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

2025/05/03 by Yiwen Lu, Lu, Yiwen, Siheng Xiong +3
Computer Science · Physics and Astronomy · #Advanced Text Analysis Techniques #Complex Network Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining

paper · pdf · doi:10.48550/arxiv.2505.01883

openalex publication_date 2025/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• BERTweet: A pre-trained language model for English Tweets
• Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks
• Interactive Attention Networks for Aspect-Level Sentiment Classification
• Effective LSTMs for Target-Dependent Sentiment Classification

Related

• Topic Based Sentiment Analysis Using Deep Learning
• Approaches for Sentiment Analysis on Twitter: A State-of-Art study
• Topic Modelling and Event Identification from Twitter Textual Data
• Sentiment Analysis of Twitter Posts on Global Conflicts
• Exploring a Hybrid Deep Learning Framework to Automatically Discover Topic and Sentiment in COVID-19 Tweets