Political Tweets Analysis in The Domain of Natural Language Processing

In an era marked by unprecedented digital engagement, social media platforms have become pivotal arenas for political discourse. This study delves into the realm of Natural Language Processing (NLP) to analyze political tweets, offering a comprehensive exploration of sentiment, sentiment shifts, and trending topics. By harnessing cutting-edge NLP techniques, this research uncovers the complex interplay of language, emotions, and political ideologies in the digital sphere. Our approach begins with data collection and preprocessing, including the extraction of tweets from diverse sources. Leveraging state-of-the-art NLP models, sentiment analysis unveils the emotional undertones of political discourse, shedding light on the dynamics of public opinion. Additionally, sentiment shift analysis tracks temporal changes in sentiment to capture evolving perspectives.

Furthermore, this study employs topic modeling to identify key political themes, discourse patterns, and emerging trends. It also explores the impact of user demographics and geographic location on tweet content and sentiment. By merging quantitative and qualitative methodologies, this research offers valuable insights into the complexities of political communication in the digital age. The findings not only enrich our understanding of political discourse but also provide a foundation for data-driven decision-making in politics, policy formulation, and election campaigns. In an era where tweets hold the power to shape public opinion and influence political landscapes, this study contributes to the evolving field of NLP, illuminating the intricate tapestry of politics woven through the fabric of social media.

Keywords: Decision Tree, Random Forest and Logistic Regression.