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Web Text Feature Extraction with Particle Swarm Optimization


Song Liangtu, Zhang Xiaoming


Vol. 7  No. 6  pp. 132-136


The Internet continues to grow at a phenomenal rate and the amount of information on the web is overwhelming. It provides us a great deal of information resource. Due to its wide distribution, its openness and high dynamics, the resources on the web are greatly scattered and they have no unified management and structure. This greatly reduces the efficiency in using web information.Web text feature extraction is considered as the main problem in text mining. We use Vector Space Model (VSM) as the description of web text and present a novel feature extraction algorithm which is based on the improved particle swarm optimization with reverse thinking particles (PSORTP). This algorithm will greatly improve the efficiency of web texts processing.


Web mining, VSM, Particle swarm optimization, Text feature extraction