A Study on the Evaluation of Fashion Design Based on Big Data Text Analysis -Focus on Semantic Network Analysis of Design Elements and Emotional Terms-

An, Hyosun; Park, Min Jung

doi:10.5850/jksct.2018.42.3.428

Cited by 13 publications

(4 citation statements)

References 4 publications

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“…Semantic network analysis is a method of recognizing a word constituting a text as a point (node), connecting the relation between nodes by a line, and grasping the meaning using a visualized connection network [74]. Network analysis is widely used as a method of research in fashion and marketing, as it allows researchers to analyze individuals' opinions and ideas posted on unstructured online boards [66,75,76].…”

Section: Analyticla Methodsmentioning

confidence: 99%

“…NodeXL were used to identify the structure of the linkage between words and to analyze the centrality and TF-IDF, thus quantifying the degree of the relationships [77,78]. For semantic network analysis using text mining, to simplify the network, researchers suggested to extract up to 30 to 200 top keywords that were often mentioned to simplify the data based on the frequency of appearance [76,79]. Therefore, for the keyword extraction stage, this study mined the top 70 nouns, adjective, and verb keywords with a frequency of 60 or more, which were selected using frequency analysis.…”

Section: Analyticla Methodsmentioning

confidence: 99%

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Semantic Network Analysis to Explore the Concept of Sustainability in the Apparel and Textile Industry

Youn

Jung

2021

Sustainability

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Consumers are becoming increasingly aware and sensitive to the negative environmental impact caused by the fashion industry and by consumers’ high consumption of fashion. This study analyzes people’s unfiltered comments and behaviors on social media sites related to the sustainability of fashion products. Recently, the number of social media data, called big data, has exploded, transcending the level that can be analyzed with existing tools. This study aims to identify consumers’ perceptions of sustainable fashion using the search words “sustainable fashion” to examine public opinion trends found in SNS big data. Text mining was employed to extract meaningful words from the SNS texts using semantic network analysis to analyze the connectivity and propagation trends. The text data were collected from Facebook using the Google search engine to detect tendencies in the occurrence of keywords related to sustainable fashion in SNS over the past five years (2016~2020). The results revealed that the keywords “eco-friendly”, “ethical”, and “recycle” had the highest frequency and centrality. As a result of grouping the keywords based on their correlations, sustainable fashion texts from the SNS data could be classified into four groups: “supply chain of sustainable fashion”, “circular fashion”, “fashion business concepts for sustainability”, and “academic importance of sustainable fashion”. This study strengthens the extent of research by using SNS big data and provides guidelines for product development and communication strategies for a sustainable fashion industry based on customers’ meaningful opinions.

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Section: Analyticla Methodsmentioning

confidence: 99%

Section: Analyticla Methodsmentioning

confidence: 99%

Semantic Network Analysis to Explore the Concept of Sustainability in the Apparel and Textile Industry

Youn

Jung

2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…Rizun and Kucharska (2018) [43] examined customers' interactions with fashion brands using a set of text mining algorithms. Likewise, An and Park (2018) [44] applied text mining and semantic network analysis to explain the correlations between fashion design features. Following these studies, we apply text mining and semantic network analysis to examine perceptions of AI fashion curation services and intentions to use those services among social media users.…”

Section: Big Data Analysismentioning

confidence: 99%

Exploring the Key Factors that Lead to Intentions to Use AI Fashion Curation Services through Big Data Analysis

2022

KSII TIIS

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An increasing number of companies in the fashion industry are using AI curation services. The purpose of this study is to investigate perceptions of and intentions to use AI fashion curation services among customers by using text mining. To accomplish this goal, we collected a total of 34,190 online posts from two Korean portals, Naver and Daum. We conducted frequency analysis to identify the most frequently mentioned keywords using Textom. The analysis extracted "various," "good," "many," "right," and "new" at the highest frequency, indicating that consumers had positive perceptions of AI fashion curation services. In addition, we conducted a semantic network analysis with the top-50 most frequently used keywords, classifying customers' perceptions of AI fashion curation services into three groups: shopping, platform, and business profit. We also identified the factors that boost continuous use intentions: usability, usefulness, reliability, enjoyment, and personalization. We conclude this paper by discussing the theoretical and practical implications of these findings.

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“…Mass entrepreneurship and innovation are the inevitable requirements of the national development strategy to adapt to the new normal economy. Controlling and selecting talents with innovation and entrepreneurship potential, continuously expanding the talent pool of innovation and entrepreneurship, and providing accurate support and effective incentive measures are the necessary premise to improve the level of enterprise innovation and service and improve efficiency [1][2]. Innovation and Entrepreneurship Talent work is an important support to promote economic and social development and technological innovation [3].…”

Section: Introductionmentioning

confidence: 99%

Model Platform Design of Innovation and Entrepreneurship Dynamic Function Evaluation Theory Based on Big Data

2021

IJBDIT

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Innovation and entrepreneurship" is an important innovation strategy to change the mode of economic development and realize the new normal of economic development. However, there are few theoretical exploration and practical research on the dynamic perspective of "innovation and entrepreneurship", especially on the evaluation of the effect of "innovation and entrepreneurship" driven development. In view of this, this paper focuses on the analysis of the dynamic role of "innovation and entrepreneurship", explores the essence of "innovation and entrepreneurship" driving economic development, constructs the evaluation theoretical model and evaluation index system of "innovation and entrepreneurship" driving economic development, uses the supply chain management model, designs the evaluation theoretical model platform of innovation and entrepreneurship dynamic role on the basis of big data, and comprehensively applies qualitative and quantitative methods An empirical study is carried out. The research results show that the average response time of the platform is 0.8s under the pressure of the specified number of concurrent users, the throughput is 98 under the pressure and duration of the specified number of concurrent users, the click through rate is 94, the number of transactions per second is 96, and the request processing success rate is 97% under the pressure of the specified number of concurrent users.

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A Study on the Evaluation of Fashion Design Based on Big Data Text Analysis -Focus on Semantic Network Analysis of Design Elements and Emotional Terms-

Cited by 13 publications

References 4 publications

Semantic Network Analysis to Explore the Concept of Sustainability in the Apparel and Textile Industry

Semantic Network Analysis to Explore the Concept of Sustainability in the Apparel and Textile Industry

Exploring the Key Factors that Lead to Intentions to Use AI Fashion Curation Services through Big Data Analysis

Model Platform Design of Innovation and Entrepreneurship Dynamic Function Evaluation Theory Based on Big Data

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