Exploring impacts of media characteristics on message content using text mining
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Baek, S.I. | - |
dc.contributor.author | Park, S. | - |
dc.contributor.author | Kim, J. | - |
dc.date.accessioned | 2021-08-02T10:27:20Z | - |
dc.date.available | 2021-08-02T10:27:20Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 2005-4238 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/11534 | - |
dc.description.abstract | Background/Objectives: The purpose of this study is to empirically investigate the semantic similarity of documents posted on different forms of media about specific social issues. Methods/Statistical analysis: Online text data were collected from personal blogs and Internet news published on a major Korean portal site, NAVER. To collect text data from online media, the study used R programming language for web crawling. We examined what effects medium characteristics had on the content of conveyed messages by using a keyword extraction method based on TF-IDF, which is a text mining method, and the cosine similarity measurement method. Findings: The results of this study demonstrate that there were differences in the major keywords extracted from messages conveyed by the three forms of media, but the similarity between keyword-to-keyword matrices extracted from the media was confirmed by a Mantel test, and there were statistically significant degrees of similarity among these matrices. We were therefore able to discover similarities of message content conveyed by each medium. Improvements/Applications: For this study, we used only blog and news data published on a single Korean portal site. The text data better be collected from variety of channels in future studies. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Science and Engineering Research Support Society | - |
dc.title | Exploring impacts of media characteristics on message content using text mining | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Baek, S.I. | - |
dc.identifier.scopusid | 2-s2.0-85082321068 | - |
dc.identifier.bibliographicCitation | International Journal of Advanced Science and Technology, v.29, no.4 Special Issue, pp.291 - 303 | - |
dc.relation.isPartOf | International Journal of Advanced Science and Technology | - |
dc.citation.title | International Journal of Advanced Science and Technology | - |
dc.citation.volume | 29 | - |
dc.citation.number | 4 Special Issue | - |
dc.citation.startPage | 291 | - |
dc.citation.endPage | 303 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | media characteristic | - |
dc.subject.keywordAuthor | online media | - |
dc.subject.keywordAuthor | offline media | - |
dc.subject.keywordAuthor | text mining | - |
dc.subject.keywordAuthor | TF-IDF | - |
dc.subject.keywordAuthor | similarity analysis | - |
dc.identifier.url | http://sersc.org/journals/index.php/IJAST/article/view/6307 | - |
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