User-based Hierarchical Network of Sina Weibo Emotion Analysis

Abstract

Emotion analysis on Sina Weibo has a great impetus for government agencies to survey public opinion and enterprises to track market demand. Most of the existing emotion analysis work on Sina Weibo focuses on mining the information contained in a single Weibo, ignoring the problem of inaccurate information extraction caused by the lack of contextual information in Weibo texts. Inspired by humans judging user emotional states from Weibo texts, this article creates a Weibo text five-category emotion classification dataset based on active users and proposes a user-based hierarchical network for Weibo emotion analysis. First, use the multi-head attention mechanism and convolutional neural network set in the information extraction module to analyze a single Weibo text to fully extract the emotional information contained in the text; at the same time, through the moving window set in the relevant information capture module, obtain other Weibo texts posted by the same user within a period, and capture the effective correlation information between Weibo texts; then, the dual text representation obtained above is concatenated, and through the information interaction layer, the relevant information is retrieved again, and the text representation is updated; finally, the classifier output the five-category emotion labels corresponding to each Weibo text. We demonstrate the model’s effectiveness through experiments and analysis in the results.