This paper illustrates the application of data science techniques to analyze complex human communication in the political sphere, specifically using tweets from political party leaders as a proxy for political programs and ideas. It examines the temporal evolution of tweet content in reaction to specific events and employs sentiment analysis. Furthermore, a Fully-Connected Neural Network (FCNN) is trained to predict political affiliation and leaning with high precision, demonstrating the utility of data science in understanding political dynamics.
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