Semantic Network Analysis Research Articles

중등 예비교사의 좋은 수업에 대한 인식: 의미연결망 분석을 중심으로

Objectives The purpose of this study is to explore the perception of good instruction that secondary pre-service
 teachers think by applying semantic network analysis.
 Methods For this purpose, data were collected using open questionnaires targeting 334 Secondary School preservice
 teachers. Keywords, centrality index, and bigrams were extracted through the collected data and the main
 results were visualized.
 Results The results were as follows: First, regarding the perception of good instruction, ‘instruction’ is located in
 the middle of the cognitive structure and forms the relationship and connection strength based on the words
 ‘student’, ‘teacher’, ‘thinking’, ‘class contents’, and ‘knowledge’. This means that ‘instruction’ is located in the middle
 of the recognition structure, and the relationship and connection strength were formed based on the words
 of ‘student’, ‘teacher’, ‘thinking’, ‘class contents’, ‘knowledge’. Second, it was found that the main perceptions of
 good instruction were classes that convey subject knowledge so that students can understand the contents of the
 class well, classes that include active participation activities of students, classes that draw students' interest,
 classes with questions that can give various thoughts or opinions, classes that educate students to have learning
 goals, and classes that interact with teachers and students.
 Conclusions The results of this study will provide teachers with a good opportunity to reflect on the instruction,
 and it can be used as useful information for practical directions such as class planning for good instruction.

A replication and extension of the Personal Social Media Ecosystem Framework

Abstract The recently forwarded Personal Social Media Ecosystem Framework (PSMEF) allows researchers to study social media in terms of generalized types of user interfaces. This study formally extended the PSMEF via the Digital User Interface Model and replicated previous work by evidencing the existence of new (e.g., Overtly Algorithmic Content Pages) and validating previously identified types of user interfaces (e.g., Home Pages and Chats/Messages) that make up individuals’ personal social media environments. Using topic modeling (i.e., Latent Dirichlet Allocation) and a novel mixed methods approach (i.e., schematic semantic network analysis), we quantitatively evidenced four distinct classes of user interfaces based on open-ended descriptions that participants provided for six popular social media platforms (i.e., Instagram, Snapchat, Facebook, Twitter, TikTok, and YouTube). Results inform on the qualitative differences between distinct user interface classes that underwrite users’ experiences over social media, with implications for conceptualization and operationalization related to social media use.

An Analysis on Needs for National Parks by Age Group using Semantic Network Analysis

Background and objective: National parks are places people from all walks of life can utilize. However, many existing studies are limited in suggesting management plans that fit the characteristics of different age groups of people visiting national parks. Therefore, this study aims to identify the requirements for national parks and set the direction for national park services by age group.Methods: This study collected the requirements for national parks from 902 people of different ages through an online self-administered questionnaire and analyzed them through semantic link network analysis.Results: As a result of the analysis, there were management requirements in two areas related to the environment (rubbish and ecological environment, facilities environment such as safety and forest fires) and one area related to the economy, and a monitoring system for rubbish and ecological environment and a private land management policy for the economy were identified as alternatives. The younger generation was interested in environmental issues, the older generation was interested in economic issues, and the elderly was interested in the facilities environment for safety and forest fires. Therefore, it was found that environmental campaigns should be conducted for the younger generation, admission fees and parking rates should be improved for the older generation, and a safe tour environment should be provided for the elderly.Conclusion: The needs analysis found that it is necessary to establish a monitoring system for all national parks from an environmental perspective and improve the management policy about private land from an economic perspective. Furthermore, environmental campaigns and events were required for the younger generation, benefits related to entrance fees for the elderly, and policies to create a safe facilities environment for the elderly. Establishing such national park policies will improve the satisfaction of users.

Sino-US Green Finance Cooperation (2016–2022)—An Empirical Analysis Based on Texts

Green finance cooperation is an organic component of international cooperation among the globalization, but few studies focused on the status of green finance cooperation. Previous studies have confirmed the value of green finance cooperation within countries for green development, laying the foundation for initiating discussions on green finance cooperation between countries. This paper uses quantitative methods such as word frequency statistical analysis, visualization cloud map, and semantic network analysis to analyze the development process of Sino-US green finance cooperation on the basis of selected green finance cooperation texts. The results show that multi-dimensional parties in Sino-US green finance cooperation play a role in promoting the cooperation process and resolving differences in cooperation cognition. Secondly, the auxiliary elements of Sino-US green finance cooperation have achieved value mobilization and effective integration. Thirdly, the development effect of Sino-US green finance cooperation has a relatively sensitive response to external shocks represented by political factors.

