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- P-ISSN1013-0799
- E-ISSN2586-2073
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Topic-Network based Topic Shift Detection on Twitter
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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, 트위터, 토픽 추적, 동시출현단어분석, 네트워크 기반 분석, 시계열 그래프
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