中圖分類號(hào)：TP391.6 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.223376 中文引用格式： 陳木生，高斐,，吳俊華. 基于單頁(yè)語(yǔ)義特征的垃圾網(wǎng)頁(yè)檢測(cè)[J]. 電子技術(shù)應(yīng)用,，2023，49(6)：24-29. 英文引用格式： Chen Musheng,，Gao Fei,，Wu Junhua. Web spam detection based on semantic features from current page[J]. Application of Electronic Technique，2023,，49(6)：24-29.
Web spam detection based on semantic features from current page
Chen Musheng1,，2，Gao Fei1,，Wu Junhua1
(1.School of Software Engineering,， Jiangxi University of Science and Technology， Nanchang 330013,， China,； 2.Nanchang Key Laboratory of Virtual Digital Engineering and Cultural Communication， Nanchang 330013,， China)
Abstract： In order to solve the problem of high difficulty and large amount of computation in feature extraction for web spam detection, a method for extracting semantic features only based on the HTML script of the current page is proposed. Firstly, the domain name is segmented by a memorization search algorithm combining depth-first search and dynamic programming. Secondly, The latent Dirichlet distribution is used to extract subject words of the web page. Lastly, three single-page semantic similarity features are calculated based on Word2Vec and word mover distance. Combining the single-page semantic similarity features with single-page statistical features, classification algorithms such as random forest are used to build classification models for web spam detection. The experimental results show that the AUC value of single-page content extraction based on semantic and statistical features for classification reaches 88.0%, which is about 4% higher than that of the control method.
Key words : web spam detection,；feature extraction；memory search,；latent Dirichlet distribution,；Word2Vec,；word mover distance；random forest