Using Online Customer Reviews to Understand Customers’ Experience and Satisfaction with Integrated Resorts

With the diverse demands of customers for their accommodation experiences, integrated resorts have emerged and have been popular among tourists. Meanwhile, in the context of Tourism and Hospitality 4.0, online information collection and marketing has been one of the most effective strategies for industry operators and investors. Thus, the present study adopted online reviews generated by customers themselves to explore their experience and satisfaction with integrated resorts. With the qualitative and quantitative analyses of online customer reviews, firstly, 70 prominent keywords were extracted from the reviews, and some general understanding was obtained from the top frequent words. After conducting a semantic network analysis of the top frequent words, significant and central words in the network were identified, which were utilized as basic variables for the specific exploration of factors reflecting customers’ experience. In the meantime, four dimensions, “Amenities”, “Entertainment”, “Tourist”, and “Atmosphere”, were garnered via CONCOR analysis to illustrate the key dimensions of customers’ cognition and awareness towards resorts. At last, a quantitative analysis with an exploration factor analysis and a linear regression analysis were conducted to testify the correlation between customer experience and satisfaction. Three factors, “environment”, “staff service”, and “food service”, have a significant effect on customer satisfaction. Analytics of online customer reviews with a big volume, which differ from traditional research methods by designed structure or content, could generate more comprehensive understanding and cognition of research subjects. The findings obtained from analytics could provide baseline information for future research and industry development and promotion.

Environmental complaint insights through text mining based on the driver, pressure, state, impact, and response (DPSIR) framework: Evidence from an Italian environmental agency

Individuals, local communities, environmental associations, private organizations, and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality, illegal waste disposal, water contamination, and general pollution. Environmental complaints represent the expressions of dissatisfaction with these issues. As the time-consuming of managing a large number of complaints, text mining may be useful for automatically extracting information on stakeholder priorities and concerns. The paper used text mining and semantic network analysis to crawl relevant keywords about environmental complaints from two online complaint submission systems: online claim submission system of Regional Agency for Prevention, Environment and Energy (Arpae) (“Contact Arpae”); and Arpae's internal platform for environmental pollution (“Environmental incident reporting portal”) in the Emilia-Romagna Region, Italy. We evaluated the total of 2477 records and classified this information based on the claim topic (air pollution, water pollution, noise pollution, waste, odor, soil, weather-climate, sea-coast, and electromagnetic radiation) and geographical distribution. Then, this paper used natural language processing to extract keywords from the dataset, and classified keywords ranking higher in Term Frequency-Inverse Document Frequency (TF-IDF) based on the driver, pressure, state, impact, and response (DPSIR) framework. This study provided a systemic approach to understanding the interaction between people and environment in different geographical contexts and builds sustainable and healthy communities. The results showed that most complaints are from the public and associated with air pollution and odor. Factories (particularly foundries and ceramic industries) and farms are identified as the drivers of environmental issues. Citizen believed that environmental issues mainly affect human well-being. Moreover, the keywords of “odor”, “report”, “request”, “presence”, “municipality”, and “hours” were the most influential and meaningful concepts, as demonstrated by their high degree and betweenness centrality values. Keywords connecting odor (classified as impacts) and air pollution (classified as state) were the most important (such as “odor-burnt plastic” and “odor-acrid”). Complainants perceived odor annoyance as a primary environmental concern, possibly related to two main drivers: “odor-factory” and “odors-farms”. The proposed approach has several theoretical and practical implications: text mining may quickly and efficiently address citizen needs, providing the basis toward automating (even partially) the complaint process; and the DPSIR framework might support the planning and organization of information and the identification of stakeholder concerns and priorities, as well as metrics and indicators for their assessment. Therefore, integration of the DPSIR framework with the text mining of environmental complaints might generate a comprehensive environmental knowledge base as a prerequisite for a wider exploitation of analysis to support decision-making processes and environmental management activities.

