• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.153-167
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• 2019

This study aims to identify the media agenda and to compare each media agenda by media and by time period, analyzing the news about 2019 Gangwon-do's wild fire reported by 37 Korean news media. Using the topic modeling algorithm and semantic network analysis, this study inspected the configuration of the network media agenda and examined the intermedia agenda setting effect by using QAP correlation analysis. Results showed that the sensational media agenda with the attributes such as victim aid and political conflict and the similarity of each media agenda for this disaster reporting.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.5
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• pp.1019-1035
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• 2020

This study examines the millennial generation, who express themselves and share information on social media after experiencing constantly changing 'hot places' (places of interest) in contemporary cities, with the goal of analyzing space consumption behaviors. Data were collected via an Instagram crawler application developed with Python 3.4 administered to 19,262 posts using the term 'hot places' from November 1 and December 15, 2019. Issues were derived from a text mining technique using Textom 2.0; in addition, semantic network analysis using Ucinet6 and the NetDraw program were also conducted. The results are as follows. First, a frequency analysis of keywords for hot places indicated words frequently found in nouns were related to food, local names, SNS and timing. Words related to positive emotions felt in experience, and words related to behavior in hot places appeared in predicate. Based on importance, communication is the most important keyword and influenced all issues. Second, the results of visualization of semantic network analysis revealed four categories in the scope of the definition of "hot place": (1) culinary exploration, (2) atmosphere of cafés, (3) happy daily life of 'me' expressed in images, (4) emotional photos.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.164-170
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• 2023

The purpose of this study is to analyze the big data of Adidas brand participating in the Qatar World Cup in 2022 as a FIFA partner to understand useful information, semantic connection and context from unstructured data. Therefore, this study collected big data generated during the World Cup from Adidas participating in sponsorship as a FIFA partner for the 2022 Qatar World Cup and collected data from major portal sites to understand its meaning. According to text mining analysis, 'Adidas' was used the most 3,340 times based on the frequency of keyword appearance, followed by 'World Cup', 'Qatar World Cup', 'Soccer', 'Lionel Messi', 'Qatar', 'FIFA', 'Korea', and 'Uniform'. In addition, the TF-IDF rankings were 'Qatar World Cup', 'Soccer', 'Lionel Messi', 'World Cup', 'Uniform', 'Qatar', 'FIFA', 'Ronaldo', 'Korea', and 'Nike'. As a result of semantic network analysis and CONCOR analysis, four groups were formed. First, Cluster A named it 'Qatar World Cup Sponsor' as words such as 'Adidas', 'Nike', 'Qatar World Cup', 'Sponsor', 'Sponsor Company', 'Marketing', 'Nation', 'Launch', 'Official', 'Commemoration' and 'National Team' were formed into groups. Second, B Cluster named it 'Group stage' as words such as 'Qatar', 'Uruguay', 'FIFA' and 'group stage' were formed into groups. Third, C Cluster named it 'Winning' as words such as 'World Cup Winning', 'Champion', 'France', 'Argentina', 'Lionel Messi', 'Advertising' and 'Photograph' formed a group. Fourth, D Cluster named it 'Official Ball' as words such as 'Official Ball', 'World Cup Official Ball', 'Soccer Ball', 'All Times', 'Al Rihla', 'Public', 'Technology' was formed into groups.

• Language and Information
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• v.14 no.1
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• pp.197-227
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• 2010

This study reviews (i) the (dis)advantages of two major semantic theories of focus, i.e., the Alternative Semantics (AS) and the Structured Meaning Theory (SM), and (ii) the hybrid analysis consisting of these two theories proposed by Krifka (2006). After pointing out a problem of this hibrid analysis, this study attempts to show the third alternative, i.e., the theory of identificational predication for focus can solve this problem.

• Journal of Contemporary Eastern Asia
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• v.16 no.2
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• pp.110-144
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• 2017

Unlike most languages, written Chinese has no spaces between words. Word segmentation must be performed before semantic network analysis can be conducted. This paper describes how to perform Chinese word segmentation using the Stanford Natural Language Processing group's Stanford Word Segmenter v. 3.8.0, released in June 2017.

