2016 Fiscal Year Final Research Report
Temporal-Spatial Community Extraction in Social Media
| Project/Area Number |
26330345
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Web informatics, Service informatics
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| Research Institution | Wakayama University |
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Principal Investigator |
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Keywords | 時空間コミュニティ / コミュニティ抽出 / ネットワーク分析 / トピックモデル / Twitter |
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| Outline of Final Research Achievements |
We study methods to detect and analyze discussion topics created by user communication on social media..
In order to detect temporal-spatial bursts in user communication, we propose dual-graph SR (Spectral Relaxation) method to extract core potions of a dual graph, which is created from replies on Twitter with specified time interval threshold.
Furthermore, we propose a method to classify topics for bag-of-words, which is created with word 2-grams instead of words, by using a generative topic model such as latent Dirichlet allocation (LDA) and visualize the results as topic sequences, which means time series variation of topics, or a topic graph, which means word relationships in each topic.
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| Free Research Field |
Webマイニング
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