Comparative Analysis of Contextual AI for Political Tweets

This paper provides a comprehensive survey of Contextual AI applications for analyzing political discourse on Twitter, focusing on the latest advancements in Natural Language Processing (NLP) and machine learning models. Analyzing Twitter data, with its informal language and rapid content generation, presents challenges like sarcasm, slang, and polarization. Contextual AI models, such as BERT and GPT-based architectures, offer significant improvements in political sentiment detection, emotion analysis, and bias identification. Key studies surveyed in this paper report high performance: the PoliBERT model achieved an F1 score of 0.95 and a precision of 0.96, while the sentiment analysis of political opinions showed balanced metrics with an F1 score of 0.89 and an accuracy of 0.88. A study on detecting political leanings achieved a 99% confidence level with an accuracy of 0.96 and ROC AUC of 0.95. These results demonstrate the potential of Contextual AI for accurately classifying political biases, real-time sentiment analysis, and handling complex language nuances. Future research should focus on real-time multilingual analysis, ethical considerations, and bias mitigation. This survey highlights the importance of Contextual AI in providing more precise, nuanced, and inclusive insights into political discourse on social media.