Sentiment Analysis in Financial Markets: Using NLP to Predict Stock Price from News and Social Media Data

This research explores the application of NLP techniques to forecast changes in stock prices by applying sentiment analysis based on views expressed by the public in 51 news stories and 28 social media posts. Investor sentiment plays a crucial role in influencing market volatility and price trends. This study specifically focuses on Tesla Inc. (TSLA) as a case study, collecting data for a period of one month (October 1 to November 2). Sentiment data was sourced from Alpha Vantage for news articles and Reddit for social media posts. Sentiment scoring was conducted using VADER and TextBlob, with polarity values between -1 (negative) to +1 (positive). The correlation analysis between sentiment scores and Tesla's daily closing prices actually presents a weak positive correlation, with the VADER social media sentiment scoring the highest correlation value of +0.2332. The results indicate that, although social media sentiments present a moderate association with stock price movements, news data is neutral. This study highlights real-time public sentiment as a supplementary tool for financial forecasting and, thus, draws significant implications for investment strategies and market trend analysis.