用机器学习分析社交媒体上的政治观点情感，区分多种立场。
Multiclass Sentiment Analysis for Identifying Political Viewpoints
- 采用XGBoost和BERT两种模型处理多类政治情感分类。
- BERT模型在测试集上F1分数达0.2806，表现略优于XGBoost。
- 为复杂政治语境下的情感分析提供基准参考，适合舆情研究者。
社交媒体的快速发展产生了大量政治讨论内容，为分析公众意见和识别不同政治立场提供了宝贵机会。情感分析（Sentiment Analysis, SA）是自然语言处理中的核心任务，可用于计算文本中态度与观点的研究，对理解政治话语日益重要。本文研究了社交媒体上政治观点的多类别情感分析，即自动区分针对政治议题和人物的多种情感类别。为此，我们设计并评估了基于XGBoost和BERT的两种机器学习方法，在标注的政治社交媒体帖子数据集上进行训练与评估，使用标准分类指标。实验结果显示，XGBoost模型在测试集上的F1分数为0.2835，而基于BERT的模型达到0.2806。这些结果表明，对复杂且语境化的政治话语进行情感分类仍具挑战性，并为未来多类别政治情感分析研究提供了基准。
原文摘要 · Abstract (English)
The rapid growth of social media has created vast amounts of political discourse, which provides valuable opportunities to analyze public opinions and identify different political perspectives. Sentiment Analysis (SA) is a core task in Natural Language Processing (NLP) that allows the computational study of attitudes and opinions in textual data, and has become increasingly important for understanding political discourse. In this work, we investigate multiclass sentiment analysis of political view- points on social media, that is to automatically discriminate multiple sentiment classes over political issues and figures. To solve this task we design and evaluate two machine-learning approaches based on XGBoost and BERT. We train and evaluate the models on a labeled dataset of political social media posts using standard classification metrics. The experimental results show that the XGBoost model reaches an F1-score of 0.2835 and the BERT- based model reaches an F1-score of 0.2806 on the test set. These results demonstrate the challenge of classifying complex and contextualized political discourse sentiment and provide a baseline for future research in multiclass political sentiment analysis.
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