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http://dx.doi.org/10.9716/KITS.2015.14.2.159

Applying Text Mining to Identify Factors Which Affect Likes and Dislikes of Online News Comments  

Kim, Jeonghun (경희대학교 일반대학원 경영학과)
Song, Yeongeun (경희대학교 일반대학원 경영학과)
Jin, Yunseon (경희대학교 일반대학원 경영학과)
kwon, Ohbyung (경희대학교 경영대학)
Publication Information
Journal of Information Technology Services / v.14, no.2, 2015 , pp. 159-176 More about this Journal
Abstract
As a public medium and one of the big data sources that is accumulated informally and real time, online news comments or replies are considered a significant resource to understand mentalities of article readers. The comments are also being regarded as an important medium of WOM (Word of Mouse) about products, services or the enterprises. If the diffusing effect of the comments is referred to as the degrees of agreement and disagreement from an angle of WOM, figuring out which characteristics of the comments would influence the agreements or the disagreements to the comments in very early stage would be very worthwhile to establish a comment-based eWOM (electronic WOM) strategy. However, investigating the effects of the characteristics of the comments on eWOM effect has been rarely studied. According to this angle, this study aims to conduct an empirical analysis which understands the characteristics of comments that affect the numbers of agreement and disagreement, as eWOM performance, to particular news articles which address a specific product, service or enterprise per se. While extant literature has focused on the quantitative attributes of the comments which are collected by manually, this paper used text mining techniques to acquire the qualitative attributes of the comments in an automatic and cost effective manner.
Keywords
eWOM; Text Mining; Online News Comments; Online Marketing;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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