Deep Analysis of the Homogenization Phenomenon of the Ancient Water Towns in Jiangnan: A Dual Perspective on Landscape Patterns and Tourism Destination Images

Attractiveness plays a key role in the marketing activities that promote the sustainable development of tourist destinations. Many researchers have acknowledged that regional tourist destinations face challenges in attracting tourists due to homogeneity. Achieving sustainability of the ecological landscapes of tourist destinations, creating a unique image that differentiates them from other destinations, and fostering cooperative relationships among regional tourist destinations remain significant challenges. To investigate the reasons behind this and potential solutions to the homogeneity of tourist attractions in regional tourist destinations, our research team employed methods including landscape pattern analysis, tourist destination image analysis, principal component analysis (PCA), clustering algorithm (K-means), text analysis, word frequency analysis, sentiment analysis, and semantic network analysis. Through an in-depth examination of the homogenization phenomenon in the ancient towns of the southern Yangtze River, our research confirms that the spatial form of these ancient towns has become increasingly simple and scattered over time, with a concentration of the same land use types. Half of the sampled ancient towns had exhibited homogeneity in landscape patterns by 2020, with pronounced homogeneity observed in cultivated land, water networks, and hand-made land. Excessive commercialization has further contributed to the loss of local characteristics, resulting in homogeneity issues in the image of tourist destinations in the Jiangnan water towns and ancient towns. This article discusses the characteristics of homogeneity in the ancient towns of the Jiangnan water towns and their practical implications, offering valuable insights and experiences to tourism planners, designers, researchers, and other stakeholders engaged in the study of sustainable regional tourism destinations.

팀기반학습으로 운영된 유아동작 모의수업 경험에 대한 의미연결망 분석

Objectives This study examined the meaning and impact of team-based learning (TBL) on early childhood movement education by analyzing the reflective materials on teaching practices written by prospective early childhood teachers participating in TBL.
 Methods The participants of this study were 45 students enrolled in a 4-year early childhood education program in G City. The movement education class they took involved the design and demonstration of simulated lessons, which were conducted using the Team-Based Learning (TBL) approach. After completing a semester of classes, the learners wrote self-reflection materials on TBL and simulated lessons in the context of movement education. The qualitative data from these materials were analyzed using KrKwic and UCINET to extract keywords and perform centrality and semantic network analysis.
 Results Firstly, the common theme keywords that emerged from the TBL activities and simulated teaching demonstration experiences were ‘lesson plan’, ‘activity’, ‘movement’, ‘class’, ‘children’, ‘simulated teaching’, ‘feedback’, and ‘interest’. Secondly, the keywords associated with TBL activities included ‘time’, ‘team members’, ‘groups’, ‘opinions’, ‘active participation’, ‘challenges’, ‘parts’, ‘materials’, ‘expression’, ‘practice’, ‘modification’, and ‘utilization’. The semantic network analysis of TBL activities revealed a meaningful structure related to simulated teaching and the experiences of TBL activities. Thirdly, the keywords identified in relation to simulated teaching experiences were ‘teacher’, ‘thoughts’, ‘progress’, ‘experience’, ‘field’, ‘assistance’, ‘application’, ‘methods’, ‘process’, ‘rules’, ‘improvement’, and ‘demonstration’. The keyword ‘teacher’ showed high values in terms of linkages, degree centrality, closeness centrality, and betweenness centrality. The proximity centrality of ‘progress’, ‘experience’, and ‘application’ was high, while the betweenness centrality was low. The semantic network structure reflected the effectiveness of learning and application through the preparation and implementation phases of simulated teaching.
 Conclusions The semantic network analysis of early childhood movement education simulated teaching experiences conducted through TBL revealed that prospective early childhood educators perceive TBL activities and simulated teaching experiences as highly significant. These findings underscore the importance of learner-engaged instructional approaches in enhancing the teaching competencies of prospective early childhood educators.

Study on the Perceived Brand Image of Nongfu Spring based on UGC Data

Brand image is a key factor in attracting consumers, and User Generated Content (UGC) on e-commerce platforms accurately reflects consumers' perception and preferences for brand image to a certain extent.This paper takes the reviews of products sold on the JD platform as samples, and takes the reviews of Nongfu Spring products before 2023 as the research object. Through collecting UGC and using word frequency analysis, semantic network analysis, and emotion analysis in the ROST CM6.0 analysis software, this paper conducts a systematic study on the brand image perception of Nongfu Spring products. Research has found that consumers' perception of the image of Nongfu Spring products can be divided into three dimensions: product, service, and price. In addition, consumers show a positive emotional tone towards the product quality and logistics of Nongfu Spring products, and the factors that affect the negative emotions of Nongfu Spring include individual product issues and slow delivery. Finally, propose discussions and suggestions.

