Design of a Machine Learning-Based Sentiment Analysis Model for Government Weibo
A machine learning-based sentiment analysis model for government Weibo is proposed to address the challenges posed by cluttered comments and subjective reviews.This model quantitatively analyzes sentiments on government Weibo,providing a reliable foundation for automatic reviews.Using the Weibo of the 2022 Beijing Winter Olympics and the Chinese Football Association as case studies,the methodology begins with the expansion of relevant vocabulary,followed by data cleaning and text feature representation.Subsequently,machine learning models are employed to assess emotional tendencies,and the Chinese sentiment lexicon from the Dalian University of Technology is utilized to calculate emotional intensity.This study employs decision trees,Naive Bayes,and Support Vector Machine(SVM)models,incorporating both bag-of-words and Word2vec models for sentiment prediction and performance comparison.The experimental results indicate that the SVM model using Word2vec achieves an accuracy of 84.3％in sentiment classification.This demonstrates the effectiveness of the proposed model in predicting sentiments on government Weibo,indicating its potential for automatic review tasks.
万方数据
维普
