The Big Data Analysis and Visualization of Mass Messages under “Smart Government Affairs” Based on Text Mining

Abstract

Based on the records of people’s political inquiries, comments from public sources on the Internet and the data of the relevant departments’ responses to some people’s messages, this study uses text analysis, text feature extraction, model building, text mining, and other evaluation methods to study and evaluate the three aspects of government services: analysis of public comments, mining of hot issues and evaluation of replies, which aims to prompt the government to understand the needs of the people quickly and solve the relevant problems in a timely and effective manner. The results show that the final classification accuracy using BERT is 3.4% and 1.8% higher than that using TF-IDF and Word2vec, respectively. Multi-classification of message data was realized by BERT combined with the LinearSVC algorithm, and the crowd message was accurately divided into seven types of problems, with an accuracy of 96.7%. It is intended to be transferred to relevant departments for processing. For problems related to people’s livelihood, law, economy, and other aspects, different departments should take countermeasures to solve them and achieve systematic, departmental, and regional coordination. This will enhance the ability of government platforms to deal with problems. Through the definition of hot indexes, hot issues mining can timely find the outstanding problems reflected by the masses. At the same time, the feedback evaluation system can comprehensively evaluate the work of relevant departments from the perspectives of relevance, completeness, and interpretability. Big data analysis technology based on text mining is a feasible way to solve the difficulties of text data analysis. The analysis model constructed in this study is suitable for mining and analyzing unstructured data with short text features, and the results can provide guidance for government decision-making.