RRepoGEO

REPOGEO REPORT · LITE

ZebangCheng/Emotion-LLaMA

Default branch main · commit 4ee28d20 · scanned 6/16/2026, 1:18:29 AM

GitHub: 591 stars · 70 forks

AI VISIBILITY SCORE
22 /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
1 / 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 ZebangCheng/Emotion-LLaMA, 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
    Clarify Emotion-LLaMA's unique value proposition in the README's opening

    Why:

    CURRENT
    The README's "Overview" section begins by describing the general problem of emotion perception.
    COPY-PASTE FIX
    Insert the following sentence as the very first sentence of the "## 🚀 Overview" section: "Emotion-LLaMA is a cutting-edge Multimodal Large Language Model (MLLM) specifically engineered for advanced emotion recognition and reasoning, uniquely integrating audio and visual cues to capture subtle facial micro-expressions and complex emotional expressions."
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/ZebangCheng/Emotion-LLaMA
  • lowtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    affective-computing, instruction-tuning, mllm
    COPY-PASTE FIX
    affective-computing, instruction-tuning, mllm, emotion-recognition

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 ZebangCheng/Emotion-LLaMA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow · recommended 1×
  2. PyTorch · recommended 1×
  3. Keras · recommended 1×
  4. scikit-learn · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How to build an AI that understands human emotions from various inputs?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. PyTorch
    3. Keras
    4. scikit-learn
    5. Hugging Face Transformers
    6. NLTK (Natural Language Toolkit)
    7. spaCy
    8. librosa
    9. OpenSMILE
    10. PyAudio
    11. OpenCV
    12. Dlib
    13. MediaPipe

    AI recommended 13 alternatives but never named ZebangCheng/Emotion-LLaMA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What MLLM solutions are available for improving affective computing through instruction tuning?
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. InstructBLIP
    3. MiniGPT-4
    4. OpenFlamingo
    5. Qwen-VL
    6. Fuyu-8B

    AI recommended 6 alternatives but never named ZebangCheng/Emotion-LLaMA. 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 ZebangCheng/Emotion-LLaMA?
    pass
    AI did not name ZebangCheng/Emotion-LLaMA — 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 ZebangCheng/Emotion-LLaMA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ZebangCheng/Emotion-LLaMA 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 ZebangCheng/Emotion-LLaMA solve, and who is the primary audience?
    pass
    AI did not name ZebangCheng/Emotion-LLaMA — 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 ZebangCheng/Emotion-LLaMA. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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ZebangCheng/Emotion-LLaMA — 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