Method for discovering network public opinion theme and concerned user groups thereof

Abstract

The invention discloses a method for discovering a network public opinion theme and concerned user groups thereof. The method comprises the steps: 1, constructing a data set in a public opinion document; 2, modeling a public opinion text topic and concerning the user groups of the public opinion text topic; 3, designing a parametric Bayesian model; and 4, carrying out parameter inference by a collapse Gibbs sampling algorithm. When large-scale online social media content and social user comment behaviors are dealt with, on one hand, public opinion topics in the network can be quickly, effectively and accurately found in combination with topic analysis, public opinion detection is facilitated, and decision support is provided for public opinion guidance and control; and on the other hand, the user groups concerning each public opinion topic can be identified, and social media users can be quickly and accurately classified according to similarities and differences of concerned topics.

Description

technical field [0001] The present invention relates to the technical field of discovering public opinion topics and user groups concerned by the public opinion topics, and specifically relates to a method for discovering network public opinion topics and user groups concerned by them. Background technique [0002] With the rapid development of network information technology, network social platforms such as network news, Sina Weibo, and Baidu Tieba are booming, and have strong communication power, speed and interactivity. In addition, social media users freely express their views and opinions on a certain event, and this information can be spread quickly point-to-point through other users' attention, forwarding, and comment relationships, making public opinion hotspots extremely easy to erupt. Under these public opinion events, there are widely discussed topics and information on the user groups who participate in comments most frequently under each topic. [0003] Therefo...

AI Technical Summary

Problems solved by technology

[0003] Therefore, it is significant and challenging to identify hot public opinion topics from large-scale, unstructured public opinion text content, and requires an extremely efficient calculation method, and the traditional Gibbs sampling algorithm will also cause the calculation process to be extremely difficult. Slow, making it impossible for enterprises or governments to capture hot public opinion topics that users care about in a real-time, dynamic, and efficient manner

In addition, the existing methods lack the joint modeling of public opinion text content data and comment user data to identify the most concerned user groups under each public opinion topic, and cannot identify users with high public opinion guidance under each topic, which will increase Difficulty in Supervising Public Opinion by Major Relevant Departments

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