Analysis of the issues that emerged in the revision of the national social studies curriculum in South Korea: Text mining and semantic network analysis of the comments at the public hearing on YouTube

In South Korea, curriculum is revised, made public and implemented under a system known as a nation led curriculum. The South Korean national curriculum was completely revised 10 times between 1946 and 2015. At present, a complete revision is underway to replace the current 2015 national curriculum which is called the 2022 revised national curriculum. This study aims to analyze stakeholders’ responses to the YouTube public hearing on social studies curriculum according to the 2022 revised national curriculum in South Korea in order to understand the context and causes of the issues that emerged. Text mining, semantic network analysis and word cloud techniques were employed to identify issues. As a result, three issues were identified in the social studies curriculum: the balanced development of general elective subjects in high school; the separation of social sciences and geography and division of textbooks in middle school social studies and the separate listing of four subjects, specifically geography, social sciences, history and morals. The issues revealed in this study provide beneficial implications for future social studies curriculum development, revision as well as the development of future research.

Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks.

To date, the application of semantic network methodologies to study cognitive processes in psychological phenomena has been limited in scope. One barrier to broader application is the lack of resources for researchers unfamiliar with the approach. Another barrier, for both the unfamiliar and knowledgeable researcher, is the tedious and laborious preprocessing of semantic data. We aim to minimize these barriers by offering a comprehensive semantic network analysis pipeline (preprocessing, estimating, and analyzing networks), and an associated R tutorial that uses a suite of R packages to accommodate the pipeline. Two of these packages, SemNetDictionaries and SemNetCleaner, promote an efficient, reproducible, and transparent approach to preprocessing linguistic data. The third package, SemNeT, provides methods and measures for estimating and statistically comparing semantic networks via a point-and-click graphical user interface. Using real-world data, we present a start-to-finish pipeline from raw data to semantic network analysis results. This article aims to provide resources for researchers, both the unfamiliar and knowledgeable, that reduce some of the barriers for conducting semantic network analysis. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

대학생의 자기표현적 글쓰기 양상 고찰

The purpose of this study is to analyze writing aspects in 110 expressive writing results conducted in liberal arts at D University using text mining and semantic network analysis. To this end, 110 pieces were finally selected for analysis. The analysis target went through a text preprocessing process such as synonymy combination and elimination of non-terms, then extracted key words and analyzed the connection relationship through network analysis. As a result of the analysis of the frequency of keywords, 'thought', 'exercise', 'friend', 'people', and 'life' were emphasized, and as a result of the analysis of the language network, “Exercise-Health-Soccer-Military-Taekwondo-Confidence,” “Picture-Draw-Admission,” “Friends-Friendly -Thanks,” and “Music-Enjoy-Singing-Sharing” were the main cluster words. As a result of the study, the author's self-expressive writing as a university student appeared in ways of individual internal reflection, self-identity formation, self-healing, emotional expression, and self-consolation. Examining university students' expressive writing aspects in liberal arts is meaningful in that it can be an opportunity to identify the characteristics of university students with clear environmental changes, seek classes necessary for future liberal arts education, and further explore their individual lives.

Research on Reform of Teaching Content of Supply Chain Management Course in Application-oriented Universities

New business format and model put forward new requirements for post competence of business talents. Under the new business background, aiming at reform of teaching content of supply chain management course in application-oriented universities, Crawler Technology is used to obtain job responsibility data of current actual enterprise recruitment. Through Text Mining Technology such as high-frequency word analysis and semantic network analysis, the focus of actual job responsibilities and abilities is deeply explored, so as to clarify the focus of current supply chain management course teaching content and put forward corresponding teaching reform suggestions. The research results show that suppliers, logistics, planning and inventory and others are the main concerns of the current job responsibilities. It is suggested that the teaching content of supply chain management course should be designed from supplier and procurement management, supply chain logistics planning and scheme design, demand and inventory optimization, demand and production planning management, product cost and quality control, supply chain system coordination and optimization, and supply chain strategy and operation.

Is the circular economy proposed as sustainability in firm mission statements? A semantic analysis

Clean production, the circular economy and eco-innovation aim at supporting global resilience and sustainability. The present work examines the conceptualization of the circular economy in Italy, via publicly available social media data. Specifically, we conducted a mixed-method analysis of the mission statements of Italian firms, as published on LinkedIn. The semantic network analysis underlined three emergent themes within these statements, referring to sustainability, technology, and production and consumption. A cluster analysis identified the most frequently occurring keywords in each of these themes, which included “sustainable development”, “technology”, “waste”, “products”, “management” and “materials.” The theme of sustainability was further analyzed with respect to its three main pillars (i.e., social, economic, environmental). According to absolute values, approximately 40% of all firms referenced all of these pillars in their mission statements. The results point to the need for interdisciplinarity at the firm level to ensure competitiveness and greater emphasis on the social component of sustainability.

