【摘要】 社交媒体账号分类方法从账号的属性信息出发，通过构建账号特征从而对账号进行分类，对从海量社交媒体数据中挖掘有价值的信息具有十分重要的作用。现有社交媒体账号分类一般基于用户发布的信息提取特征，存在账号信息描述不完整、分类有效性低的问题。针对上述问题，提出了一种基于多模态特征融合的社交媒体账号分类方法。该方法综合考虑账号自身属性、文本以及账号之间的社交关系等信息，使用张量分析的方法对账号所表现的多模态特征进行融合。相比现有方法，所提方法可以更好地利用账号的各种信息，获得更好的分类效果。通过实验，所提方法准确率达到了93.74%。更多还原

【Abstract】 Social media account classification methods start from the attribute information of the account, construct the account features and classify the account, which is very important for mining valuable information from the massive social media data. Existing social media account classification is generally based on extracting features from the information posted by users, which has the problems of incomplete description of account information and low effectiveness of classification. To solve the problems above, the paper proposes a social media account classification method based on multimodal feature fusion. The method uses tensor analysis to fuse the multimodal features expressed by the account after comprehensively considering the information of the account′s own attributes, the text, and the social relationships between the accounts. Compared with the existing methods, the method proposed in this paper can better utilize the various information of accounts and obtain better classification results. Through experiments, the method in this paper achieves an accuracy rate of 93.74%.更多还原