The National Science Foundation needed to analyze large-scale social media data to understand the emotional impact of COVID-19 and detect scams related to the pandemic.
Collected 30 million data points from platforms like Twitter, Reddit, and Flickr using Python and REST API.
Applied algorithms like Random Forest, Decision Trees, and XGBoost to detect emotions and uncover scams.
Used the LDA algorithm to identify the 20 most relevant topics from millions of tweets, offering valuable insights into public sentiment.
Delivered actionable insights into public emotions and scam patterns during COVID-19.
Earned prestigious recognition for the project, highlighting its impact on public understanding.
