Exploring impacts of media characteristics on message content using text mining
- Authors
- Baek, S.I.; Park, S.; Kim, J.
- Issue Date
- 2020
- Publisher
- Science and Engineering Research Support Society
- Keywords
- media characteristic; online media; offline media; text mining; TF-IDF; similarity analysis
- Citation
- International Journal of Advanced Science and Technology, v.29, no.4 Special Issue, pp.291 - 303
- Indexed
- SCOPUS
- Journal Title
- International Journal of Advanced Science and Technology
- Volume
- 29
- Number
- 4 Special Issue
- Start Page
- 291
- End Page
- 303
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/11534
- ISSN
- 2005-4238
- 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.
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