I Compared 5 Python Text Analysis Libraries — Then Built a REST API Instead

When you need readability scores in Python, your first search turns up textstat. For sentiment, VADER. For keyword extraction, yake or keybert. For everything at once, you're running 3-4 libraries with their own install requirements, version conflicts, and update cycles. I spent a few hours comparing the main options. Here's what I found — and why I ended up building a REST API instead. The five main options Library What it does Install size textstat Readability scoring (FK, Fog, SMOG, etc.) Small vaderSentiment Sentiment for social media text Small TextBlob Sentiment + NLP basics Medium NLTK Full NLP toolkit Large spaCy Production NLP Large What each one actually does textstat is the go-to for readability. It gives you Flesch-Kincaid, Gunning Fog, SMOG, Coleman-Liau, ARI, and Dale-Chall in one call. PyPI shows it at around 218,000 downloads per week, which tells you there's a real use case here. What it doesn't do: sentiment, keywords, or anything beyond readability formulas. import t