download0 view136
twitter facebook

공공누리This item is licensed Korea Open Government License

Title
Deep sentiment analysis: Mining the causality between personality-value-attitude for analyzing business ads in social media
Author(s)
장행진
Publication Year
2013-12-15
Abstract
IT vendors routinely use social media such as YouTube not only to disseminate their IT product information, but also to acquire customer input efficiently as part of their market research strategies. Customer responses that appear in social media, however, are typically unstructured, thus, a fairly large data set is needed for meaningful analysis. Although identifying customers’ value structures and attitudes may be useful for developing targeted or niche markets, the unstructured and volume heavy nature of customer data prohibits efficient and economical extraction of such information. Automatic extraction of customer information would be valuable in determining value structure and strength. This paper proposes an intelligent method of estimating causality between user profiles, value structures, and attitudes based on the replies and published content managed by open social network systems such as YouTube. To show the feasibility of the idea proposed in this paper, information richness and agility are used as underlying concepts to create performance measures based on media information richness theory. The resulting deep sentiment analysis proves to be superior to legacy sentiment analysis tools for estimation of causality among the focal parameters.
Keyword
Sentimentanalysis; Dataanalytics; Valuestructure; Eulergraph; Socialmedia
Journal Title
Expert systems with applications
ISSN
0957-4174
Files in This Item:
There are no files associated with this item.
Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
URI
https://repository.kisti.re.kr/handle/10580/14218
Export
RIS (EndNote)
XLS (Excel)
XML

Browse