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http://dx.doi.org/10.3743/KOSIM.2013.30.1.285

Topic-Network based Topic Shift Detection on Twitter  

Jin, Seol A (연세대학교 문헌정보학과 대학원)
Heo, Go Eun (연세대학교 문헌정보학과 대학원)
Jeong, Yoo Kyung (연세대학교 문헌정보학과 대학원)
Song, Min (연세대학교 문헌정보학과)
Publication Information
Journal of the Korean Society for information Management / v.30, no.1, 2013 , pp. 285-302 More about this Journal
Abstract
This study identified topic shifts and patterns over time by analyzing an enormous amount of Twitter data whose characteristics are high accessibility and briefness. First, we extracted keywords for a certain product and used them for representing the topic network allows for intuitive understanding of keywords associated with topics by nodes and edges by co-word analysis. We conducted temporal analysis of term co-occurrence as well as topic modeling to examine the results of network analysis. In addition, the results of comparing topic shifts on Twitter with the corresponding retrieval results from newspapers confirm that Twitter makes immediate responses to news media and spreads the negative issues out quickly. Our findings may suggest that companies utilize the proposed technique to identify public's negative opinions as quickly as possible and to apply for the timely decision making and effective responses to their customers.
Keywords
LDA; latent Dirichlet allocation; twitter; topic detection; co-word analysis; network-based analysis; time-series graph;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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