Abstract: This article addresses the issues of chaotic comments and difficult moderation in government Weibo discussions and proposes a machine learning-based model for sentiment analysis. This model quantitatively analyzes emotions in government Weibo posts, providing an effective basis for automated moderation. Using the examples of the Winter Olympics and the Chinese Football Association Weibo accounts, the research first expands the relevant vocabulary associated with these topics and performs data cleaning and text feature representation. Subsequently, machine learning models are employed to determine sentiment tendencies, and the Chinese sentiment lexicon from Dalian University of Technology is used to calculate sentiment intensity. This paper employs decision tree, Naïve Bayes, and Support Vector Machine models based on both the bag-of-words model and Word2Vec model and evaluates their performance comparatively. Experimental results demonstrate that, under the Word2Vec-based Support Vector Machine model, the accuracy of sentiment classification reaches 84.3%. This suggests the effectiveness and comprehensiveness of the proposed model in predicting sentiment in government Weibo posts and its potential application in automated moderation.

摘要： 本文针对政务微博评论杂乱、审核困难的问题，提出了一种基于机器学习的政务微博情感分析模型。该模型能够量 化分析政务微博中的情感，为自动审核提供了有效依据。研究以冬奥会和中国足协微博为例，首先扩展了与冬奥会和中国足 协相关的词汇，并进行了数据清洗和文本特征表示。然后，采用机器学习模型进行情感倾向判断，并结合大连理工大学中文 情感词汇文本计算情感强度。本文分别采用了基于词袋模型和 Word2vec 模型的决策树、朴素贝叶斯和支持向量机模型，并对 它们的性能进行了对比评估。实验结果表明，在基于 Word2vec 的支持向量机模型下，模型对于情感分类的准确率达到 84.3%。 这表明本文提出的模型在预测政务微博情感方面具有有效性和全面性，可应用于政务微博自动审核。