User keyword extraction apparatus and method, and computer readable storage medium

Abstract

The present invention discloses a social network-based user keyword extraction method. The method comprises: obtaining a blog post published by a target user within a preset time interval and performing word segmentation processing to obtain a word list of each blog post; inputting the word list corresponding to each blog post into a Word2Vec model for training to obtain a word vector model; extracting keywords corresponding to the blog post based on a keyword extraction algorithm to form candidate keyword sets for the target user, based on the word vector model, calculating a word vector of each keyword in each candidate keyword set, and constructing a semantic similarity graph; and running the Pagerank algorithm on the semantic similarity graph, and scoring the keywords to obtain interest keywords of the user. The present invention further proposes a social network-based user keyword extraction apparatus and a computer readable storage medium. The technical scheme of the present invention solves the technical problem in the prior art that it is difficult to extract the keywords that can effectively represent the interest of the user, according to the blog post of the user.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a social network-based user keyword extraction device, method and computer-readable storage medium. Background technique [0002] At present, with the popularization of social networks, there are more and more various applications based on social networks such as Weibo. For example, personalized recommendations are made for users' blog posts. The current recommendation method is mainly based on friend recommendations based on the same tag information. , friend recommendation based on common concern, microblog topic recommendation based on topic popularity, etc., but this recommendation method has large limitations, and it is difficult to make targeted recommendations according to the user's hobbies. Therefore, how to extract keywords that can effectively represent the interests of users from the massive blog post data, and analyze and determine the real interests of use...

AI Technical Summary

Problems solved by technology

[0002] At present, with the popularization of social networks, there are more and more various applications based on social networks such as Weibo. For example, personalized recommendations are made for users' blog posts. The current recommendation method is mainly based on friend recommendations based on the same tag information. , friend recommendation based on common concern, Weibo topic recommendation based on topic popularity, etc., but this recommendation method has large limitations, and it is difficult to make targeted recommendations according to the user's hobbies

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