Information Visualization Analysis of Public Opinion Data on Social Media

Abstract

Public opinion data on social media contains many useful information, which can be visually displayed through visualization. This study mainly focused on Weibo and analyzed the keyword extraction of text and the analysis of emotional tendency. Keywords were extracted using the term frequency-inverse document frequency (TF-IDF) method, and the emotional tendency of the text was calculated based on the HowNet emotion dictionary and BosonNLP emotion dictionary. Finally, relevant data were collected by taking “Jiang Ziya” as the keyword for visualization analysis. It was found that the discussion on Jiang Ziya gradually reduced in the research period, and the extracted keywords were relatively positive. The visualization results of word cloud showed that there were many positive comments on Jiang Ziya, but there were also negative comments. Finally, the calculation of emotional tendency showed that 69% of the texts showed a positive emotional tendency, and 31% of the texts were negative, indicating that netizens’ emotional tendency towards Jiang Ziya was mainly positive. The results of the study make some contributions to the visualization of public opinion data and can be further applied in practice.