Journal
IEEE ACCESS
Volume 7, Issue -, Pages 26996-27007Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2893980
Keywords
Feature extraction; hot topic detection; hot terms; time sensitive; user attention
Categories
Funding
- National Natural Science Foundation of China [61374178, 61402092, 61603082]
- Online Education Research Fund of MOE Research Center for Online Education, China [2016ZD306]
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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.
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