Manual Sentiment Analysis of X Posts and Short-Term Stock Volatility: Daily Correlations and Regression Evidence from AAPL, TSLA, and NVDA

Abstract

Social media platforms such as X (formerly Twitter) and Reddit have become important places where investors share opinions, news, and rumors about stocks in real time. Recent research suggests that changes in online sentiment can be linked to sudden movements in stock prices and market volatility, especially during events like meme stock rallies. This paper investigates whether social media sentiment is related to short‑term stock market volatility for a small set of well‑known companies. Using daily price and volume data from public finance websites, combined with manually coded social media posts, I construct simple measures of stock volatility, posting activity, and average sentiment for each day in a chosen sample period. I then use graphs, correlations, and basic regression analysis to see whether days with more intense or more negative sentiment tend to coincide with higher volatility. The goal of this study is not to “predict” the market, but to explore whether social media conversations appear to move together with short‑term risk in individual stocks and to highlight the potential benefits and limits of using sentiment as an additional tool for understanding market behavior.