Artificial Intelligence-Powered Sentiment Analysis Tool to Assess the Popularity of Political Leaders in Informal Communities

Abstract

This study investigates the utility of Artificial Intelligence sentiment analysis tools in assessing the popularity of political leaders within informal communities. In contemporary socio-political landscapes, understanding public sentiment towards political figures is paramount for gauging societal perceptions and predicting electoral outcomes. Leveraging sentiment analysis techniques, this research delves into the nuanced expressions and sentiments prevalent in informal online communities regarding political leaders. By analyzing textual data from social media platforms, forums, and other digital sources, the study aims to discern patterns and trends indicative of public opinion dynamics. Through natural language processing and machine learning algorithms, sentiment analysis tools enable the automated extraction of sentiments, emotions, and attitudes towards political leaders, providing valuable insights into their perceived popularity and public reception. The findings contribute to our understanding of the intersection between technology, politics, and public opinion, shedding light on the evolving landscape of political discourse in digital spaces. The study underscores the significance of sentiment analysis for political analysts, policymakers, and researchers in comprehending and navigating the complexities of contemporary political environments.