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Hot Topic Detection Based on a Refined TF-IDF Algorithm
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DOI：10.1109/ACCESS.2019.2893980.png)
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Abstract
En 中文
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.
Keywords:
Feature extraction
hot topic detection
hot terms
time sensitive
user attention
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