• Journal of The Korean Association For Science Education
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• v.32 no.5
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• pp.823-840
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• 2012

The importance of teaching the nature of science (NOS) has been emphasized in the science curriculum, especially in the science curriculum for science-gifted students. Nevertheless, few studies concerning the structure and formation of students' mental model on NOS have been carried out. This study aimed to explore science-gifted students' understanding of 'fact', 'hypothesis', 'theory', 'law', and 'scientificness' by utilizing semantic network analysis. One hundred ten science-gifted middle school students who were selected by a national university participated in this study. We collected students' written responses of five items and analyzed them by the semantic network analysis(SNA) method. As a result, the core ideas of students' understanding of 'fact' were proof and reality, of 'hypothesis' were tentativeness and uncertainty, of 'theory' was proven hypothesis by experimentation, of 'law' were absoluteness and authority, and of 'scientificness' were factual evidence, verifiability, accurate and logical theoretical framework. The result of integrated semantic network illustrated that the viewpoint of science-gifted students were similar to absolutism and logical positivism (empiricism). Methodologically, this study showed that the semantic network analysis method was an useful tool for visualization of students' mental model of scientific conceptions including NOS.

• Journal of Korean Library and Information Science Society
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• v.47 no.3
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• pp.95-114
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• 2016

The purpose of this preliminary study is to collect specific examples of book reports and understand semantic characteristics of them through semantic network. The analysis was conducted with 23 book reports which classified by three groups. The keywords were selected from the of book reports. Five types of keyword network were composed based on co-occurrence relations with keywords. The result of this study is following these. First, each keyword network of book reports of groups and individuals is shown to have different structural characteristics. Second, each network has different high centrality keywords according to the result analysis of 3 types of centrality(degree centrality, closeness centrality, betweenness centrality). These characteristic means that keyword network analysis is useful in recognizing the characteristics of not only groups' and but also individual's book reports.

• Journal of the Korean earth science society
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• v.34 no.2
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• pp.173-186
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• 2013

The purpose of the study was to investigate the change of students' conceptual structures about sedimentary rocks through the field trip. A semantic network analysis method was utilized to assess the change. An open-ended questionnaire was developed to assess high school students' knowledge of sedimentary rock including its definition, classification, formation process, and characteristics. Fifteen high school students participated in the field trip of this study. The text data were analyzed using the semantic network analysis method. Results are as follows. First, high school students' conceptual structures about sedimentary rocks were more expanded after the field trip. Second, students' conceptual structures formed a 'small world network' by combining the sub-clusters. Third, the size of students' conceptual structures was decreased after a few month of field trip. Nonetheless, the connection among the clusters remained the same.

• Smart Media Journal
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• v.9 no.2
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• pp.69-77
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• 2020

Unlike the past, which was limited to terrestrial broadcasts, many dramas are currently being broadcast on cable channels and the Internet web. After watching the drama, viewers actively express their opinions through reviews and studies related to the analysis of these reviews are actively being conducted. Due to the nature of the drama, the genre is not clear, and due to the various age groups of viewers, reviews and ratings from other viewers help to decide which drama to watch. However, since it is difficult for viewers to check and analyze many reviews individually, a data analysis technique is required to automatically analyze them. Accordingly, this paper classifies the topics of reviews that have an important influence on drama selection and reclassifies them into semantic topics according to the similarity of words. In addition, we propose a model that classifies reviews into sentences according to semantic topics and sentiment analysis through sentiment words.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.3
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• pp.417-439
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• 2009

In this study, the intellectual structure of Records Management and Archival Science in Korea was analyzed based on the syntactic and semantic structure analysis of article titles. The data used in this study were 344 articles from three major representative journals in the field of Records Management and Archival Science, published from 1999 to 2008. The results of the syntactic and semantic structure analysis of article titles show that the three role concepts of keywords are 'research domain', 'research object', and 'research focus'. Keywords in article titles were clustered into the core subject areas after they were assigned three concepts. Based on the results of cluster analysis, the intellectual structure of Records Management and Archival Science in Korea was proposed.