Effects of FDI, External Trade, and Human Capital of the ICT Industry on Sustainable Development in Taiwan

Understanding how international trade, FDI and human capital (FDI-HC and ET-HC) in the ICT industry affect Taiwan’s stable economic growth between 2001 and 2020 is the main objective of this study. The empirical analysis method used in this study is mainly divided into two steps: First, it uses variables with reliability and authenticity as keywords for primary, data mining, and semantic network analysis (SNA). Second, it investigates the long- and short-term interactions between the variables using the vector error correction model (VECM). The results of data mining and SNA using FDI and ET as keywords reveal that terms connected to HC have high levels of centrality, clustering, and frequency. This finding implies that the variables FDI-HC and ET-HC are reliable and can be utilized as interaction variables. Moreover, FDI–HC and ET–HC exert positive short- and long-term influences on GDP, and ET–HC exerts strong mid- to long-term impacts on GDP, FDI–HC, and ET.

Forecasting consumer confidence through semantic network analysis of online news

This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. Using over 1.8 million online articles on Italian media covering four years, we calculate the semantic importance of specific economic-related keywords to see if words appearing in the articles could anticipate consumers’ judgments about the economic situation and the Consumer Confidence Index. We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation. Our indicator offers a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods.

Digital city diplomacy and international cities networks: Collaboration and city branding strategies around climate issues

Our study refers to what the communication of city networks looks like regarding mentions and frames and if the digital communication activities of city networks could primarily serve as collaborative diplomacy of governance or are motivated by self-interests such as city branding. Our theoretical frame integrates Castells’ (2010, 2009) networked society and communication power and combines various concepts from the public (digital) diplomacy, public relations, and strategic communication. In a semantic network analysis of Tweets, we analysed the communication strategies of three city networks according to their digital content ( n = 47,226) and more specifically each network's top 50 hashtags and mentions therein. We find that city networks use three different Twitter communication strategies. We propose a model describing topic-, city/mayor-, and hybrid-oriented strategies. The analysis of the three city groups undertaken here has extended our knowledge of global urban governance communication strategies in burgeoning city diplomacy.

Information Alienation and Circle Fracture: Policy Communication and Opinion-Generating Networks on Social Media in China from the Perspective of COVID-19 Policy

The emergence of the Internet and social media provides a new platform for information diffusion, promoting the interaction among relatively independent participants in the opinion market and changing the balance of the intrinsic mechanism and external dynamics based on political communication. In this way, it is necessary to investigate the new interactive landscape of political communication and political discourse regarding digital media. In this study, we conduct a social and semantic network analysis of the dissemination and public opinion generation landscape of the COVID-19 “New Ten Articles” policy communication by the Chinese government, exploring the network relationships and emotional value interactions behind the contact of a new public policy in China. The results show that, in the political communication system, the influence of information’s position in the communication field has surpassed the information source impact, and the power of network opinion leaders is significant; the policy communication network presents a situation of “identity status circle” division, and the information circle connection presents a trend of fracture and barrier thickening, which may cause policy information alienation and social opinion polarization risk; the imbalance between policy information supply and public demand is further enhanced, and the negative emotion “cloud” is distributed on a scale and condenses into grassroots social governance pressure; and the content released by some key opinion leaders, experts, online media, and local mainstream media accounts is significantly correlated with network emotions. These emotions continue to spread in subsequent discussions, and to some extent, influence the formation process of political public opinion.

Bridging Echo Chambers? Understanding Political Partisanship through Semantic Network Analysis

In an era of intense partisanship, there is widespread concern that people are self-sorting into separate online communities which are detached from one another. Referred to as echo chambers, the phenomenon is sometimes attributed to the new media landscape and internet ecosystem. Of particular concern is the idea that communication between disparate groups is breaking down due to a lack of a shared reality. In this article, we look to evaluate these assumptions. Applying text and semantic network analyses, we study the language of users who represent distinct partisan political ideologies on Reddit and their discussions in light of the January 6, 2021, Capitol Riots. By analyzing over 58k posts and 3.4 million comments across three subreddits, r/politics, r/democrats, and r/Republican, we explore how these distinct groups discuss political events to understand the possibility of bridging across echo chambers. The findings of this research study provide insight into how members of distinct online groups interpret major political events.

Development of a “List of Cultural Landscape Adjectives” Using Big Data and Semantic Network Analyses

Development of a “List of Cultural Landscape Adjectives” Using Big Data and Semantic Network Analyses