Topic-Network based Topic Shift Detection on Twitter (트위터 데이터를 이용한 네트워크 기반 토픽 변화 추적 연구)

Seol A jin (진설아); Heo Go Eun (허고은); Jeong Yoo Kyung (정유경); Min Song (송민)

doi:10.3743/KOSIM.2013.30.1.285

Topic-Network based Topic Shift Detection on Twitter

Journal of the Korean Society for Information Management
Abbr : JKOSIM
2013, 30(1), pp.285~302
DOI : 10.3743/KOSIM.2013.30.1.285
Publisher : 한국정보관리학회
Research Area : Interdisciplinary Studies > Library and Information Science
Received : February 15, 2013
Accepted : March 25, 2013
Published : March 30, 2013

Seol A jin ¹, Heo Go Eun ¹, Jeong Yoo Kyung ¹, Min Song ¹

¹연세대학교

Accredited

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

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* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

Topic-Network based Topic Shift Detection on Twitter

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

Topic-Network based Topic Shift Detection on Twitter

ABSTRACT

KEYWORDS

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REFERENCES (25) * References for papers published after 2023 are currently being built.

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* References for papers published after 2023 are currently being built.