RRepoGEO

REPOGEO REPORT · LITE

declare-lab/MELD

Default branch master · commit 2d2011b4 · scanned 5/11/2026, 3:12:54 PM

GitHub: 1,042 stars · 232 forks

AI VISIBILITY SCORE
63 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 declare-lab/MELD, 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
    Add a concise "About this repository" section to the README

    Why:

    CURRENT
    The README currently starts with the title, then a "Note" section about other projects.
    COPY-PASTE FIX
    Add a new section immediately after the main title, e.g., "This repository serves as the official home for the MELD dataset, a comprehensive multimodal (audio, video, and text) multi-party dataset designed for emotion recognition in conversations. It includes data download instructions, updated baselines, and research works utilizing MELD."
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Set the homepage URL to `https://arxiv.org/pdf/1810.02508.pdf` (the updated paper link mentioned in the README).
  • lowreadme#3
    Reorder or clarify the initial "Note" section in the README

    Why:

    CURRENT
    The current README structure places a "Note" section about other projects directly after the main title.
    COPY-PASTE FIX
    Move the "Note" section to a later part of the README, perhaps under a "Related Projects" or "Other Work from Declare-Lab" section, or rephrase it to clearly state its relation to MELD (e.g., "While you're here, check out our related work...").

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
1 / 2
50% of queries surface declare-lab/MELD
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
IEMOCAP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. IEMOCAP · recommended 1×
  2. MSP-IMPROV · recommended 1×
  3. DailyDialog · recommended 1×
  4. EmoContext · recommended 1×
  5. CMU-MOSEI · recommended 1×
  • CATEGORY QUERY
    How can I find a dataset for training emotion recognition models in multi-party conversations?
    you: #3
    AI recommended (in order):
    1. IEMOCAP
    2. MSP-IMPROV
    3. MELD ← you
    4. DailyDialog
    5. EmoContext
    6. CMU-MOSEI
    7. SEMAINE
    Show full AI answer
  • CATEGORY QUERY
    Where can I find resources for multimodal emotion detection in dialogue systems?
    you: not recommended
    AI recommended (in order):
    1. CMU-MOSEI Dataset
    2. MELD (Multimodal EmotionLines Dataset)
    3. Hugging Face Transformers
    4. Hugging Face Datasets
    5. PyTorch
    6. TensorFlow
    7. Keras
    8. Awesome Multimodal Learning GitHub Repository (pliang279/awesome-multimodal-ml)
    9. SpeechBrain (speechbrain/speechbrain)

    AI recommended 9 alternatives but never named declare-lab/MELD. 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 declare-lab/MELD?
    pass
    AI named declare-lab/MELD explicitly

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

  • If a team adopts declare-lab/MELD in production, what risks or prerequisites should they evaluate first?
    pass
    AI named declare-lab/MELD 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 declare-lab/MELD solve, and who is the primary audience?
    pass
    AI named declare-lab/MELD explicitly

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

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declare-lab/MELD — 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
declare-lab/MELD — RepoGEO report