REPOGEO REPORT · LITE

totalgood/nlpia

Default branch master · commit c3571dc2 · scanned 6/1/2026, 1:31:40 AM

GitHub: 635 stars · 260 forks

AI VISIBILITY SCORE

33 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface totalgood/nlpia, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README's opening to clarify its role as official book code

Why:

CURRENT

# NLPIA

Community-driven code for the book **N**atural **L**anguage **P**rocessing **i**n **A**ction.

## Description

A community-developed book about building socially responsible NLP pipelines that give back to the communities they interact with.

COPY-PASTE FIX

# NLPIA: Official Code and Examples for "Natural Language Processing in Action" (O'Reilly)

This repository provides the community-driven, open-source code, examples, and exercises accompanying the O'Reilly book "Natural Language Processing in Action." It's designed to help readers build socially responsible NLP pipelines and apply practical machine learning techniques.

mediumreadme#2

Add a 'Who is this for?' section to the README

Why:

COPY-PASTE FIX

## Who is this for?

This repository is primarily for readers of the 'Natural Language Processing in Action' book, students, and practitioners looking for practical, hands-on examples and code to learn and apply NLP concepts. It's ideal for those who want to build socially responsible NLP pipelines and understand the underlying machine learning techniques.

lowtopics#3

Add more specific educational and book-related topics

Why:

CURRENT

ai, book, bot, chatbot, deep-learning, machine-learning, natural-language-processing, nlp, virtual-assistant

COPY-PASTE FIX

ai, book, bot, chatbot, deep-learning, machine-learning, natural-language-processing, nlp, virtual-assistant, education, tutorial, learning, book-companion

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface totalgood/nlpia

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

nltk/nltk

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

nltk/nltk · recommended 1×
explosion/spaCy · recommended 1×
huggingface/transformers · recommended 1×
scikit-learn/scikit-learn · recommended 1×
piskvorky/gensim · recommended 1×

CATEGORY QUERY
What are good libraries for learning natural language processing concepts from a practical guide?
you: not recommended
AI recommended (in order):
1. NLTK (Natural Language Toolkit) (nltk/nltk)
2. spaCy (explosion/spaCy)
3. Hugging Face Transformers (huggingface/transformers)
4. scikit-learn (scikit-learn/scikit-learn)
5. Gensim (piskvorky/gensim)
6. PyTorch (pytorch/pytorch)
7. TensorFlow (tensorflow/tensorflow)
8. Keras (keras-team/keras)
AI recommended 8 alternatives but never named totalgood/nlpia. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I build a conversational AI bot using Python for natural language understanding?
you: not recommended
AI recommended (in order):
1. Rasa Open Source
2. spaCy
3. NLTK
4. scikit-learn
5. Hugging Face Transformers
6. DeepPavlov
7. Microsoft Bot Framework SDK for Python
8. Azure LUIS
9. Google Cloud Dialogflow
AI recommended 9 alternatives but never named totalgood/nlpia. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of totalgood/nlpia?
pass
AI did not name totalgood/nlpia — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts totalgood/nlpia in production, what risks or prerequisites should they evaluate first?
pass
AI named totalgood/nlpia explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo totalgood/nlpia solve, and who is the primary audience?
pass
AI named totalgood/nlpia explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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Live preview

MARKDOWN (README)

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HTML

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite