Time Series Topic Extraction from Millions of Tweets after the East Japan Great Earthquake Considering Author's Role
| Project/Area Number |
15K00314
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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 |
Intelligent informatics
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| Research Institution | Chiba University of Commerce |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
白田 由香利 学習院大学, 経済学部, 教授 (30337901)
久保山 哲二 学習院大学, 付置研究所, 教授 (80302660)
チャクラボルティ バサビ 岩手県立大学, ソフトウェア情報学部, 教授 (90305293)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | データマイニング / 東日本大震災 / 二部グラフ / 話題抽出 / クラスタリング / 知識発見 / 著者の役割 / ソーシャルメディア解析 / ビックデータ解析 / ツイッター解析 / 特徴抽出 / 2部グラフ解析 / 時系列話題抽出 / ビッグデータ解析 |
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| Outline of Final Research Achievements |
This research has developed a time series topic extraction method from millions of Tweets that were posted after the East Japan Great Earthquake. Our method is using our original community detection technique in bipartite networks. Our method considers the relationship between the authors and the words as bipartite networks and explores the authors role by forming clusters from them as topics. We utilize the random walk algorithm to effectively apply to big data. As a result, by generating time series bipartite networks with authors and words (e.g. every 30 min) and employing our method, we found that our method could extract more precise topics compared with the conventional methods such as LDA.
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Report
(4 results)
Research Products
(39 results)
