Data-driven Public Perception and Media Impact Analytics of Political Event: Tarunner Somabesh

In today's fast-paced political world, understanding how media shapes public views on big events is key for leaders and analysts. Works like this one are vital as they use modern AI tools to dig deep into data from news, videos, and searches, giving real facts over guesswork. This study introduces a comprehensive framework that integrates advanced data mining techniques and artificial intelligence (AI) tools to evaluate the media impact and public perception of a significant political event- “Tarunner Somabesh” organized by the Bangladesh Nationalist Party (BNP). Leveraging Python-based data acquisition from Google News, YouTube APIs, and Google Trends, combined with natural language processing (NLP) algorithms trained for multilingual sentiment analysis, the study elucidates the dynamic interaction between traditional media coverage, digital commentary, and public search behavior. The findings illustrate the dominant narrative strategies employed by media outlets, reflect regional variances in public sentiment, and underscore the pivotal role of AI in facilitating scalable and rigorous political media analytics. Overall, this approach sets a strong example for future studies on political media in places like Bangladesh.