RRepoGEO

REPOGEO REPORT · LITE

songyouwei/ABSA-PyTorch

Default branch master · commit 9a7c6c1c · scanned 5/29/2026, 3:13:15 PM

GitHub: 2,110 stars · 523 forks

AI VISIBILITY SCORE
35 /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
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 songyouwei/ABSA-PyTorch, 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 H1 to emphasize its role as a comprehensive ABSA model collection

    Why:

    CURRENT
    # ABSA-PyTorch

> Aspect Based Sentiment Analysis, PyTorch Implementations.
>
> 基于方面的情感分析，使用PyTorch实现.
    COPY-PASTE FIX
    # ABSA-PyTorch: A Comprehensive Collection of Aspect-Based Sentiment Analysis (ABSA) Models in PyTorch

> This repository provides PyTorch implementations for a wide range of Aspect-Based Sentiment Analysis (ABSA) models.
  • mediumhomepage#2
    Add the repository's own URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/songyouwei/ABSA-PyTorch
  • lowtopics#3
    Add more specific topics to highlight PyTorch implementation and model collection

    Why:

    CURRENT
    aspect-based-sentiment-analysis, attention, bert, natural-language-processing, nlp, sentiment-analysis, sentiment-classification
    COPY-PASTE FIX
    aspect-based-sentiment-analysis, attention, bert, natural-language-processing, nlp, sentiment-analysis, sentiment-classification, pytorch-models, deep-learning-nlp

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 songyouwei/ABSA-PyTorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TextAttack
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TextAttack · recommended 2×
  2. Hugging Face Transformers Library · recommended 1×
  3. PyTorch-Transformers-ABSA · recommended 1×
  4. SpaCy · recommended 1×
  5. AllenNLP · recommended 1×
  • CATEGORY QUERY
    How to perform fine-grained sentiment analysis on specific aspects using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. PyTorch-Transformers-ABSA
    3. TextAttack
    4. SpaCy
    5. AllenNLP
    6. PyTorch Lightning

    AI recommended 6 alternatives but never named songyouwei/ABSA-PyTorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a PyTorch library to implement BERT-based aspect sentiment classification.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch-Transformers-Classification (zhoukaifan/PyTorch-Transformers-Classification)
    3. TextAttack
    4. Keras-BERT (CyberZHG/keras-bert)
    5. Simple Transformers

    AI recommended 5 alternatives but never named songyouwei/ABSA-PyTorch. 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 songyouwei/ABSA-PyTorch?
    pass
    AI named songyouwei/ABSA-PyTorch explicitly

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

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

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

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songyouwei/ABSA-PyTorch — 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