基于AdaBoost的微博垃圾评论识别方法

Ling Huang, Xueming Li
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引用次数: 1

摘要

针对微博中存在大量的垃圾评论,提出了一种基于AdaBoost的垃圾评论识别新方法。该方法首先提取由8个特征值组成的特征向量来表示评论,然后通过AdaBoost算法对这些特征训练出优于随机预测的几个弱分类器,最后将这些加权弱分类器组合在一起,构建精度较高的强分类器。对新浪热门微博评论数据集的实验结果表明,所选择的8个特征对该方法是有效的,在微博垃圾评论的识别中具有较高的识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification method of spam comments in microblog based on AdaBoost
In view of the existence of a lot of spam comments in microblog,a new method based on AdaBoost was proposed to identify spam comments. This method firstly extracted feature vectors which consisted of eight feature values to represent the comments,then trained several weak classifiers which were better than random prediction on these features via AdaBoost algorithm,and finally combined these weighted weak classifiers to build a strong classifier with a high precision. The experimental results on comment data sets extracted from the popular Sina microblogs indicate that the selected eight features are effective for the method,and it has a high recognition rate in the identification of spam comments in microblog.
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