Name
Abstract | ||
|---|---|---|
Detecting hot topics from massive academic data is a very challenging task. Because various types of academic information are overgrowing, e.g., papers, news, and blogs, which has gone far beyond the limits that researchers can accept. Therefore, how to efficiently and accurately detect hot topics from big academic data is the main problem that researchers are facing. In view of this, we design a general framework for Academic Hot Topic Detection (AHTD). Specifically, in this framework, a DeepWalk-based keyword extraction algorithm for a single paper (S-DWKE) is proposed to detect popular topics in diverse academic fields dynamically. Moreover, we propose a keyword extraction algorithm to extract keywords from multiple articles (M-GCKE), which enables us to detect new topics in emerging academic areas. Then, hot topics can be generated from keywords extracted by the S-DWKE and M-GCKE. A large number of experiments demonstrates the proposed framework effectively improves the performance of hot topic detection in the academic field and performs better than the comparison algorithms. We have applied the above work to the Academic Headline application to provide the hot topics for researchers. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2960285 | IEEE ACCESS |
Keywords | DocType | Volume |
Academic big data, hot spot discovery, keyword extraction, knowledge representation | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 2 | 0.38 |
References | Authors | |
21 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Beibei Wang | 1 | 2 | 0.38 |
| Bo Yang | 2 | 822 | 64.08 |
| Shuangshuang Shan | 3 | 2 | 0.38 |
| Hechang Chen | 4 | 18 | 9.53 |