一种结合文本情感分析的微博僵尸粉识别模型

社交网站中的僵尸粉群体严重威胁社交平台公信力且增加了社交风险。为准确识别僵尸粉,构建一个基于神经网络的僵尸粉识别模型(Zat-NN)。通过分析微博僵尸粉的社交行为得到高级僵尸粉的行为特征,利用累积分布函数研究僵尸粉与正常用户在行为特征上的差异,并结合卷积神经网络与长短时记忆网络加强微博文本情感分析能力,同时增加日均转发微博数、发博工具和微博情感特征3个用户新特征提高Zat-NN模型识别准确率及鲁棒性。在新浪微博用户数据集上的实验结果表明,Zat-NN模型能有效识别高级僵尸粉,提升社交网络用户体验。

A Zombie Fans Recognition Model for Microblog Combining Text Sentiment Analysis

The social risk caused by prevalence of zombie fans brings significant threat to the credibility of social platforms.To effectively recognize these zombie fans,this paper proposes a zombie fans recognition model,Zat-NN,based on neural network.First,the social behavior of zombie fans on microblog is analyzed to obtain behavior features of high-level zombie fans.Second,the cumulative distribution function is used to study the behavior feature differences between zombie fans and normal users.Then Convolutional Neural Network(CNN)and Long Short Term Memory(LSTM)network are combined to strengthen the sentiment analysis of microblog texts.At the same time,the number of daily forwarded microblogs,blogging tools and microblog emotion features are added as user features to improve the recognition accuracy and robustness of the Zat-NN model.Experimental results on the user dataset of Sina microblog show that the Zat-NN model can effectively recognize high-level zombie fans,improving user experience of social network.