This is the first of three chapters dedicated to extracting signals for algorithmic trading strategies from text data using natural language processing (NLP) and machine learning (ML).
Text data is very rich in content, yet unstructured in format, and hence requires more preprocessing so that an ML algorithm can extract the potential signal. The key challenge lies in converting text into a numerical format for use by an algorithm, while simultaneously expressing the semantics or meaning of the content. We will cover several techniques that capture nuances of language that are readily understandable to humans so that they can become an input for ML algorithms.
In this chapter, we introduce fundamental feature extraction techniques that focus on individual semantic units; that is, words or short groups of words called tokens. We will show how to represent...
