Research of News Text with Word Frequency Statistics and User Information

被引：0
|
作者：
Liu, Shan [1 ]
Huang, Kun [1 ]
Chai, Jianping [1 ]
机构：
[1] Commun Univ China, Sch Informat Engn, Beijing, Peoples R China
来源：
PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC) | 2017年
关键词：
word frequency; user information; tag model; TF-IDF algorithm;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper focuses on the news collection of a single topic, analyzes the large number of keywords and phrases in the news, uses the traditional TF-IDF algorithm based on the word frequency, adds the weight to the tags, extracts the keywords which have certain use value in the news, and uses the tag selection formula to tag the news. In addition, collect user reviews on the Internet news portal to create user comments information tags library for the news tag group to make the news tags more fit user needs. In this paper, the value of the tag is measured by the recall rate and the F1 score, and the tag based on the news content is analyzed according to the intermediate variable in the model operation. It also summarizes the general content of the topic by analyzing the tags distribution of the news collection.
引用
收藏
页码：2633 / 2637
页数：5
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