Abstract: Microblog user interest discovery is of great significance to the personalized recommendation of social networks and the correct information dissemination guidance.We propose a method of microblog user interest recognition based on multi-granular text feature representation.First,this paper constructs a text vector for microblog users from three aspects,including topic layer,word order layer,and vocabulary layer.LDA is used to extract the content's topic features,and LSTM learns the semantic features of the sentences.The open-source word vector of Tencent AI Lab is introduced to obtain the semantic features of words;then,the multi-granular text feature representative matrix obtained by the above three feature vectors is input into CNN for text classification training.Finally,the interest recognition of Weibo users is completed through the multi-terminal output layer.Experimental results show that the precision rate,recall rate,and F1 value of the multi-granularity feature representation model are improved by 8%,12%,and 13%,respectively.Based on the careful consideration of text coarse and fine semantic granularity and word granularity,combined with the neural network classification algorithm,the multi-granularity feature representation model's evaluation index is better than the single-granularity feature representation model.

Key words: Interest recognition, Social network, Text classification, Text feature, Weibo user