Automated Sentiment Classification and Topic Discovery in Large-Scale Social Media Streams

TL;DR

This paper introduces a scalable framework for analyzing large-scale Twitter data by combining automated sentiment labeling, topic modeling, and interactive visualization to understand discourse in geopolitical contexts.

Contribution

It presents a novel pipeline integrating multiple pre-trained models, LDA-based topic discovery, and visualization tools for comprehensive social media analysis.

Findings

Effective sentiment classification across large datasets

Identification of latent themes related to geopolitical issues

Visualization of sentiment and topic trends over time and regions

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.