Briefly describe the project you worked on. What did you create, who did you create it for, and how did you approach this project?

In the climate sentiment analysis project, I developed a system to analyze the sentiment of Twitter users towards climate change. The project involved working with quality data and CrowdFlower judgments. I addressed the dataset imbalance by performing random under-sampling and SMOTE sampling. I evaluated various classification models based on precision, recall, and F1-score, with Random Forest performing the best at 92% F1-score. I implemented a web interface using Flask that ran on the best model (Random Forest) and applied it to scrape tweets from 2022. The analysis revealed a significant number of tweets denying climate change. This project showcases my expertise in sentiment analysis, data preprocessing, classification modeling, and web application development.