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Natural language processing

As explained in Investing for Programmers

A field of computing concerned with analyzing and generating human language in textual or spoken form.

Natural language processing (NLP) analyzes text to extract useful signals. In financial research, it turns language from news and other sources into sentiment scores that can support trading decisions. section 8.2 section 11.3.2

A practical NLP workflow can:

  • Collect text through web crawlers or APIs. section 11.3.2
  • Parse recurring sources and measure how their moods change over time. section 11.3.2
  • Combine sentiment with other quantifiable signals in scorecards or supervised machine-learning datasets. section 11.3.2

Treat results cautiously: media sources change, historical comparisons become less reliable, and backtests can overfit. Past performance does not guarantee future results. section 11.3.2

How Investing for Programmers covers it

How deep it goes
Explains collection, parsing, scoring, and signal combination, but defers transformer and attention internals.
Examples
Uses collected media, recurring source moods, sentiment scores, scorecards, and historical trading datasets.
What it assumes
Assumes text sources and historical observations; distinguishes language analysis from task-specific predictive models.
What you can do after
Apply it via web crawlers or APIs to create comparable sentiment signals for scorecards and backtests.
How it connects
Connects NLP with generative AI, large language models, web crawling, APIs, sentiment analysis, backtesting, and supervised machine learning.
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