| 1 | Korea | 3.94% | |
| 2 | S. | 3.41% | |
| 3 | China | 2.62% | |
| 4 | Seoul | 2.62% | |
| 5 | US | 1.57% | |
| 6 | National | 1.05% | |
| 7 | Iran | 1.05% | |
| 8 | Korean | 1.05% | |
| 9 | News] | 1.05% | |
| 10 | Yoon | 0.79% | |
| 11 | Hong | 0.79% | |
| 12 | India | 0.79% | |
| 13 | Air | 0.79% | |
| 14 | North | 0.79% | |
| 15 | Paris | 0.79% | |
| 16 | Park | 0.79% | |
| 17 | SK | 0.79% | |
| 18 | Venice | 0.52% | |
| 19 | Cheongju | 0.52% | |
| 20 | N. | 0.52% | |
| 21 | Tibetan | 0.52% | |
| 22 | Life | 0.52% | |
| 23 | Modi | 0.52% | |
| 24 | [New | 0.52% | |
| 25 | Israel | 0.52% | |
| 26 | South | 0.52% | |
| 27 | [Graphic | 0.52% | |
| 28 | Briefing | 0.52% | |
| 29 | [Editorial] | 0.52% | |
| 30 | [Today’s | 0.52% | |
| 31 | Biennale | 0.52% | |
| 32 | [Photo | 0.52% | |
| 33 | Kong | 0.52% | |
| 34 | Korean] | 0.52% | |
| 35 | Myanmar | 0.52% | |
| 36 | TSMC | 0.52% | |
| 37 | K-pop] | 0.52% | |
| 38 | Isfahan | 0.52% | |
| 39 | Fouser] | 0.26% | |
| 40 | ‘Provisional | 0.26% | |
| 41 | Vision | 0.26% | |
| 42 | Jay | 0.26% | |
| 43 | Baguette | 0.26% | |
| 44 | Australia | 0.26% | |
| 45 | June | 0.26% | |
| 46 | Ethnic | 0.26% | |
| 47 | Foreign | 0.26% | |
| 48 | [What | 0.26% | |
| 49 | Museum’s | 0.26% | |
| 50 | Japan | 0.26% | |
|
는 분류, 데이터 기준으로 의 기사에서 의 고유명사 데이터를 통해 생성되었습니다.
|
|||