REPOGEO REPORT · LITE

declare-lab/conv-emotion

Default branch master · commit 6128ca20 · scanned 5/21/2026, 11:52:55 PM

GitHub: 1,516 stars · 344 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

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 declare-lab/conv-emotion, 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 the README's opening to clearly state its purpose as a specialized library/framework

Why:

CURRENT

# Emotion Recognition in Conversations

COPY-PASTE FIX

# Emotion Recognition in Conversations

This repository provides a comprehensive PyTorch-based framework and implementations for various deep learning architectures specifically designed for Emotion Recognition in Conversations (ERC). It serves as a research toolkit and baseline for advancing the state-of-the-art in conversational emotion analysis.

mediumreadme#2

Add a 'Key Features' or 'Why Use This?' section to highlight differentiators

Why:

COPY-PASTE FIX

## Key Features

*   **Specialized for Conversations:** Focuses exclusively on emotion recognition within multi-turn dialogues, leveraging conversational context and inter-speaker dynamics.
*   **Multiple Architectures:** Implements various state-of-the-art deep learning models (e.g., DialogueRNN, bcLSTM, COSMIC, Memory Network) for Emotion Recognition in Conversations (ERC).
*   **PyTorch-based:** Built on PyTorch for flexibility, ease of experimentation, and integration with the broader PyTorch ecosystem.
*   **Research-Oriented:** Provides robust baselines, datasets, and tools for researchers in Natural Language Processing (NLP) and conversational AI.

lowhomepage#3
Add a homepage URL to the repository settings
Why:
COPY-PASTE FIX
```
https://declare-lab.github.io/conv-emotion/
```

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 declare-lab/conv-emotion

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 1×
PyTorch · recommended 1×
TensorFlow · recommended 1×
Keras · recommended 1×
PyTorch Lightning · recommended 1×

CATEGORY QUERY
How to implement emotion recognition in a conversational AI system using deep learning?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. PyTorch
3. TensorFlow
4. Keras
5. PyTorch Lightning
6. spaCy
7. FastText
8. Google Cloud Natural Language API
9. Azure Cognitive Services Text Analytics
10. Amazon Comprehend
AI recommended 10 alternatives but never named declare-lab/conv-emotion. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a PyTorch library to analyze emotions within dialogue systems.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. Flair (flairNLP/flair)
3. PyTorch-NLP (torchtext) (pytorch/text)
4. TextAttack (TextAttack/TextAttack)
5. DeepPavlov (deepmipt/DeepPavlov)
6. AllenNLP (allenai/allennlp)
AI recommended 6 alternatives but never named declare-lab/conv-emotion. 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/conv-emotion?
pass
AI did not name declare-lab/conv-emotion — 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 declare-lab/conv-emotion in production, what risks or prerequisites should they evaluate first?
pass
AI named declare-lab/conv-emotion 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/conv-emotion solve, and who is the primary audience?
pass
AI did not name declare-lab/conv-emotion — 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

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MARKDOWN (README)

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declare-lab/conv-emotion — 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