With Twitter users generating ample data during a day on multiple topics, the team mines such data, with mentions of a specific company. The sentiment of these tweets is then correlated to the closing value of a stock by day. Shareholders and company executives may then be able to predict if dissatisfaction affects the value of their company and holdings

• Company name and stock market abbreviation: e.g. Tesla’s market abbreviation  TSLA
• Date range for needed analysis: e.g. 2019-09-23 to 2019-09-27

Python

Installable using pip: tweepy pandas six numpy matplotlib alpha-vantage csv google-cloud-language

• Open-source API’s are tricky because there is no direct assistance from the API’s owners
• API’s are discontinued from time to time, e.g. the Yahoo Finance stock market API; and often, the data is limited, e.g. the Quandl stock market API provides data up to March 26, 2018
• There is a lot of potential with the Twitter API, aside from a sentiment analysis: the team can imagine extensive models which return most used words, heatmaps on where a product is used the most, etc. allowing entities such as small businesses or indie artists to thrive in the digital economy
• For some companies, there may not be a correlation between sentiment and stock value. For some, the correlation might be more visible over a long period of time. The correlation might even be mor elikely due to industry; perhaps, fashion might see higher correlation. The analysis can now be performed with the tool provided.