Abstract
We present a framework for large-scale sentiment and topic analysis of Twitter discourse. Our pipeline begins with targeted data collection using conflict-specific keywords, followed by automated sentiment labeling via multiple pre-trained models to improve annotation robustness. We examine the relationship between sentiment and contextual features such as timestamp, geolocation, and lexical content. To identify latent themes, we apply Latent Dirichlet Allocation (LDA) on partitioned subsets grouped by sentiment and metadata attributes. Finally, we develop an interactive visualization interface to support exploration of sentiment trends and topic distributions across time and regions. This work contributes a scalable methodology for social media analysis in dynamic geopolitical contexts.
Abstract (translated)
我们提出了一种针对大规模Twitter言论情感和主题分析的框架。我们的数据处理流程首先通过使用冲突特定关键词进行目标化数据收集，随后利用多个预训练模型自动标注情感标签以增强注释的稳健性。接下来，我们研究了情感与上下文特征（如时间戳、地理位置和词汇内容）之间的关系。为了识别潜在的主题，我们在按情感和元数据属性分组的分区子集中应用潜在狄利克雷分配（LDA）。最后，我们开发了一个交互式可视化界面，以支持对不同时段和地区的情感趋势和主题分布进行探索。这项工作为动态地缘政治环境中社交媒体分析提供了一种可扩展的方法论。
URL
https://arxiv.org/abs/2505.01883
