关键词: 社会网络, 网络关系, 文本内容, 深度学习, 聚类算法, 用户画像

Abstract: [Purpose/significance] In the study of user profile based on social network, aiming at the problems that traditional user modeling is difficult to deal with the complex network relationship, group construction is mostly based on content, and the group is low similarity or poor tightness, a construction method of group profile based on network structure and text content is proposed.[Method/process] Firstly, using the convolutional neural network method, the network structure and the text content are combined to represent the network user as a space vector. Secondly, based on the k-means algorithm, the modularity calculation method is combined to cluster the space vector. In the crawled Chinese and English datasets, a comparative study is conducted. Finally, 1000 important users are selected from the Chinese dataset for instance analysis.[Result/conclusion] The experimental results show that the density value of this method is increased by 0.105 compared with the content-based method, and the entropy value decreases by 0.955 on average compared with the structure-based (including structure-based and content-based) method. The instance analysis further illustrates the feasibility of the proposed method.

Key words: social network, network relationship, text content, deep learning, clustering algorithm, user profile