Automated Sentiment Classification and Topic Discovery in Large-Scale Social Media Streams
This work provides a scalable methodology for social media analysis in dynamic geopolitical contexts, but it is incremental as it combines existing techniques without introducing new methods.
The authors tackled the problem of analyzing sentiment and topics in large-scale Twitter data by developing a pipeline that uses pre-trained models for sentiment labeling and LDA for topic discovery, resulting in an interactive visualization tool for exploring trends across time and regions.
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.