Unveiling political rhetoric: exploring natural language processing methods to analyze political discourse

Abstract

Politics has significant effects on how a country develops and how we live our daily lives, affecting things like public services, social norms, and economic policies, extending its impact to other countries. The United States, regarded as the most influential political entity, through its election results, not only influences national policies but also has far-reaching global effects across several fields. This research focuses on the evaluation of political textual data collected from Twitter and Reddit comments. This study’s main objective is to enhance the detection accuracy of political comments. To achieve a significant improvement in the detection of political comments, we used advanced language models, specifically the Bidirectional Long Short-Term Memory (BiLSTM), Multilayer BiLSTM, Bidirectional Encoder Representations from Transformers (BERT), Robustly optimized BERT approach (RoBERTa), and A Lite BERT (ALBERT) models. These models were used to significantly increase efficiency and accuracy. By improving this detection ability, social media platforms will be able to effectively moderate political discourse and obtain deeper insights into public support for different political parties.