In a team of four, we created a solution for extracting stock price and tweets based on ‘cash-tag’ and performing sentiment analysis. We devised a full stack POC leveraging two databases (MongoDB, Postgres), Web Framework (Flask API) and Front-end (HTML, CSS).
We extracted and stored historical stock prices and twitter data for selected stocks(FAANG). We cleaned the raw data, performed sentiment analysis based on the polarity of tweets, and aggregated stock market data and twitter data to predict stock price using a LSTM model.
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