Hot Topic Detection Based on a Refined TF-IDF Algorithm
Zhiliang Zhu, Jie Liang, Deyang Li, Hai Bo Yu, Guoqi Liu · IEEE Access · 2019
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.