Text Analysis in Go Without a Machine Learning Library

Go's standard library handles strings and Unicode well. strings.Fields , unicode.IsLetter , bufio.Scanner — you can build word count and basic stats without any third-party packages. Where the ecosystem gets thin is content quality metrics: readability grades, sentiment scoring, keyword extraction. If you've worked with Python's textstat , textblob , or spacy , you've seen how much ground is already covered there. Go is a different story. The Go NLP Landscape Go does have some text processing packages worth knowing: github.com/jdkato/prose is the most complete option. It handles tokenization, part-of-speech tagging, and named entity recognition. Solid for linguistic analysis, but it doesn't cover readability grades (Flesch-Kincaid, Gunning Fog, Coleman-Liau) or AFINN sentiment scoring. Built-in strings and unicode packages get you word counts, sentence boundaries (if you're careful about punctuation), and character-level stats. You can compute a rough syllable count heuristic from ther