Developing NLP Pipelines for Analyzing Linguistic Trends in Political and Social Discourse
Abstract
Political and social discourse analysis has become increasingly important in understanding public opinion dynamics and democratic processes, yet traditional manual analysis methods struggle to process the vast volumes of digital communication generated daily. This study aims to develop comprehensive Natural Language Processing (NLP) pipelines capable of automatically identifying and analyzing linguistic trends in political and social discourse across multiple digital platforms. We implemented a multi-stage pipeline incorporating advanced transformer-based models, sentiment analysis, topic modeling using Latent Dirichlet Allocation (LDA), and temporal trend analysis techniques, validated on a dataset of 500,000 social media posts and political speeches from 2020-2024. Our pipeline achieved 89.3% accuracy in political stance classification, 92.1% precision in sentiment analysis, and successfully identified 15 distinct thematic clusters with high coherence scores (average 0.78). The developed framework demonstrates significant improvements over existing approaches in processing speed (40% faster) and analytical depth, providing valuable insights into political polarization patterns and social movement evolution. These findings have substantial implications for political science research, policy analysis, and democratic engagement monitoring.