NBA Player Sentiment Analysis

// Description

This project scrapes and processes posts from X (Twitter) to perform sentiment analysis on NBA players. Using natural language processing, each post is classified as positive, negative, or neutral, and the results are aggregated to surface public perception trends over time. The pipeline ingests raw social data, cleans and tokenizes the text, runs it through a sentiment model, and stores the output for visualization. The goal was to explore how social media discourse shifts around players during different points in the season — trades, injuries, big performances — and turn that signal into something meaningful.

// Skills Used