Dynamic Topic Modeling for Real-Time Social Media Sentiment Analysis
Abstract
Social media platforms serve as a rich source of public sentiment, providing valuable insights for businesses and policymakers. This study develops a dynamic topic modeling framework that combines Latent Dirichlet Allocation (LDA) with time-series analysis to track evolving sentiments in real-time. Using datasets from Twitter and Reddit, the framework identifies emerging trends, sentiment shifts, and their drivers. The results demonstrate the model’s effectiveness in capturing temporal sentiment dynamics, making it a powerful tool for crisis management and brand monitoring.
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