Discovering Advertisement Links by Using URL Text
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DOI: 10.23977/iccsc.2017.1020
Author(s)
Jing-Shan Xu, Peng Chang and Yong-Zheng Zhang
Corresponding Author
Peng Chang
ABSTRACT
Capture of videos from websites is a basic work for searching video and analyzing
video content. Discovery of advertising video URLs effectively and accurately is very
important for video capture. It helps to improve the precision of video capture, optimize
network utilization and reduce storage space. Currently, video content-based methods have a
good ability to discover advertisement but in a limit speed, which do not meet the
requirements of real projects. Since increasing achievements of URL based technologies
have been made on classification subject of web pages, we utilize this technology to discover
advertising video URLs. The method first produces a collection of URL segments. Next by
applying N-gram feature selection, we get totally 2500 features. Afterwards, via combining
the statistical information and selected word vector, the final features are generated. We use
Naive Bayes, C4.5 Tree and SVM to train models. Ultimately, the experiment shows SVM is
the most suitable model for discovery of advertising URLs discovery with 94% precision.
KEYWORDS
Advertisement links, Text mining, SVM, Video capture.