Design and implementation of Weibo sentiment analysis based on LDA and dependency parsing

Abstract

Information content security is a branch of cyberspace security. How to effectively manage and use Weibo comment information has become a research focus in the field of information content security. Three main tasks involved are emotion sentence identification and classification, emotion tendency classification, and emotion expression extraction. Combining with the latent Dirichlet allocation (LDA) model, a Gibbs sampling implementation for inference of our algorithm is presented, and can be used to categorize emotion tendency automatically with the computer. In accordance with the lower ratio of recall for emotion expression extraction in Weibo, use dependency parsing, divided into two categories with subject and object, summarized six kinds of dependency models from evaluating objects and emotion words, and proposed that a merge algorithm for evaluating objects can be accurately evaluated by participating in a public bakeoff and in the shared tasks among the best methods in the sub-task of emotion expression extraction, indicating the value of our method as not only innovative but practical.