetics submarkets showed different logical results, they utilized similar discourse patterns. Furthermore, each segmented submarket was observed to possess a distinct positioning strategy.Conclusion: The consumer groups that utilized the taste value, the subject of comparative analysis, and used the low-cost, high-value, and imported color cosmetics have different identities. Consequently, the market segmentation and positioning of these brands differ, and consumer preferences vary. This allows us to outline the direction of the domestic color cosmetics industry and provide basic theoretical materials for marketing strategies. According to the research results obtained from each comparative research group, it will be possible to present a wide range of practical implications for management and marketing strategies to strengthen the market competitiveness of color cosmetics brand segments.

Digital Service Quality Measurement Model Proposal and Prototype Development

Traditional service quality models, which are survey-based methods, have been noted by researchers to contain operational errors in their application. Researchers criticize service quality models such as SERVQUAL and SERVPERF for containing operational errors, high implementation costs, and the issue of response recall. Additionally, these models face difficulties when applied to different sectors, as they were developed for the retail industry. The adaptation of the model, data collection, and processing have become outdated in comparison to current information processing technologies. With the rise in the use of social media, new communication paradigms have emerged. In this new paradigm, direct communication is established between people and institutions through social media. Institutions analyze social media data using text mining and sentiment analysis methods to keep up with this change. There are studies in the literature proposing new methods for measuring service quality by separately using text mining and sentiment analysis techniques. In this study, these two techniques have been combined. It is believed that combining these two techniques will result in a more robust service quality measurement model. Additionally, an application has been developed to demonstrate the functionality of the model. A municipality was specifically chosen as the application area because social media allows for fast, efficient, and inclusive participation between citizens and the municipality. The proposed model will enable the better identification of service quality deficiencies, leading to a more efficient use of municipal resources and fostering a more sustainable understanding of the municipality. With the implementation of the model, 463,886 tweets sent to the @ankarabbld and @mavimasa accounts were analyzed to identify 10 service quality dimensions and 106 keywords representing these dimensions, which would reveal the municipality’s service quality. The sentiment analysis technique was applied to 187,084 tweets containing the identified keywords. Thus, an attempt was made to uncover the municipality’s service quality.

How Subjective Well-being Evolved Over Time: Insights from Bibliometric and Text Mining Analyses

How Subjective Well-being Evolved Over Time: Insights from Bibliometric and Text Mining Analyses

Canine Bacterial Endocarditis: A Text Mining and Topics Modeling Analysis as an Approach for a Systematic Review.

Bacterial endocarditis (BE) is a severe infection of the endocardium and cardiac valves caused by bacterial agents in dogs. Diagnosis of endocarditis is challenging due to the variety of clinical presentations and lack of definitive diagnostic tests in its early stages. This study aims to provide a research literature analysis on BE in dogs based on text mining (TM) and topic analysis (TA) identifying dominant topics, summarizing their temporal trend, and highlighting any possible research gaps. A literature search was performed utilizing the Scopus® database, employing keywords pertaining to BE to analyze papers published in English from 1990 to 2023. The investigation followed a systematic approach based on the PRISMA guidelines. A total of 86 records were selected for analysis following screening procedures and underwent descriptive statistics, TM, and TA. The findings revealed that the number of records published per year has increased in 2007 and 2021. TM identified the words with the highest term frequency-inverse document frequency (TF-IDF), and TA highlighted the main research areas, in the following order: causative agents, clinical findings and predisposing factors, case reports on endocarditis, outcomes and biomarkers, and infective endocarditis and bacterial isolation. The study confirms the increasing interest in BE but shows where further studies are needed.

Research on "The Belt and Road Initiative" report of think tank based on theme evolution and identification--Taking 2013-2020 as an example.

