Name
Title | ||
|---|---|---|
Does deep learning help topic extraction? A kernel k-means clustering method with word embedding. |
Abstract | ||
|---|---|---|
•A novel topic extraction method incorporated with a kernel k-means model and a word embedding model.•The incorporation of word embedding techniques in data pre-processing for topic extraction.•A polynomial kernel function-based k-means model for effectively conducting bibliometric data-oriented topic extraction.•Empirical insights into both overlapping and diverse research interests among three top-tier bibliometric journals. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.joi.2018.09.004 | Journal of Informetrics |
Keywords | Field | DocType |
Bibliometrics,Topic analysis,Cluster analysis,Text mining | Kernel (linear algebra),Data mining,k-means clustering,Information retrieval,Computer science,Bibliometrics,Artificial intelligence,Topic model,Deep learning,Word embedding,Cluster analysis,Empirical research | Journal |
Volume | Issue | ISSN |
12 | 4 | 1751-1577 |
Citations | PageRank | References |
7 | 0.46 | 27 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Zhang | 1 | 95 | 10.69 |
| Jie Lu | 2 | 578 | 38.78 |
| Feng Liu | 3 | 80 | 8.59 |
| Qian Liu | 4 | 46 | 20.95 |
| Alan L. Porter | 5 | 398 | 32.61 |
| Hongshu Chen | 6 | 8 | 2.50 |
| Guangquan Zhang | 7 | 1973 | 145.64 |