RRepoGEO

REPOGEO REPORT · LITE

liqian-bio/text-classification-surveys

Default branch master · commit cbaa5dfc · scanned 6/1/2026, 8:12:41 PM

GitHub: 613 stars · 102 forks

AI VISIBILITY SCORE
10 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
0 / 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 liqian-bio/text-classification-surveys, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    text-classification, nlp, deep-learning, machine-learning, survey, literature-review, natural-language-processing, text-mining, ai, research-papers, datasets, evaluation-metrics
  • highlicense#2
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Choose and add a standard open-source license file (e.g., MIT, Apache-2.0, GPL-3.0) to the repository root.
  • mediumreadme#3
    Refine README's opening sentence to emphasize its curated nature

    Why:

    CURRENT
    This repository contains resources for Natural Language Processing (NLP) with a focus on the task of Text Classification. The content is mainly from paper 《A Survey on Text Classification: From Shallow to Deep Learning》
    COPY-PASTE FIX
    This repository is a curated collection of resources and survey papers focused on Natural Language Processing (NLP) and the task of Text Classification. It primarily summarizes content from the paper 《A Survey on Text Classification: From Shallow to Deep Learning》

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 liqian-bio/text-classification-surveys
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers With Code
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 1×
  2. Hugging Face · recommended 1×
  3. Kaggle · recommended 1×
  4. Google AI Blog / Google Research · recommended 1×
  5. NLP Progress · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive overview of text classification models and datasets?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. Hugging Face
    3. Kaggle
    4. Google AI Blog / Google Research
    5. NLP Progress
    6. Stanford NLP Group
    7. Speech and Language Processing

    AI recommended 7 alternatives but never named liqian-bio/text-classification-surveys. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the key evaluation metrics for text classification, including deep and shallow learning approaches?
    you: not recommended
    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 liqian-bio/text-classification-surveys?
    pass
    AI did not name liqian-bio/text-classification-surveys — 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 liqian-bio/text-classification-surveys in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name liqian-bio/text-classification-surveys — 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 liqian-bio/text-classification-surveys solve, and who is the primary audience?
    pass
    AI did not name liqian-bio/text-classification-surveys — 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 liqian-bio/text-classification-surveys. 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/liqian-bio/text-classification-surveys.svg)](https://repogeo.com/en/r/liqian-bio/text-classification-surveys)
HTML
<a href="https://repogeo.com/en/r/liqian-bio/text-classification-surveys"><img src="https://repogeo.com/badge/liqian-bio/text-classification-surveys.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

liqian-bio/text-classification-surveys — 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