NDLI: Detection and Extraction of Hot Topics on Chinese Microblogs

Content Provider	Springer Nature Link
Author	Yang, Liang Lin, Hongfei Lin, Yuan Liu, Shengbo
Copyright Year	2016
Abstract	Peoples’ perceptions of reality are conditioned on how others see the world. Unfortunately, with the vast amount of information made available through online media, such as microblog sites, it is impossible for people to absorb all information in a timely manner. Therefore, the detection of hot topics on a microblog platform is becoming increasingly important. The present paper proposes a new hot-topic detection and extraction approach based on language and topic models, which analyzes the differences in emotion distribution language models between adjacent time intervals to detect hot topics. According to the contents and repost degree of microblogs, we estimate the importance of each microblog and generate topic models. Experiments conducted on the Sina Microblog show that the proposed approach can detect and extract hot topics effectively and can thus assist the Sina Microblog platform in managing and monitoring hot topics.
Starting Page	577
Ending Page	586
Page Count	10
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	8
Issue Number	4
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2016-02-17
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Topic detection Emotion distribution Chinese microblog Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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