RRepoGEO

REPOGEO REPORT · LITE

kk7nc/Text_Classification

Default branch master · commit 4d72fc88 · scanned 5/17/2026, 2:28:10 PM

GitHub: 1,829 stars · 542 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's opening to clarify its nature as a survey/research resource

    Why:

    COPY-PASTE FIX
    Add a clear introductory paragraph immediately after the main title, such as: "This repository serves as a comprehensive survey and accompanying collection of implementations for various text classification algorithms. It is designed primarily as an educational and research resource for students, researchers, and practitioners to explore, understand, and compare different techniques, rather than a production-ready library or framework for direct integration into applications."
  • mediumhomepage#2
    Add the referenced paper's URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/1904.08067
  • lowabout#3
    Refine the repository description to emphasize its role as a research/educational resource

    Why:

    CURRENT
    Text Classification Algorithms: A Survey
    COPY-PASTE FIX
    A comprehensive survey and collection of implementations for text classification algorithms, designed as a research and educational resource.

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
scikit-learn/scikit-learn
Recommended in 5 of 2 queries
COMPETITOR LEADERBOARD
  1. scikit-learn/scikit-learn · recommended 5×
  2. huggingface/transformers · recommended 4×
  3. dmlc/xgboost · recommended 1×
  4. microsoft/LightGBM · recommended 1×
  5. facebookresearch/fastText · recommended 1×
  • CATEGORY QUERY
    What are the most effective machine learning algorithms for classifying text documents?
    you: not recommended
    AI recommended (in order):
    1. BERT (huggingface/transformers)
    2. RoBERTa (huggingface/transformers)
    3. DistilBERT (huggingface/transformers)
    4. ALBERT (huggingface/transformers)
    5. XGBoost (dmlc/xgboost)
    6. LightGBM (microsoft/LightGBM)
    7. FastText (facebookresearch/fastText)
    8. Support Vector Machines (SVM) (scikit-learn/scikit-learn)
    9. Logistic Regression (scikit-learn/scikit-learn)
    10. Naive Bayes (scikit-learn/scikit-learn)
    11. Multinomial Naive Bayes (scikit-learn/scikit-learn)
    12. Complement Naive Bayes (scikit-learn/scikit-learn)

    AI recommended 12 alternatives but never named kk7nc/Text_Classification. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I compare different text classification techniques for my NLP application?
    you: not recommended
    AI recommended (in order):
    1. Scikit-learn
    2. FastText
    3. Hugging Face Transformers
    4. PyTorch
    5. TensorFlow
    6. Keras
    7. Spark MLlib
    8. Word2Vec
    9. GloVe
    10. optuna
    11. Weights & Biases
    12. SHAP
    13. LIME

    AI recommended 13 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 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?

  • In one sentence, what problem does the repo kk7nc/Text_Classification solve, and who is the primary audience?
    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?

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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