Sentiment Analysis For Algorithmic Trading

Max Margenot, Academia and Data Science Lead at Quantopian, discusses how to build a model in Python to analyze sentiment from Twitter data. He covers basic natural language processing (NLP) techniques, providing different ways to extract features from text data for use in modeling. He also describes a potential use of this sentiment model in developing algorithmic trading signals for factor models. You will get an understanding of how to use the Word2Vec Python package and long short-term memory networks to analyze Twitter data and turn those insights into trades.

You can find the slides here.