Name
Abstract | ||
|---|---|---|
In this paper, we propose a refined term frequency inversed document frequency (TF-IDF) algorithm called TA TF-IDF to find hot terms, based on time distribution information and user attention. We also put forward a method to generate new terms and combined terms, which are split by the Chinese word segmentation algorithm. Then, we extract hot news according to the hot terms, grouping them into K-means clusters so as to realize the detection of hot topics in news. The experimental results indicated that our method based on the refined TF-IDF algorithm can find hot topics effectively. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2893980 | IEEE ACCESS |
Keywords | Field | DocType |
Feature extraction,hot topic detection,hot terms,time sensitive,user attention | Time distribution,tf–idf,Computer science,Algorithm,Text segmentation | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi-Liang Zhu | 1 | 694 | 64.61 |
| jie liang | 2 | 26 | 10.90 |
| Deyang Li | 3 | 0 | 0.68 |
| Hai Yu | 4 | 283 | 17.63 |
| Guo-qi Liu | 5 | 18 | 9.39 |