| Project/Area Number |
26330345
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Web informatics, Service informatics
|
|---|
| Research Institution | Wakayama University |
|---|
Principal Investigator |
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
斉藤 和巳 静岡県立大学, 経営情報学部, 教授 (80379544)
|
|---|
| Project Period (FY) |
2014-04-01 – 2017-03-31
|
| Project Status |
Completed (Fiscal Year 2016)
|
| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
|
|---|
| 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 / 議論話題 / 時空間バースト検出 / 情報拡散 / トピック抽出 / Spatial Network / 因果関係 |
|---|
| 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.
|
