Name
Abstract | ||
|---|---|---|
This paper describes a method to extract local event information from the micro-blog service Twitter. Twitter holds innumerable user-posted short messages called tweets that cover various topics including local events. Our proposal is composed of three steps: 1) extract tweets related to local events from local tweets by the Support Vector Machine (SVM) approach, 2) identify and extract the venues, names and times of local events mentioned in the tweets by applying Conditional Random Fields (CRF), 3) use the venues and similarity of names to aggregate duplicate local event information. We implement the proposed method and confirm that it extracts local event information with higher precision than the conventional methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICMU.2015.7061020 | ICMU |
Field | DocType | Citations |
Conditional random field,Data mining,Information retrieval,Trip planning,Computer science,Support vector machine | Conference | 2 |
PageRank | References | Authors |
0.40 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wataru Yamada | 1 | 29 | 15.22 |
| Daisuke Torii | 2 | 2 | 0.40 |
| Haruka Kikuchi | 3 | 32 | 4.06 |
| Hiroshi Inamura | 4 | 253 | 25.67 |
| Keiichi Ochiai | 5 | 7 | 3.21 |
| Ken Ohta | 6 | 28 | 5.21 |