China's "the Belt and Road Initiative" (BRI) is a top-level cooperation initiative among countries proposed by China, which has promoted China's cooperation with relevant countries in various aspects and fields. Research reports from think tanks and experts on the evaluation, analysis, and research conclusions of the BRI can reflect the stance, opinions, and demands of various countries abroad regarding the initiative. This paper takes the BRI reports of important think tanks in the " Global Go To Think Tank Index Report 2020" as the subject of its research, and analyzes the key points and development trends of foreign think tank research on the BRI by using text mining, topic evolution, and social network analysis. It provides reasonable suggestions and ideas for promoting the construction of the BRI and deepening related cooperation in China. Research shows that the thematic distribution of research reports on the BRI by think tanks is mainly focused on the fields of politics, economy, and military. The research areas are relatively stable, and there is not a strong trend of thematic evolution. The evolution paths are also mainly distributed in the fields of politics, economy, and military. There are not many expansions in the thematic evolution directions over the years, and there is a strong inheritance of themes. The connection between research themes and the main purpose of the BRI is somewhat inadequate, indicating a certain limitation in the understanding of the BRI.

Insights into wheat science: A bibliometric review using unsupervised machine learning techniques

Wheat (Triticum spp.) has been one of the most important cereal crops, serving as a source of protein and energy in the human diet. It remains a vital component of global food security, with extensive scientific literature dedicated to its study, although the large volume of literature often hinders global analysis. In this study, different unsupervised machine learning techniques, such as K-Nearest Neighbors (KNN) and Uniform Manifold Approximation and Projection (UMAP), text mining analyses, including word embeddings and statistical word analysis, and graph analysis methodologies, were applied to gain a deeper understanding of the wheat literature. The proposed bibliometric analysis was conducted and integrated with the Journal Citation Reports (JCR) to identify major wheat research trends in the PubMed literature. This analysis examined the evolution of these trends over time, evaluated the geographical distribution, impact, and research domains, and assessed author collaboration networks and the evolving relevance of different countries. Research on disease resistance, genetic modification, and dietary impact demonstrates a consistent increase in output, while interest in topics related to overcoming salt stress and enhancing animal feed appears to be diminishing. Interestingly, research on wheat germ agglutinin saw a surge in interest during the late 2000s, stabilizing thereafter. These trends underscore the dynamic nature of wheat research, driven by evolving priorities and technological advancements, particularly in genetics and omics tools. Moreover, the increasing significance of China in wheat research, including its size, impact, and networking, alongside longstanding leaders such as the United States, signals a shifting landscape in global wheat research.

A quantitative study of disruptive technology policy texts: An example of China’s artificial intelligence policy

Abstract Purpose The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention. This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence (AI) disruptive technology policy, and to put forward suggestions for optimizing China’s AI disruptive technology policy. Design/methodology/approach Develop a three-dimensional analytical framework for “policy tools-policy actors-policy themes” and apply policy tools, social network analysis, and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools, cooperative relationships among policy actors, and the trends in policy theme settings within China’s innovative AI technology policy. Findings We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close. Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects, forming a “center-periphery” network structure. Policy tool usage is predominantly focused on supply and environmental types, with a severe inadequacy in demand-side policy tool utilization. Policy themes are diverse, encompassing topics such as “Intelligent Services” “Talent Cultivation” “Information Security” and “Technological Innovation”, which will remain focal points. Under the themes of “Intelligent Services” and “Intelligent Governance”, policy tool usage is relatively balanced, with close collaboration among policy entities. However, the theme of “AI Theoretical System” lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities. Research limitations The data sources and experimental scope are subject to certain limitations, potentially introducing biases and imperfections into the research results, necessitating further validation and refinement. Practical implications The study introduces a three-dimensional analysis framework for disruptive technology policy texts, which is significant for formulating and enhancing disruptive technology policies. Originality/value This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts. It systematically evaluates China’s AI policies quantitatively, focusing on policy tools, policy actors, policy themes. The study uncovers the characteristics and deficiencies of current AI policies, offering recommendations for formulating and enhancing disruptive technology policies.

