A network community user identification method and device

Abstract

The invention discloses a network community user identification method and device, and relates to the technical field of machine learning and user identification. The method comprises the following steps: extracting a first N-gram feature of the pre-collected network community text data and generating a first word vector; Taking the cross entropy as a cost function, and training the first word vector by adopting a deep neural network to obtain a text content analysis model; Analyzing the text data of the to-be-identified user by using a text content analysis model to obtain a content attribute; Analyzing the behavior data of the to-be-identified user by using a preset behavior analysis model to obtain behavior attributes; And identifying the to-be-identified user according to the content attribute and the behavior attribute. In the invention, N-gram features are introduced, the target user identification is carried out based on the the user content and user behavior two-dimensional data, and identification performance and accuracy are greatly improved compared with existing single-dimensional data user identification.

Description

technical field [0001] The present application relates to the technical field of machine learning and user identification, and in particular to a method and device for user identification in a network community. Background technique [0002] With the continuous development of social networks, people are more willing to express their opinions and publish comments on hot events through public social platforms such as online communities and Weibo. The influence and dissemination ability of this kind of public platform are increasing day by day, which makes the influence of individuals also increase. Therefore, in many cases, it is more and more popular to accurately identify users based on users' publishing data and publishing behaviors in social networks. s concern. [0003] Currently, target user identification methods mainly use traditional statistical models or data mining algorithms based on machine learning, among which data mining algorithms based on machine learning ar...

AI Technical Summary

Problems solved by technology

However, the above methods are all statistics and mining of single-dimensional feature data, and the recognition performance and accuracy rate need to be improved; and in the process of machine learning, as the target becomes more and more complex, some important features are difficult to quantify and require a lot of effort. High cost to select appropriate features

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