An Analysis of Writing Items in College Scholastic Ability Test(CSAT) Using Text Mining and Semantic Network Analysis Methodologies

2021 ◽  
Vol 43 (4) ◽  
pp. 827-848
Author(s):  
Taehyun Kwon ◽  
Seunghyun Kim
Keyword(s):  
Network Analysis ◽  
Text Mining ◽  
Semantic Network ◽  
Semantic Network Analysis ◽  
Ability Test

scholarly journals Analysis of Issues Based on Disaster Type Using Text Mining and a Semantic Network

2021 ◽  
Vol 21 (3) ◽  
pp. 49-60
Author(s):  
Tae Jin Kim ◽  
Mi Ryeong Eum ◽  
Sang Hyun Park
Keyword(s):  
Network Analysis ◽  
Text Mining ◽  
Forest Fire ◽  
Semantic Network ◽  
Semantic Network Analysis ◽  
The Public ◽  
State Administration ◽  
The Government ◽  
Media Channels ◽  
Negative Sentiment

Recently, the government has been increasingly communicating with the public in response to their opinions on state administration and policy projects. To examine the practicality of the public’s suggestions, this study investigated issues by disaster type, based on information from major media channels and comment data from the news. An analysis of the frequency of appearance, text mining (TF-IDF, LDA, and sentiment analysis), and the semantic network was performed by extracting the comment data of articles on the themes of “disaster” and “evacuation,” published from January 2010 to May 2020. The analysis results showed that news articles centered on these themes increased rapidly from 2017. The main disasters in Korea were those of “fire,” “typhoon,” “forest fire,” “radioactivity,” and “earthquake,” in order of enormity. Of the total negative words pertaining to “radioactivity” disasters, 43% were negative-sentiment words, and the semantic network analysis revealed that the terms “typhoon,” “forest fire,” and “earthquake” were connected to “radioactivity” disasters. This study is meaningful as it identifies issues by type of disaster and factors of anxiety expressed by the public using news and comment data, without conducting surveys and interviews.

scholarly journals Approaching fashion design trend applications using text mining and semantic network analysis

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Hyosun An ◽  
Minjung Park
Keyword(s):  
Network Analysis ◽  
Text Mining ◽  
Trend Analysis ◽  
Semantic Network ◽  
Design Feature ◽  
Design Guidelines ◽  
Fashion Design ◽  
Semantic Network Analysis ◽  
Design Features ◽  
Blog Posts

Abstract This study aims to identify fashion trends with design features and provide a consumer-driven fashion design application in digital dynamics, by using text mining and semantic network analysis. We examined the current role and approach of fashion forecasting and developed a trend analysis process using consumer text data. This study focuses on analyzing blog posts regarding fashion collections. Specifically, we chose the jacket as our fashion item to produce practical results for our trend report, as it is an item used in multiple seasons and can be representative of fashion as a whole. We collected 29,436 blog posts from the past decade that included the keywords “jacket” and “fashion collection.” After the data collection, we established a list of fashion trend words for each design feature by classifying styles (e.g., retro), colors (e.g., black), fabrics (e.g., leather), and patterns (e.g., checkered). A time-series cluster analysis was used to categorize fashion trends into four clusters—increasing, decreasing, evergreen, and seasonal trends—and a semantic network analysis visualized the latest season’s dominant trends along with their corresponding design features. We concluded that these results are useful as they can reduce the time-consuming process of fashion trend analysis and offer consumer-driven fashion design guidelines.

Sign in / Sign up

Export Citation Format

Share Document