当一个国家说话：机器学习和NLP在孟加拉国2024年大规模抗议期间的人们情感分析
When a Nation Speaks: Machine Learning and NLP in People's Sentiment Analysis During Bangladesh's 2024 Mass Uprising
AI总结：
本研究利用机器学习和NLP技术，首次在孟加拉国2024年大规模抗议期间对孟加拉语进行情感分析，通过分析公众情绪揭示政治动荡中的情感动态。
摘要：
情感分析，作为自然语言处理（NLP）领域的一个新兴研究方向，主要被探索在选举和社会媒体趋势等背景下，但在 civil unrest 中的情感动态理解仍存在显著空白，尤其是在孟加拉语中。我们的研究首次在国家危机中对孟加拉语进行情感分析，通过分析孟加拉国2024年大规模抗议期间的公众情绪。我们收集了2028个注释的新闻标题，来自主要的Facebook新闻门户，将其分类为愤怒、希望和绝望。通过潜在狄利克雷分配（LDA），我们识别出政治腐败和公众抗议等普遍主题，并分析了互联网断黑等事件如何影响情感模式。它优于多语言转换器（mBERT: 67%，XLM-RoBERTa: 71%）和传统机器学习方法（SVM和逻辑回归：两者均为70%）。这些结果突显了语言特定模型的有效性，并提供了关于政治动荡期间公众情绪的宝贵见解。
英文摘要：
Sentiment analysis, an emerging research area within natural language processing (NLP), has primarily been explored in contexts like elections and social media trends, but there remains a significant gap in understanding emotional dynamics during civil unrest, particularly in the Bangla language. Our study pioneers sentiment analysis in Bangla during a national crisis by examining public emotions amid Bangladesh's 2024 mass uprising. We curated a unique dataset of 2,028 annotated news headlines from major Facebook news portals, classifying them into Outrage, Hope, and Despair. Through Latent Dirichlet Allocation (LDA), we identified prevalent themes like political corruption and public protests, and analyzed how events such as internet blackouts shaped sentiment patterns. It outperformed multilingual transformers (mBERT: 67%, XLM-RoBERTa: 71%) and traditional machine learning methods (SVM and Logistic Regression: both 70%). These results highlight the effectiveness of language-specific models and offer valuable insights into public sentiment during political turmoil.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
