Text Data for Trading – Sentiment Analysis
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 but highly unstructured, so it requires more preprocessing to enable an ML algorithm to extract relevant information. A key challenge consists of converting text into a numerical format without losing its meaning. We will cover several techniques capable of capturing the nuances of language so that they can be used as input for ML algorithms.
In this chapter, we will 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 documents as vectors of token counts by creating a document-term matrix and then proceed to use it as input for news classification and sentiment analysis. We will also...
