RRepoGEO

REPOGEO REPORT · LITE

kk7nc/Text_Classification

Default branch master · commit 4d72fc88 · scanned 6/28/2026, 7:03:45 PM

GitHub: 1,824 stars · 541 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 kk7nc/Text_Classification, 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 opening to highlight both survey and implementations

    Why:

    CURRENT
    Text Classification Algorithms: A Survey
    COPY-PASTE FIX
    This repository provides a comprehensive survey of Text Classification Algorithms, alongside practical implementations and code examples for various methods, from traditional machine learning to deep learning approaches.
  • highhomepage#2
    Add the arXiv paper URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/1904.08067
  • mediumreadme#3
    Explicitly state the MIT license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under the MIT License - see the LICENSE file for details.

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 kk7nc/Text_Classification
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Keras
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Keras · recommended 2×
  2. TensorFlow · recommended 2×
  3. PyTorch · recommended 2×
  4. BERT · recommended 1×
  5. RoBERTa · recommended 1×
  • CATEGORY QUERY
    What are the best machine learning approaches for categorizing unstructured text documents?
    you: not recommended
    AI recommended (in order):
    1. BERT
    2. RoBERTa
    3. DistilBERT
    4. XLNet
    5. FastText
    6. XGBoost
    7. Word2Vec
    8. GloVe
    9. Support Vector Machines (SVM)
    10. Logistic Regression
    11. Keras
    12. TensorFlow
    13. PyTorch

    AI recommended 13 alternatives but never named kk7nc/Text_Classification. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I pre-process text data and apply various algorithms for document categorization?
    you: not recommended
    AI recommended (in order):
    1. scikit-learn
    2. NLTK
    3. spaCy
    4. Gensim
    5. Keras
    6. TensorFlow
    7. PyTorch
    8. Hugging Face Transformers

    AI recommended 8 alternatives but never named kk7nc/Text_Classification. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 kk7nc/Text_Classification?
    pass
    AI named kk7nc/Text_Classification explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts kk7nc/Text_Classification in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kk7nc/Text_Classification 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 kk7nc/Text_Classification solve, and who is the primary audience?
    pass
    AI did not name kk7nc/Text_Classification — 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?

Embed your GEO score

Drop this badge into the README of kk7nc/Text_Classification. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/kk7nc/Text_Classification.svg)](https://repogeo.com/en/r/kk7nc/Text_Classification)
HTML
<a href="https://repogeo.com/en/r/kk7nc/Text_Classification"><img src="https://repogeo.com/badge/kk7nc/Text_Classification.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

kk7nc/Text_Classification — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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