This project uses advanced data analysis methods with the aim to answer the research question: How do Elon Musk's personal social media conversations compare with relative news articles in influencing Tesla's stock prices?
Three data sources have been gathered - a dataset containing changes in the stock price changes for Tesla, an open-source dataset containing news articles and a dataset containing Tweets from Elon Musk's official Twitter account, all over several years. The data has been cleaned, collated and stored in a suitable data warehouse.
Using R and Tableau, an analysis has been conducted using various analytical methods, such as sentiment analysis by grouping tweets and news articles into different types of sentiment, such as positive, neutral and negative and searching for trends between these sentiment types and spikes or dips in stock prices. A statistical analysis has been carried out byperforming hypothesis testing in order to determine statistical significance in the results.Also used the Pearson correlation coefficient to determine the level of linear correlation between stock prices and Tweets and/or News Articles.