Shaping Digital Sovereignty in Russia: Actors and Debates

ABSTRACT The paper examines the construction of digital sovereignty ideas in authoritarian regimes, with a focus on Russia. Text mining and content analysis reveal that authoritarian regimes, in the face of new technologies, cannot simply impose a concept in a top-down manner. Instead, a diverse range of actors, including non-state entities, contribute to its formation, with their significance and participation fluctuating over time. In the context of Russia, digital sovereignty is not just a concept, but a tool used in international politics, economic development, and more, often sidelining the actual issues of technology development and their role in Russian politics.

Nursing researchers'concern about research activities during the COVID-19 pandemic: A secondary analysis of longitudinal survey data in Japan.

The coronavirus disease(COVID-19) pandemic has negatively affected research activities across various fields. This study aimed to determine nursing researchers'concerns about research activities during the COVID-19 pandemic in Japan and subsequent changes brought on by it. For this study, we conducted descriptive statistics and text mining analyses using data from two surveys conducted by the Japan Academy of Nursing Science (JANS) in the early days of the pandemic (first survey: mid-2020) and after 2 years (second survey: early 2022). Concerns about research activities were observed in 89% and 80% of the nursing researchers in the first and second surveys, respectively. Furthermore, concerns about "Difficulty in collecting research data" and "Content and quality of your research" were stronger in the second survey. Text mining analyses revealed that in the first survey, they were concerned about environmental changes and restrictions when proceeding with research during the COVID-19 pandemic, which was unfamiliar at the time. In the second survey, after overcoming environmental changes in the early stages of the pandemic, nursing researchers'concerns shifted to anxiety about the future, such as concerns about degree acquisition, employment and career advancement, and research results. The current study highlights various concerns among nursing researchers regarding research activities that have evolved over time during the pandemic. Academic societies must flexibly construct support measures for nursing researchers when a new infectious disease occurs. Such measures should be sensitive to the prevailing social circumstances and the evolving needs of researchers.

국내 음악 평생교육 연구의 의미구조

This article is to examine the semantic structure of previously published studies on lifelong music education in Korea. And Text mining analysis was conducted using abstracts of related papers as source data. The main analysis results are as follows. First, the most important topic was ‘music education for the elderly.’ Second, the main musical instrument was the piano, the genre was traditional music and classical music, and the form of education was more in group than individual. Third, the most important related institution was the university-affiliated lifelong education center, and most of the research focused on the tutees rather than the tutors. Fourth, mentions of the meaning and effects of lifelong music education occupy a certain proportion. Fifth, words related to research methodology also formed a certain group. Sixth, the network analysis confirmed the strong centrality of the elderly and music education. And CONCOR analysis showed that the main discourse topics discussed in this research subject largely converged into as follows. ‘Educational administration’, ‘research methodology and psychological effects’, ‘social meaning and prospects of the elderly music education’, and ‘tutees and educational materials’.

Impact of social innovation on value in cooperatives: a systematic literature review and a modeling attempt

Social innovation is often presented as a fashion or a trend in the world of business and management. However, it is important to note that it is much more than just a passing mode. It is usually classified as a way to boost the competitiveness and profitability of the social organizations, or a way to generate their value.For cooperatives, social innovation concerns more social and environmental issues. A perception that remains very limited given the managerial changes and organizational transitions of these cooperatives. Hence the need to develop a wider spectrum of reflection in terms of the value created by social innovation.Furthermore, the main objective of this communication is to model the conciliation between social innovation and value in cooperatives. In other words, the authors tried to pinpoint the concept of value in social organizations and study the impacts of social innovation on this value.To do so, the authors conducted a text mining analysis referring to 510 articles from Scopus engine by using IRaMuteQ 0.7 alpha 2 software.The results of this communication reveal a typology three categories of social value (economic, social, and organizational) and present a model describing the contribution of social innovation to value in cooperatives.This communication remains a reflection on modeling the complexity of the existing relationships between social innovation and value in cooperatives.

진주소방서 구급활동일지 평가소견 텍스트 마이닝 분석 연구

진주소방서 구급활동일지 평가소견 텍스트 마이닝 분석 연구